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Open AccessEditorial

Preface: The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025)

by Mustapha Hlyal and Abdessamad Ait El Cadi

Eng. Proc. 2025, 97(1), 22; https://doi.org/10.3390/engproc2025097022 - 12 Jun 2025

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Abstract

SMILE 2025 was held in Casablanca, Morocco, on 16–19 April 2025, co-organized by ESITH Morocco, through its Center of Excellence in Logistics (CELOG), and the Université Polytechnique Hauts-de-France, home to the Transport, Circular Economy and Sustainable Logistics Chair (TEC-LOG^d), which provided [...] Read more.

SMILE 2025 was held in Casablanca, Morocco, on 16–19 April 2025, co-organized by ESITH Morocco, through its Center of Excellence in Logistics (CELOG), and the Université Polytechnique Hauts-de-France, home to the Transport, Circular Economy and Sustainable Logistics Chair (TEC-LOG^d), which provided significant scientific coordination and strategic support throughout the event [...] Full article

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1 pages, 148 KB

Open AccessEditorial

Statement of Peer Review

by Mustapha Hlyal and Abdessamad Ait El-Cadi

Eng. Proc. 2025, 97(1), 57; https://doi.org/10.3390/engproc2025097057 - 22 Oct 2025

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Abstract

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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15 pages, 1705 KB

Open AccessProceeding Paper

Hybrid LSTM-DES Models for Enhancing the Prediction Performance of Rail Tourism: A Case Study of Train Passengers in Thailand

by Piyaphong Supanyo, Prakobsiri Pakdeepinit, Pannanat Katesophit, Supawat Meeprom and Anirut Kantasa-ard

Eng. Proc. 2025, 97(1), 1; https://doi.org/10.3390/engproc2025097001 - 4 Jun 2025

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Abstract

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the [...] Read more.

This paper proposes hybrid LSTM-DES models that combine traditional forecasting methods with recurrent neural network techniques. We experimented with these proposed models using four passenger datasets from different regions of Thailand. Additionally, we compared their performance with several individual forecasting models, including the Double Moving Average (DMA), Double Exponential Smoothing (DES), and Holt–Winters methods (both additive and multiplicative trends), as well as long short-term memory (LSTM) recurrent neural networks. Our proposed hybrid model builds upon previous work with improvements in hyperparameter tuning using the GRG nonlinear optimization method. The results demonstrate that the hybrid LSTM-DES models outperformed all individual models in terms of both accuracy and demand variation. The reason behind the success of the hybrid model is that it works well with both linear and nonlinear trends, as well as the seasonality of certain periods. Furthermore, the forecast results for train passengers will serve as input variables to estimate the future revenue of train travel programs in various regions, including rail tourism. This information will help identify which regions should receive increased focus and investment by the train tourism program. For example, if the forecasted number of passengers in the northern region is high, the State Railway of Thailand will promote and improve infrastructure at the train station and nearby tourist attractions. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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7 pages, 979 KB

Open AccessProceeding Paper

Toward a Demand-Driven Supply Chain: BLR Evaluating the Impact of AI and ML Integration in the Healthcare and Pharmaceutical Industry

by Majda Boualam and Imane Ibn El Farouk

Eng. Proc. 2025, 97(1), 2; https://doi.org/10.3390/engproc2025097002 - 5 Jun 2025

Cited by 4 | Viewed by 2410

Abstract

The application of Artificial Intelligence and Machine Learning in the supply chain fields is significantly changing the way businesses manage their operations, forecast their demand, manage their inventory, optimize their logistics, and increase their level of resilience. This research explores, through a bibliometric [...] Read more.

The application of Artificial Intelligence and Machine Learning in the supply chain fields is significantly changing the way businesses manage their operations, forecast their demand, manage their inventory, optimize their logistics, and increase their level of resilience. This research explores, through a bibliometric literature review, how the integration of these technologies can support the implementation of a demand-driven supply chain approach in the global healthcare and pharmaceutical supply chains, which are facing remarkable challenges in ensuring demand-driven operations, especially in light of sudden disruptions such as the COVID-19 pandemic. Full article

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8 pages, 402 KB

Open AccessProceeding Paper

Closing the Loop: Achieving a Sustainable Future for Plastics Through Eco-Design and Recycling

by Youssef Moujoud, Hafida Bouloiz and Maryam Gallab

Eng. Proc. 2025, 97(1), 3; https://doi.org/10.3390/engproc2025097003 - 6 Jun 2025

Cited by 1 | Viewed by 1945

Abstract

The global plastic pollution crisis demands a shift to a circular economy focused on sustainability and resource efficiency. This study examines the current linear approach to plastic use, which depends heavily on fossil fuels and waste materials and often leads to poor waste [...] Read more.

The global plastic pollution crisis demands a shift to a circular economy focused on sustainability and resource efficiency. This study examines the current linear approach to plastic use, which depends heavily on fossil fuels and waste materials and often leads to poor waste management. We highlight the importance of eco-design and Design for Recycling (DfR) as key solutions. These approaches aim to make plastic products easier to recycle, extend their usability, and support a more circular system, while also creating new opportunities in the recycling sector. Achieving this shift requires cooperation between stakeholders, including industry, government, and the public. Supportive policies and the development of new technologies are also crucial. By addressing challenges like complex product designs and limited recycling infrastructure, this study offers practical ways to reduce the environmental and economic impacts of plastic use. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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12 pages, 530 KB

Open AccessProceeding Paper

Corporate Social Responsibility Commitment: Is Regulatory Pressure a Necessary Driver?

by El Aziz Ouissal and Asdiou Abdelkarim

Eng. Proc. 2025, 97(1), 4; https://doi.org/10.3390/engproc2025097004 - 6 Jun 2025

Cited by 1 | Viewed by 1210

Abstract

Corporate social responsibility and its communication are practices that have been developed for several years in developed and emerging countries, which have aroused great interest around the world and particularly in Morocco. Therefore, our main objective is to study the impact of ESG [...] Read more.

Corporate social responsibility and its communication are practices that have been developed for several years in developed and emerging countries, which have aroused great interest around the world and particularly in Morocco. Therefore, our main objective is to study the impact of ESG disclosure imposed by the market regulator on the extent of corporate commitment. This paper aims to understand one of the factors explaining the CSR commitment of Moroccan companies, namely the regulatory environment. Its main strength lies in the in-depth examination of the current regulatory environment and its real impact on corporate social responsibility strategies and actions. To answer our research question, we conducted an empirical study based on a single hypothesis tested through secondary data processing. The results show that engagement, under institutional pressure, has a positive impact on the annual publication of ESG reports on the CSR engagement of Moroccan companies. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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11 pages, 1036 KB

Open AccessProceeding Paper

Federated Learning-Based Framework: A New Paradigm Proposed for Supply Chain Risk Management

by Thanh Tuan Nguyen, Abdelghani Bekrar, Thi Muoi Le, Abdelhakim Artiba, Tarik Chargui, Thi Thu Huong Trinh and Ahmed Snoun

Eng. Proc. 2025, 97(1), 5; https://doi.org/10.3390/engproc2025097005 - 6 Jun 2025

Cited by 2 | Viewed by 2967

Abstract

This paper proposes federated learning-based frameworks for supply chain risk management to address data-sharing constraints. To validate, centralized federated learning with horizontal data was applied for delivery delay prediction using datasets from two textile suppliers: supplier 1 has less data and is considered [...] Read more.

This paper proposes federated learning-based frameworks for supply chain risk management to address data-sharing constraints. To validate, centralized federated learning with horizontal data was applied for delivery delay prediction using datasets from two textile suppliers: supplier 1 has less data and is considered small, while supplier 2, with more data, represents a larger one. The prediction model is developed using an artificial neural network within the federated framework. The results show that federated learning benefits suppliers, especially the ones with limited data. Notably, federated learning outperforms centralized learning and local standalone learning. This highlights its potential to address privacy and facilitate collaboration. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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6 pages, 439 KB

Open AccessProceeding Paper

A Predictive Self-Healing Model for Optimizing Production Lines: Integrating AI and IoT for Autonomous Fault Detection and Correction

by Salah Eddine Ayoub El Ahmadi and Laila El Abbadi

Eng. Proc. 2025, 97(1), 6; https://doi.org/10.3390/engproc2025097006 - 6 Jun 2025

Cited by 1 | Viewed by 2553

Abstract

The increasing complexity of the new generation of production lines necessitates the development of intelligent, autonomous, and adaptable systems that are capable of self-diagnosis and recovery from failures and errors. A “self-healing production line” refers to a production system that integrates artificial intelligence [...] Read more.

The increasing complexity of the new generation of production lines necessitates the development of intelligent, autonomous, and adaptable systems that are capable of self-diagnosis and recovery from failures and errors. A “self-healing production line” refers to a production system that integrates artificial intelligence (AI), the Internet of Things (IoT), and advanced mathematical models to identify anomalies, forecast potential failures that can occur, and implement corrective measures with minimal or no human oversight. This manuscript offers a comprehensive examination of self-healing mechanisms, encompassing IoT-enabled sensors, AI-driven predictive maintenance, and Markov Decision Processes (MDPs) for the optimization of decision-making. Also, it includes an exploration of practical implementation strategies and an automotive case study that illustrates significant enhancements in operational uptime and cost-effectiveness. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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8 pages, 269 KB

Open AccessProceeding Paper

The Use of Artificial Intelligence to Calculate the Estimate of a Public Procurement Act

by Riyad Berraida and EL Abbadi Laila

Eng. Proc. 2025, 97(1), 7; https://doi.org/10.3390/engproc2025097007 - 11 Jun 2025

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Abstract

Public procurement refers to the purchasing of goods and services for public entities. Before launching the call for tender, the public body prepares an estimate of the procurement act; this estimate is taken into consideration by the tender commission before awarding the contract. [...] Read more.

Public procurement refers to the purchasing of goods and services for public entities. Before launching the call for tender, the public body prepares an estimate of the procurement act; this estimate is taken into consideration by the tender commission before awarding the contract. Through technological innovation, buyers can now rely on new solutions as a support to improve the way of calculating the estimate. In this paper, we present research that has been performed in this field, to produce different AI solutions that can be used by buyers to make the estimate more accurate. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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7 pages, 587 KB

Open AccessProceeding Paper

Architectural and Methodological Advancements in Large Language Models

by Zakaria Zaza and Omar Souissi

Eng. Proc. 2025, 97(1), 8; https://doi.org/10.3390/engproc2025097008 - 9 Jun 2025

Cited by 1 | Viewed by 5173

Abstract

The evolution of large language models (LLMs) has been marked by significant architectural and methodological breakthroughs that have redefined the landscape of natural language processing. This review examines the key techniques driving modern LLMs, including foundational architectures, novel training methodologies, and cutting-edge performance [...] Read more.

The evolution of large language models (LLMs) has been marked by significant architectural and methodological breakthroughs that have redefined the landscape of natural language processing. This review examines the key techniques driving modern LLMs, including foundational architectures, novel training methodologies, and cutting-edge performance benchmarks. In addition to offering a performance overview, this work presents a focused and up-to-date architectural benchmark that highlights key design differences between the best-performing open-source and closed-source LLMs, providing actionable insights into their underlying components. Beyond the performance comparison, our analysis details the inherent limitations of the monolithic transformer architecture and outlines emerging strategies. By bridging open-source innovations and proprietary advancements, this review offers a balanced resource for researchers and practitioners navigating this rapidly evolving field. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 406 KB

Open AccessProceeding Paper

Location-Routing Optimization for Pickup Operation in Reverse Logistics Systems

by Mozhgan Jahanafroozi, Abdessamad Ait El Cadi, Abdelghani Bekrar and Abdelhakim Artiba

Eng. Proc. 2025, 97(1), 9; https://doi.org/10.3390/engproc2025097009 - 9 Jun 2025

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Abstract

This paper presents a Location-Routing Problem (LRP) model for optimizing pickup operations in reverse logistics while incorporating drivers’ well-being constraints. The LRP is formulated as a Mixed-Integer Linear Programming (MILP) model, integrating collection center selection and vehicle routing to minimize total costs, including [...] Read more.

This paper presents a Location-Routing Problem (LRP) model for optimizing pickup operations in reverse logistics while incorporating drivers’ well-being constraints. The LRP is formulated as a Mixed-Integer Linear Programming (MILP) model, integrating collection center selection and vehicle routing to minimize total costs, including facility operation, vehicle fixed costs, travel expenses, and driver salary rates. A key contribution of this study is the inclusion of maximum driving time and mandatory break constraints to enhance drivers’ well-being, ensuring compliance with regulations and mitigating fatigue-related risks. We solve the problem using the MILP model in Gurobi and validate it with data from the literature. We test multiple instances to check the model’s performance and solution quality. The results show that the model effectively optimizes collection point allocation and routing while considering cost efficiency and drivers’ well-being. The inclusion of breaks leads to a trade-off between cost minimization and operational sustainability, highlighting the importance of incorporating social factors in logistics planning. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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10 pages, 1153 KB

Open AccessProceeding Paper

Coordination Contracts and Their Impact on Supply Chain Performance: A Systematic Literature Review

by Yassine Tahiri, Zitouni Beidouri and Mohamed El Oumami

Eng. Proc. 2025, 97(1), 10; https://doi.org/10.3390/engproc2025097010 - 9 Jun 2025

Cited by 1 | Viewed by 5105

Abstract

With the increasing complexity of supply chain structures, effective coordination among stakeholders remains essential to maximize performance. This paper presents a systematic literature review of coordination contracts. Fourteen types were explored, ranging from traditional to smart contracts. This study includes a bibliometric analysis [...] Read more.

With the increasing complexity of supply chain structures, effective coordination among stakeholders remains essential to maximize performance. This paper presents a systematic literature review of coordination contracts. Fourteen types were explored, ranging from traditional to smart contracts. This study includes a bibliometric analysis addressing technological, environmental, and risk management challenges. Despite significant progress in the field, most studies focus on dyadic supply chains, failing to cover the multi-echelon complexity. The study concludes by identifying research perspectives, particularly the combined adoption of artificial intelligence and game theory to enhance the analysis and execution of these contracts, thereby fostering resilient logistical systems. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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13 pages, 1524 KB

Open AccessProceeding Paper

Introducing a New Hybrid Project Management Framework for Moroccan Enterprises

by Fatima-Zahra Eddoug, Rajaa Benabbou, Mustapha Ahlaqqach and Jamal Benhra

Eng. Proc. 2025, 97(1), 11; https://doi.org/10.3390/engproc2025097011 - 9 Jun 2025

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Abstract

The needs of modern businesses are constantly evolving, and project management must continuously adapt by proposing new solutions to emerging challenges. Building on previous research—a systematic literature review (SLR) identifying international best practices and a questionnaire survey capturing insights from the Moroccan context—this [...] Read more.

The needs of modern businesses are constantly evolving, and project management must continuously adapt by proposing new solutions to emerging challenges. Building on previous research—a systematic literature review (SLR) identifying international best practices and a questionnaire survey capturing insights from the Moroccan context—this study aims to enhance the practices of Moroccan project management. By integrating global methodologies from the SLR with local business realities from the survey, this research lays the foundation for improving project management practices in Moroccan enterprises. Rather than presenting a finalized framework, this study explores key project management methodologies, with a particular focus on Lean and Agile principles, and evaluates their relevance to the local context. The findings highlight the benefits of combining these approaches to enhance efficiency, adaptability, and overall project performance. This paper thus introduces the initial foundation for the subsequent development of a comprehensive hybrid framework aligned with both global standards and local realities. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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10 pages, 814 KB

Open AccessProceeding Paper

AI-Driven Innovations in Waste Management: Catalyzing the Circular Economy

by Ahmed Snoun, Miratul Khusna Mufida, Abdessamad Ait El-Cadi and Thierry Delot

Eng. Proc. 2025, 97(1), 12; https://doi.org/10.3390/engproc2025097012 - 9 Jun 2025

Cited by 3 | Viewed by 10131

Abstract

An effective CE approach will require new waste management practices that provide more value and have improved resource use and ecological consequences. The innovations offered by artificial intelligence (AI) are revolutionary: automation, predictive analytics, and generative AI enhance sorting and recycling of waste [...] Read more.

An effective CE approach will require new waste management practices that provide more value and have improved resource use and ecological consequences. The innovations offered by artificial intelligence (AI) are revolutionary: automation, predictive analytics, and generative AI enhance sorting and recycling of waste and recovery of materials. This paper analyzes AI applications at the micro, meso, and macro levels, detailing practical examples of improved efficiency AI solutions offer, as well as the sustainability and circularity benefits. By adopting AI within CE frameworks, businesses and policymakers confront existing barriers to change, instigate deep shifts, and catalyze from new waste designable surfaces, designable surface engineering, and sustainable industrial symbiosis opportunities. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 769 KB

Open AccessProceeding Paper

Developing a Virtual Laboratory Framework Based on the Lean Approach in Engineering Education: A Response to Industry 4.0 Skills

by Khadija Talbi, Zineb Ait Haddouchane, Soumia Bakkali and Souad Ajana

Eng. Proc. 2025, 97(1), 13; https://doi.org/10.3390/engproc2025097013 - 6 Jun 2025

Cited by 2 | Viewed by 1558

Abstract

The rapid advancement of digital technologies, referred to as Industry 4.0, has profoundly transformed the manufacturing landscape, necessitating a reevaluation of engineering education. Future engineers must possess diverse skills and competencies to effectively navigate this new era of intelligent, interconnected, and data-driven production [...] Read more.

The rapid advancement of digital technologies, referred to as Industry 4.0, has profoundly transformed the manufacturing landscape, necessitating a reevaluation of engineering education. Future engineers must possess diverse skills and competencies to effectively navigate this new era of intelligent, interconnected, and data-driven production systems. In response to this challenge, this research paper introduces a framework for a virtual laboratory in mechanical and industrial engineering that creates a laboratory in virtual reality (VR) by integrating Lean Manufacturing principles to optimize flow shop processes, thereby preparing engineering students for the demands of Industry 4.0. This approach prepares students to navigate the challenges of modern manufacturing, bridging the gap between theoretical knowledge and its practical application. This paper will discuss the concept of the virtual laboratory for mechanical and industrial engineering education in the Moroccan context based on lean principles. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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12 pages, 1424 KB

Open AccessProceeding Paper

An Aggregation Method for Evaluating the Performance of a Production Line Operating Under the Slowdown Policy

by Zouheir Nahas, Nizar El-Hachemi and Nabil Nahas

Eng. Proc. 2025, 97(1), 14; https://doi.org/10.3390/engproc2025097014 - 10 Jun 2025

Viewed by 998

Abstract

The performance evaluation of production systems is essential for optimizing throughput caused by machine failures and operational constraints. In this study, we introduce an aggregation method designed to evaluate serial production lines (SPLs) that operate under the Slowdown Policy (SP). The SP dynamically [...] Read more.

The performance evaluation of production systems is essential for optimizing throughput caused by machine failures and operational constraints. In this study, we introduce an aggregation method designed to evaluate serial production lines (SPLs) that operate under the Slowdown Policy (SP). The SP dynamically adjusts machine service rates based on buffer levels, reducing blocking and ensuring a stable production rate. We propose an aggregation-based analytical approach that simplifies the evaluation of large-scale production systems while maintaining accuracy. To validate the method, a five-machine, four-buffer production line was analyzed, and the aggregation results were compared with those of a simulation. The results showed that our method provides a fast and good approximation of system performance. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 613 KB

Open AccessProceeding Paper

Resilient Emergency Procurement Strategies and Quantitative Metrics: A Review

by Imane Sassaoui, Jamila El Alami and Mustapha Hlyal

Eng. Proc. 2025, 97(1), 15; https://doi.org/10.3390/engproc2025097015 - 10 Jun 2025

Cited by 2 | Viewed by 2673

Abstract

The growing frequency and complexity of both natural and man- made crises like pandemics have highlighted the need for robust emergency logistics strategies during the last decade. Procurement is one of the critical areas impacting any emergency supply chain. Dealing with disruptions usually [...] Read more.

The growing frequency and complexity of both natural and man- made crises like pandemics have highlighted the need for robust emergency logistics strategies during the last decade. Procurement is one of the critical areas impacting any emergency supply chain. Dealing with disruptions usually involves multi-sourcing, option contracts, and back-up suppliers as resilient strategies. The proposed review classifies emergency procurement strategies into three capacities following the capacity resilience framework: absorptive, adaptive, and restorative. Also, quantitative resilience metrics, modelling methods, and objectives for practical scenarios are discussed. Conclusions are extracted, and future research agendas outlined. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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8 pages, 866 KB

Open AccessProceeding Paper

Internet of Things and Predictive Artificial Intelligence for SmartComposting Process in the Context of Circular Economy

by Soukaina Fouguira, Emna Ammar, Mounia Em Haji and Jamal Benhra

Eng. Proc. 2025, 97(1), 16; https://doi.org/10.3390/engproc2025097016 - 10 Jun 2025

Cited by 4 | Viewed by 3813

Abstract

To promote sustainable development, adopting circular economy principles is crucial for preserving natural resources and ensuring environmental continuity. Among solid waste management strategies, composting plays a significant role by converting biodegradable waste into eco-friendly biofertilizers. Traditional composting methods, which rely on open-window techniques, [...] Read more.

To promote sustainable development, adopting circular economy principles is crucial for preserving natural resources and ensuring environmental continuity. Among solid waste management strategies, composting plays a significant role by converting biodegradable waste into eco-friendly biofertilizers. Traditional composting methods, which rely on open-window techniques, face challenges in controlling critical physico-chemical parameters such as temperature, humidity, and gaseous emissions. Additionally, these methods require significant labor and over 100 days to achieve compost maturity. To address these issues, we propose an intelligent, automated composting system leveraging the Internet of Things (IoT) and wireless sensor networks (WSNs). This system integrates sensors for real-time monitoring of key parameters: DS18b20 for waste temperature, HD-38 for humidity, DHT11 for ambient conditions, and MQ sensors for detecting CO₂, NH₃, and CH₄. Controlled by an ESP32 microcontroller unit (MCU), the system employs a mixer and heating elements to optimize waste degradation based on sensor feedback. Data transmission is managed using the MQTT protocol, allowing real-time monitoring via a cloud-based platform (ThingSpeak). Furthermore, the degradation process was analyzed during the first 24 h, and a recurrent neural network (RNN) algorithm was employed to predict the time required for reaching optimal compost maturity, ensuring an efficient and sustainable solution. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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11 pages, 638 KB

Open AccessProceeding Paper

Social Assessment of Alternative Urban Buses

by Faissal Jelti and Naoufel Cheikhrouhou

Eng. Proc. 2025, 97(1), 17; https://doi.org/10.3390/engproc2025097017 - 10 Jun 2025

Cited by 1 | Viewed by 727

Abstract

Public transportation in cities is negatively affected by reliance on petroleum-based fuels, leading to emissions and poor air quality. Although the environmental evaluation of alternative buses in terms of sustainability has been extensively studied, the social dimensions have not received as much attention. [...] Read more.

Public transportation in cities is negatively affected by reliance on petroleum-based fuels, leading to emissions and poor air quality. Although the environmental evaluation of alternative buses in terms of sustainability has been extensively studied, the social dimensions have not received as much attention. In this regard, this research examines the social implications of alternative urban buses through life cycle impact assessment (LCIA) methods, including Eco-Indicator 99, Impact 2002+, and ReCiPe Endpoint. The results indicate that diesel buses significantly impact health, while hybrid, fuel cell, and electric buses can decrease emissions by 50%. These results underscore the necessity of zero-emission technologies to enhance urban air quality and promote better public health. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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10 pages, 202 KB

Open AccessProceeding Paper

A Survey on Applications of Distributed Ledger Technology in International Trade

by Fayssal Moukafi and Amine Dafir

Eng. Proc. 2025, 97(1), 18; https://doi.org/10.3390/engproc2025097018 - 11 Jun 2025

Cited by 1 | Viewed by 1727

Abstract

With its potential to address persistent issues like inefficiency, fraud, and a lack of transparency, distributed ledger technology (DLT), and in particular blockchain, has become a game-changing breakthrough in the realm of international trade. With a thorough examination of its potential to revolutionize [...] Read more.

With its potential to address persistent issues like inefficiency, fraud, and a lack of transparency, distributed ledger technology (DLT), and in particular blockchain, has become a game-changing breakthrough in the realm of international trade. With a thorough examination of its potential to revolutionize trade processes, this study examines the applications of DLT in global commerce. It starts by examining the conventional cloud-based models that predominate in global trade procedures and contrasting them with the blockchain-based approach that has been suggested. The viability and effect of blockchain technology (BCT) in this industry are evaluated by the research using both qualitative and quantitative approaches, such as data collecting, comparative analysis, and SWOT analysis. The main impediments to blockchain adoption are noted, along with suggested fixes for them. A discussion of potential future possibilities and suggestions for using blockchain technology into global trade networks round out the report. The purpose of this study is to offer theoretical understandings and useful suggestions for the successful use of blockchain technology in international trade. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

10 pages, 653 KB

Open AccessProceeding Paper

Towards a Smart Evaluation Model for Assessing Transport Providers’ Maturity in Support of Logistic Sustainability

by Hicham El Abdellaoui and Adil Bellabdaoui

Eng. Proc. 2025, 97(1), 19; https://doi.org/10.3390/engproc2025097019 - 11 Jun 2025

Viewed by 1136

Abstract

The development of the supply chain’s outsourcing and globalization has intensified the requirement for sustainable transport. The evaluation of the transport providers’ maturity, which is crucial to all and any achievements in this context, is hampered by a myriad of factors ranging from [...] Read more.

The development of the supply chain’s outsourcing and globalization has intensified the requirement for sustainable transport. The evaluation of the transport providers’ maturity, which is crucial to all and any achievements in this context, is hampered by a myriad of factors ranging from the transport ecosystem complexity, self-contradictory and unverifiable data and the ceaseless march of modern technology. This study argues for an agile and smart approach to evaluate the maturity level of transport providers, particularly for high-risk areas like hazardous materials transport. Such models should include holistic analysis frameworks of all performance indicator measurement systems with their data collection methods and technology tools to be employed. The need to involve all stakeholders within the supply chain is said to require diverse collaboration. With regard to the solution, collaborative participation between transport providers and relevant institutions is vital to reduce the environmental impacts and improve the efficiency of the entire sector. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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8 pages, 727 KB

Open AccessProceeding Paper

Strategic Analysis of IoT Integration in 3PL Competition: A Simulation-Based Study

by Kenza Izikki, Hlyal Mustapha and Jamila El Alami

Eng. Proc. 2025, 97(1), 20; https://doi.org/10.3390/engproc2025097020 - 11 Jun 2025

Viewed by 721

Abstract

Digital transformation is crucial for businesses to thrive in today’s rapidly evolving marketplace. It is a strategic choice that enables organizations to improve customer service, strengthen supplier relationships, and boost sales and business growth, ultimately enhancing their competitive stance. The Internet of Things [...] Read more.

Digital transformation is crucial for businesses to thrive in today’s rapidly evolving marketplace. It is a strategic choice that enables organizations to improve customer service, strengthen supplier relationships, and boost sales and business growth, ultimately enhancing their competitive stance. The Internet of Things (IoT) has become a transformative force across various domains, leveraging interconnected devices and sensors to gather and analyse data, thus enhancing decision making, efficiency, and innovation. This paper analyses the strategic competition between two 3PL firms integrating IoT technologies. Based on a game-theoretic model, the study uses Monte Carlo simulation and K-means clustering to identify distinct strategic groups and optimal adoption ranges. The findings highlight risks of over- or under-investments as well as asymmetric outcomes. Also, a set of recommendations and managerial insights are provided for better decision making in a tech-competitive setting. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 1174 KB

Open AccessProceeding Paper

A Fuzzy Programming Approach for a Multi-Objective Design of a Sustainable Closed-Loop Supply Chain Network in the Case of End-of-Life Medical Textiles

by Mustapha Ahlaqqach, Achraf Touil, Jamal Benhra, Mariam Atwani, Moulay Ali Oualidi and Jamal Lmariouh

Eng. Proc. 2025, 97(1), 21; https://doi.org/10.3390/engproc2025097021 - 12 Jun 2025

Viewed by 882

Abstract

The reverse logistics of medical textiles has become a major concern in Morocco today, compelling authorities and professionals to develop a sustainable reverse logistics model. This study proposes a model for designing a sustainable closed-loop supply chain network in a fuzzy environment, using [...] Read more.

The reverse logistics of medical textiles has become a major concern in Morocco today, compelling authorities and professionals to develop a sustainable reverse logistics model. This study proposes a model for designing a sustainable closed-loop supply chain network in a fuzzy environment, using the medical textile life cycle as a case study. The model aims to generate economic gains, increase corporate social responsibility through job creation, and mitigate risks associated with the transportation of end-of-life products. In addition, the uncertainty of the model parameters is considered. The multi-objective model, formulated as a mixed-integer linear program, was solved using an exact approach, enabling strategic and tactical decision-making. Furthermore, the results demonstrate that accounting uncertainty can significantly impact strategic and tactical decisions in network design. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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11 pages, 507 KB

Open AccessProceeding Paper

Supply Chain Management, Steering and Decision-Making Through the S&OP Process in the Era of Digitalization and Artificial Intelligence: A Literature Review

by Rachid Ouamalich and Nizar El Hachemi

Eng. Proc. 2025, 97(1), 23; https://doi.org/10.3390/engproc2025097023 - 12 Jun 2025

Cited by 1 | Viewed by 4139

Abstract

Crises and disruptive events over new technologies like artificial intelligence, have transformed supply chains management. This literature review proposes a conceptual framework to identify the relevance of digitalization and artificial intelligence for decision-making through S&OP. Selected articles use mathematical models to resolve conflicts [...] Read more.

Crises and disruptive events over new technologies like artificial intelligence, have transformed supply chains management. This literature review proposes a conceptual framework to identify the relevance of digitalization and artificial intelligence for decision-making through S&OP. Selected articles use mathematical models to resolve conflicts between objectives related to value parameters (revenue, cost, cash, asset and sustainability) and optimize planning. Half of the articles integrate computational intelligence, but do not directly address the use of AI in S&OP. One promising stream is the S&OP process as study object where artificial and generative intelligence will play a key role in collective and collaborative intelligence. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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25 pages, 1304 KB

Open AccessProceeding Paper

A Reinforcement Learning-Based Proximal Policy Optimization Approach to Solve the Economic Dispatch Problem

by Adil Rizki, Achraf Touil, Abdelwahed Echchatbi, Rachid Oucheikh and Mustapha Ahlaqqach

Eng. Proc. 2025, 97(1), 24; https://doi.org/10.3390/engproc2025097024 - 12 Jun 2025

Cited by 3 | Viewed by 5922

Abstract

This paper presents a novel approach to economic dispatch (ED) optimization in power systems through the application of Proximal Policy Optimization (PPO), an advanced reinforcement learning algorithm. The economic dispatch problem, a fundamental challenge in power system operations, involves optimizing the generation output [...] Read more.

This paper presents a novel approach to economic dispatch (ED) optimization in power systems through the application of Proximal Policy Optimization (PPO), an advanced reinforcement learning algorithm. The economic dispatch problem, a fundamental challenge in power system operations, involves optimizing the generation output of multiple units to minimize operational costs while satisfying load demands and technical constraints. Traditional methods often struggle with the non-linear, non-convex nature of modern ED problems, especially with increasing penetration of renewable energy sources. Our PPO-based methodology transforms the ED problem into a reinforcement learning framework where an agent learns optimal generator scheduling policies through continuous interaction with a simulated power system environment. The proposed approach is validated on a 15-generator test system with varying load demands and operational constraints. Experimental results demonstrate that the PPO algorithm achieves superior performance compared to conventional techniques, with cost reductions of up to 7.3% and enhanced convergence stability. The algorithm successfully handles complex constraints including generator limits, ramp rates, and spinning reserve requirements, while maintaining power balance with negligible error margins. Furthermore, the computational efficiency of the PPO approach allows for real-time adjustments to rapidly changing system conditions, making it particularly suitable for modern power grids with high renewable energy penetration. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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8 pages, 1048 KB

Open AccessProceeding Paper

The Role of Artificial Intelligence in Supply Chain Finance in the Context of Industry 5.0: A Systematic Literature Review

by Taoufiq Hamdaoui and Noura Aknin

Eng. Proc. 2025, 97(1), 25; https://doi.org/10.3390/engproc2025097025 - 13 Jun 2025

Cited by 2 | Viewed by 2254

Abstract

This study conducts a systematic literature review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process. The Scopus database is our main source of data, and our data analyses comprised bibliometric, systematic, and advanced analyses conducted with the help [...] Read more.

This study conducts a systematic literature review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process. The Scopus database is our main source of data, and our data analyses comprised bibliometric, systematic, and advanced analyses conducted with the help of VOSviewer. The analysis pinpoints the various roles played by AI in SCF: the strengthening of supply chain resilience and management, especially in times of crises like the COVID-19 pandemic; the convergence of sustainability performances via better relationships with suppliers; the effective management of risks by anticipating distress and fraud; improvements in working capital and supply chain efficiency; and tracking supply chain activities while on the go with IoT (Internet of Things). However, the following major setbacks are also observed: implementation challenges, large initial investments, resistance to change, a dearth of expertise in AI, security and privacy concerns, challenges in the integration of systems, the reliability of the data’s quality, etc. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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14 pages, 1499 KB

Open AccessProceeding Paper

A Parallel Processing Architecture for Long-Term Power Load Forecasting

by Adil Rizki, Achraf Touil, Abdelwahed Echchatbi and Mustapha Ahlaqqach

Eng. Proc. 2025, 97(1), 26; https://doi.org/10.3390/engproc2025097026 - 16 Jun 2025

Viewed by 1121

Abstract

The increasing complexity of power grids and integration of renewable energy sources necessitate accurate power load forecasting across multiple time horizons. While existing methods have advanced significantly, they often struggle with consistent performance across different prediction ranges, leading to suboptimal resource allocation. We [...] Read more.

The increasing complexity of power grids and integration of renewable energy sources necessitate accurate power load forecasting across multiple time horizons. While existing methods have advanced significantly, they often struggle with consistent performance across different prediction ranges, leading to suboptimal resource allocation. We propose MP-RWKV (Multi-Path Recurrent Weighted Key–Value), an enhanced architecture that builds upon RWKV-TS and addresses these challenges through parallel processing paths for temporal modeling. Our model maintains robust performance across both short-term and long-term forecasting scenarios through its context state mechanism and position-aware attention. Evaluated on extensive power load data, MP-RWKV demonstrates superior performance over state-of-the-art baselines, including Transformer-based models and LSTM variants. The model achieves the lowest Mean Absolute Error (MAE) across prediction horizons ranging from 24 h to 432 h, showing particular strength in maintaining consistent accuracy where traditional models deteriorate. Notably, MP-RWKV successfully balances immediate temporal correlations with extended dependencies, offering promising implications for power grid management and sustainable energy systems. The model’s stable performance across varying prediction horizons makes it particularly suitable for real-world power load forecasting applications. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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19 pages, 537 KB

Open AccessProceeding Paper

Data Envelopment Analysis of Rural Water Access Efficiency: A Multi-Dimensional Assessment Framework for Resource Optimization

by Youness Boudrik, Achraf Touil, Abdellah Oulakhmis and Rachid Hasnaoui

Eng. Proc. 2025, 97(1), 27; https://doi.org/10.3390/engproc2025097027 - 13 Jun 2025

Viewed by 1203

Abstract

This paper introduces a comprehensive framework for evaluating rural water access efficiency using Data Envelopment Analysis (DEA). Despite significant investment in water infrastructure, disparities in access efficiency across regions remain a critical challenge for developing countries. We apply an input-oriented Banker-Charnes-Cooper (BCC) DEA [...] Read more.

This paper introduces a comprehensive framework for evaluating rural water access efficiency using Data Envelopment Analysis (DEA). Despite significant investment in water infrastructure, disparities in access efficiency across regions remain a critical challenge for developing countries. We apply an input-oriented Banker-Charnes-Cooper (BCC) DEA model with bootstrap bias correction to assess the relative efficiency of 16 regions in rural Morocco. Our approach incorporates multiple inputs (infrastructure investment, operational costs) and outputs (access coverage, water quality) to evaluate each region’s efficiency in converting resources into water access outcomes. The results reveal substantial efficiency variations (mean bias-corrected efficiency: 0.906, SD: 0.071) with seven regions identified as globally efficient under variable returns to scale. We introduce a novel Water Access-Efficiency Matrix that enables targeted policy interventions across four strategic quadrants. The analysis demonstrates that inefficient regions have an average input reduction potential of 16.4%, with six regions requiring improvements exceeding 10%. Furthermore, the identification of returns to scale characteristics (5 IRS, 6 CRS, 5 DRS) provides crucial guidance for scaling strategies. This framework offers policymakers a robust, multi-dimensional decision support tool for optimizing resource allocation, benchmarking performance, and developing tailored strategies that address both technical efficiency and access equity in water infrastructure development. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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12 pages, 396 KB

Open AccessProceeding Paper

Multi-Objective MILP Models for Optimizing Makespan and Energy Consumption in Additive Manufacturing Systems

by Safae Saaad, Achraf Touil and Rachid Oucheikh

Eng. Proc. 2025, 97(1), 28; https://doi.org/10.3390/engproc2025097028 - 11 Jun 2025

Cited by 1 | Viewed by 1396

Abstract

Additive manufacturing (AM) is revolutionizing industrial production by enabling the fabrication of complex, customized components with reduced material waste. However, the scheduling of AM machines presents significant challenges in terms of optimizing both time-related performance and energy consumption. This paper introduces a novel [...] Read more.

Additive manufacturing (AM) is revolutionizing industrial production by enabling the fabrication of complex, customized components with reduced material waste. However, the scheduling of AM machines presents significant challenges in terms of optimizing both time-related performance and energy consumption. This paper introduces a novel multi-objective mixed-integer linear programming (MILP) model for scheduling AM machines with the dual objectives of minimizing makespan and energy consumption. We address the single-machine environment with detailed mathematical formulation that accounts for machine-specific parameters such as power consumption rates during different operational states, including printing, setup, and idle modes. Additionally, we consider part-specific characteristics including height, area requirements, and volume, ensuring practical feasibility constraints are met. The proposed model is validated using a comprehensive set of test problems, with optimal solutions reported for small to medium-sized instances. For larger problem instances, where computational complexity prevents finding optimal solutions within reasonable time limits, we report the best solutions obtained under specified time constraints. Computational experiments demonstrate that our approach effectively balances the trade-off between makespan and energy consumption, providing valuable insights for production planning in AM facilities. The results indicate potential energy savings of up to 18% compared to makespan-only optimization approaches, with minimal impact on overall completion times. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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10 pages, 1246 KB

Open AccessProceeding Paper

Bi-Objective Optimization for Sustainable Logistics in the Closed-Loop Inventory Routing Problem

by Chaima Zormati, Tarik Chargui, Abdelghani Bekrar and Abdessamad Ait-El-Cadi

Eng. Proc. 2025, 97(1), 29; https://doi.org/10.3390/engproc2025097029 - 16 Jun 2025

Cited by 1 | Viewed by 1143

Abstract

This study proposes a bi-objective optimization model for the inventory routing problem with pickup and delivery (IRP–PD) in a closed-loop supply chain, addressing the growing demand for sustainable logistics solutions. The model simultaneously minimizes transportation costs and inventory costs and enhances driver well-being [...] Read more.

This study proposes a bi-objective optimization model for the inventory routing problem with pickup and delivery (IRP–PD) in a closed-loop supply chain, addressing the growing demand for sustainable logistics solutions. The model simultaneously minimizes transportation costs and inventory costs and enhances driver well-being by incorporating regular rest breaks. The network operates within a circular economy framework, where pallets are both delivered and returned for reuse, contributing to waste reduction. A normalized weighted-sum method is initially used to balance the conflicting objectives. However, since the model cannot efficiently solve large-scale instances, we adopt the NSGA-II metaheuristic to generate a Pareto front, enabling decision-makers to explore trade-offs between objectives. The model is tested on a single instance, and the results demonstrate a promising compromise between economic and social goals. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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11 pages, 363 KB

Open AccessProceeding Paper

Decentralized Supply Chains Using Fused Disposition Modeling as a Framework: Optimization Using a Machine Learning Approach

by Yassine Abika, Abdelkabir Bacha, Mustapha Ahlaqqach and Jamal Benhra

Eng. Proc. 2025, 97(1), 30; https://doi.org/10.3390/engproc2025097030 - 16 Jun 2025

Viewed by 1351

Abstract

The globally used additive manufacturing technique called Fused Deposition Modeling plays a central role in advancing dematerialized logistics by enabling on-demand production and minimizing material waste. The integration of Artificial Intelligence (AI) into FDM processes has introduced promising avenues to improve efficiency, accuracy, [...] Read more.

The globally used additive manufacturing technique called Fused Deposition Modeling plays a central role in advancing dematerialized logistics by enabling on-demand production and minimizing material waste. The integration of Artificial Intelligence (AI) into FDM processes has introduced promising avenues to improve efficiency, accuracy, and sustainability. Expressly, researchers have proved in what ways machine learning algorithms can upgrade printing parameters, initiating enhanced product quality and lower defects. In the context of dematerialized logistics, the PRISMA methodology mentioned in this review is set to maintain a structured analysis of the junction between AI and FDM. Exhaustive research of analyzed studies issued from 2009 to 2024 through databases like Scopus, Web of Science, and IEEE Xplore demonstrate an expanding reliance on AI techniques like neural networks and genetic algorithms. All these mentioned methods are used to approach challenges such as print quality inconsistencies, material overuse, and structural weaknesses. The outcome shows the prospect of AI to reshape FDM, but major obstacles remain present: many problems, such as the scalability of models and their integration into existing logistical frameworks, need further studies and research. As demonstrated, this review gives an inclusive perspective on the actual progress and highlights the main directions for what lies ahead to improve FDM processes in logistics. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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11 pages, 5211 KB

Open AccessProceeding Paper

Leveraging Data Science for Impactful Logistics Automatons in the E-Commerce Sector

by Nabila Belhaj, Jérémy Patrix, Ouail Oulmakki and Jérôme Verny

Eng. Proc. 2025, 97(1), 31; https://doi.org/10.3390/engproc2025097031 - 16 Jun 2025

Viewed by 1177

Abstract

Automation technologies play a pivotal role in optimizing logistics operations within warehouse facilities. Retail companies invest in these technologies to maintain pace with the customers demands by increasing their production capacity while reducing their financial expenses. In this paper, we conduct a study [...] Read more.

Automation technologies play a pivotal role in optimizing logistics operations within warehouse facilities. Retail companies invest in these technologies to maintain pace with the customers demands by increasing their production capacity while reducing their financial expenses. In this paper, we conduct a study on warehouse automation in the European e-commerce sector by analyzing historical data from three fulfillment centers. Accordingly, we explore diverse data science approaches applied to trained machine learning models to determine the automatons that have the greatest impact on financial costs. The purpose is to support supply chain managers in identifying the most profitable logistics automatons that merit consideration in future automation projects. The study offers a comprehensive analysis that encourages e-commerce companies to invest in tailored automation for future warehouse installations. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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26 pages, 4690 KB

Open AccessProceeding Paper

Wage Rates and Job Requirements Prediction: An Application to Logistics Online Job Postings Using Search Tools and Web Scraping

by Khoa Huu Dang Tran, Huong Quynh Nguyen, Hang My Hanh Le, Lina Doan Tran and Nhi To Yen Tran

Eng. Proc. 2025, 97(1), 32; https://doi.org/10.3390/engproc2025097032 - 17 Jun 2025

Viewed by 2463

Abstract

This paper predicts offered wage rates and job requirements in the logistics industry by utilizing data from online job postings collected through two methods: search tools and web scraping. We apply conventional estimation techniques, such as ordinary least squares and kernel density estimation, [...] Read more.

This paper predicts offered wage rates and job requirements in the logistics industry by utilizing data from online job postings collected through two methods: search tools and web scraping. We apply conventional estimation techniques, such as ordinary least squares and kernel density estimation, to analyze the collected data. Additionally, for the first time, we employ nowcasting methods (linear regression, decision tree, and K-nearest neighbor methods) in this context to generate robust results. Our main findings are as follows: First, the average real wage derived from online job postings aligns with officially published GDP per capita data for the studied countries and regions. Second, we identify significantly positive causal effects of work experience on real wages in the logistics industry. Third, skill requirements exhibit year-over-year variations. Finally, the decision tree method generates the closest nowcasted results in line with the actual web scraped data. The proposed methodologies and their findings establish a reliable approach using search tools and web scraping to define and predict labor demand for stakeholders in this sector as well as others. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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18 pages, 2437 KB

Open AccessProceeding Paper

Sustainable Logistics Strategy Deployment: A BSC-Based Developed QFD

by Eszter Sós and Peter Földesi

Eng. Proc. 2025, 97(1), 33; https://doi.org/10.3390/engproc2025097033 - 18 Jun 2025

Viewed by 1101

Abstract

This paper tackles the logistics dilemma of how to meet customer expectations while at the same time respecting the internal processes and financial interests of the company and ensuring long-term sustainability. In this paper, integrated Quality Function Deployment (QFD) and Balanced Scorecard (BSC) [...] Read more.

This paper tackles the logistics dilemma of how to meet customer expectations while at the same time respecting the internal processes and financial interests of the company and ensuring long-term sustainability. In this paper, integrated Quality Function Deployment (QFD) and Balanced Scorecard (BSC) techniques developed a method for the structured planning of logistics strategies. BSC, combined with QFD, gives the opportunity not only to “translate” the voice of the customer but also to focus on the company’s interests from four perspectives. For example, for products, we evaluated the interactions between different expectations, and the focus was on the disputes that arise during the expectations. The result of this paper is that Extended QFD provides a new method to formulate the various requirements. This method is suitable for creating a sustainable logistics strategy. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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13 pages, 221 KB

Open AccessProceeding Paper

Circular Economy Practices for Data Management

by Miratul Khusna Mufida, Ahmed Snoun, Joseph Sarkis, Abessamad Ait El-Cadi, Thierry Delot and Martin Trépanier

Eng. Proc. 2025, 97(1), 34; https://doi.org/10.3390/engproc2025097034 - 18 Jun 2025

Cited by 3 | Viewed by 2366

Abstract

Data waste presents a growing challenge in the digital era, characterized by redundant, outdated, and underutilized data that contributes to inefficiencies, increased costs, and environmental concerns. This paper explores how circular economy (CE) principles—reduce, reuse, recycle, repair, and rethink—can be adapted to optimize [...] Read more.

Data waste presents a growing challenge in the digital era, characterized by redundant, outdated, and underutilized data that contributes to inefficiencies, increased costs, and environmental concerns. This paper explores how circular economy (CE) principles—reduce, reuse, recycle, repair, and rethink—can be adapted to optimize data usage, minimize waste, and ensure responsible data handling throughout its lifecycle. Applying CE concepts to data management can enhance sustainability, improve operational efficiency, and support responsible digital transformation. This study examines key strategies and challenges in implementing circular data management, emphasizing data reuse, lifecycle management, and policy frameworks. Furthermore, real-world examples and case studies demonstrate the impact of CE principles in reducing data waste and improving efficiency. Notably, AI-driven data minimization strategies have led to 30% reductions in storage costs, while centralized data-sharing initiatives have improved operational efficiency by 20%. These findings underscore the necessity of structured data governance in the digital economy. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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12 pages, 649 KB

Open AccessProceeding Paper

The Employability of Engineers in the Era of Industry 4.0

by Bahia Ismaili, Soumia Bakkali and Salwa Benriyene

Eng. Proc. 2025, 97(1), 35; https://doi.org/10.3390/engproc2025097035 - 19 Jun 2025

Viewed by 2666

Abstract

This study explores the employability of engineers in the context of Industry 4.0, focusing on the KASH model (Knowledge, Attitude, Skills, Habits). Using a sequential exploratory design, we collected data from 800 engineers in Morocco through an online questionnaire. The findings reveal that [...] Read more.

This study explores the employability of engineers in the context of Industry 4.0, focusing on the KASH model (Knowledge, Attitude, Skills, Habits). Using a sequential exploratory design, we collected data from 800 engineers in Morocco through an online questionnaire. The findings reveal that attitude and knowledge have a significant impact on employability, whereas skills and habits show moderate to weak effects. The study employs Partial L6east Squares (PLS) Structural Equation Modeling to validate the model, demonstrating a strong predictive relevance (Q² = 0.714) and a high coefficient of determination (R² = 0.716). The results suggest that engineering schools should emphasize attitude and knowledge development to enhance employability in the rapidly evolving job market of Industry 4.0. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 904 KB

Open AccessProceeding Paper

Geopolitical Risk, Economic Uncertainty, and Market Volatility Index Impact on Energy Price

by Minh Tam Le, Hang My Hanh Le, Huong Quynh Nguyen and Le Ngoc Nhu Pham

Eng. Proc. 2025, 97(1), 36; https://doi.org/10.3390/engproc2025097036 - 19 Jun 2025

Cited by 3 | Viewed by 4300

Abstract

Using the OLS model with different quantiles of GPR, we aim to examine the impact of GPR, EPU, and VIX on monthly international crude oil prices, including WTI, BRENT, and DUBAI prices, while differentiating the impact on different levels of risks. Afterwards, we [...] Read more.

Using the OLS model with different quantiles of GPR, we aim to examine the impact of GPR, EPU, and VIX on monthly international crude oil prices, including WTI, BRENT, and DUBAI prices, while differentiating the impact on different levels of risks. Afterwards, we use the GARCH and MGARCH models to assess the impact of these metrics on the volatility of oil prices, and the spillover effects between oil prices with these three metrics as exogenous shocks. Our result indicates (i) global oil price is negatively affected by GPRT at a moderate level of risks in longer time intervals; (ii) GPR, EPU, and VIX affect oil price’s volatility, and (iii) there exists a stronger long-persistent spillover effect between BRENT and DUBAI, with these metrics as exogenous shocks, while WTI is not affected. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 372 KB

Open AccessProceeding Paper

Optimization of Delivery Allocation for Enhanced Fleet Utilization and Trip Minimization: A Case Study from an Indonesian Manufacturing Company

by Meilita Tryana Sembiring, Novika Zuya, Muhammad Riezky Anindhitya Laksmana and M. Zaky Hadi

Eng. Proc. 2025, 97(1), 37; https://doi.org/10.3390/engproc2025097037 - 20 Jun 2025

Cited by 1 | Viewed by 1493

Abstract

Logistics efficiency is critical to operational success in manufacturing, especially for corrugated carton manufacturers. The challenges of this type of manufacturing include optimizing truck utilization, without which high costs, resource waste, and customer dissatisfaction can occur. Transportation consolidation can reduce trips, increase vehicle [...] Read more.

Logistics efficiency is critical to operational success in manufacturing, especially for corrugated carton manufacturers. The challenges of this type of manufacturing include optimizing truck utilization, without which high costs, resource waste, and customer dissatisfaction can occur. Transportation consolidation can reduce trips, increase vehicle capacity, and lower carbon emissions. This study proposes a delivery optimization model using genetic algorithms within the Multi-Objective Evolutionary Algorithm (MOEA) framework. The results show that the model significantly improves fleet utilization from 75% to 100% and reduces delivery delays by adhering to predefined time windows, thereby improving cost efficiency and customer satisfaction. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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17 pages, 901 KB

Open AccessProceeding Paper

Enhanced Water Access Segmentation Using an Improved Salp Swarm Algorithm for Regional Development Planning

by Youness Boudrik, Achraf Touil, Rachid Hasnaoui, Mustapha Ahlaqqach and Mhammed El Bakkali

Eng. Proc. 2025, 97(1), 38; https://doi.org/10.3390/engproc2025097038 - 20 Jun 2025

Viewed by 517

Abstract

This paper presents a novel approach to water access segmentation by introducing an improved version of the Salp Swarm Algorithm (ISSA), addressing the complex challenge of household water access classification in developing regions. The proposed enhancement incorporates dynamic exploration–exploitation balancing and feature-aware mechanisms [...] Read more.

This paper presents a novel approach to water access segmentation by introducing an improved version of the Salp Swarm Algorithm (ISSA), addressing the complex challenge of household water access classification in developing regions. The proposed enhancement incorporates dynamic exploration–exploitation balancing and feature-aware mechanisms into the original SSA framework, significantly improving cluster quality and interpretability. Using a real-world dataset of 500 households from the El Hajeb region in Morocco and 12 socio-economic criteria, our method demonstrates superior clustering performance compared to conventional techniques. The ISSA achieves a 25% improvement in the silhouette coefficient (0.732 vs. 0.480) and a 22% reduction in the Davies–Bouldin index (0.421 vs. 0.645) compared to the standard SSA and other state-of-the-art metaheuristic algorithms. Five distinct water access segments are identified, enabling targeted infrastructure development strategies across different community types. The approach provides regional planners with essential insights into the spatial distribution of water access patterns and their relationship with socio-economic factors. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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10 pages, 731 KB

Open AccessProceeding Paper

How Blockchains Improve Lean Manufacturing Tools in the Industry 4.0 Context

by Rouidi Ibrahim, Serrou Driss and Lagrat Ismail

Eng. Proc. 2025, 97(1), 39; https://doi.org/10.3390/engproc2025097039 - 23 Jun 2025

Viewed by 1780

Abstract

Lean manufacturing (LM) is one of the strongest tools used by manufacturing companies to improve and optimize their processes, making them more performant and agile. The current industrial revolution, or Industry 4.0, aims to give new momentum to manufacturing systems through various technologies, [...] Read more.

Lean manufacturing (LM) is one of the strongest tools used by manufacturing companies to improve and optimize their processes, making them more performant and agile. The current industrial revolution, or Industry 4.0, aims to give new momentum to manufacturing systems through various technologies, of which Blockchain is one of them. This technology has gained significant attention for its ability to enhance transparency, traceability, and data security within manufacturing and supply chain operations, representing a valuable opportunity to enhance lean manufacturing tools. Firstly, this paper presents what lean manufacturing is. After that, it explores how Industry 4.0 technologies influence LM. Then, it examines the impact of blockchain on LM, paving the way for lean 4.0 by presenting a case study concerning the Kanaban method in the automotive sector. Finally, the summary and future research direction will be presented. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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12 pages, 2435 KB

Open AccessProceeding Paper

Predicting Color Change of Cotton Fabric After Biopolishing Treatment Using Fuzzy Logic Modeling

by Elkhaoudi Mostafa, Elbakkali Mhammed, Messnaoui Redouan, Omar Cherkaoui and Aziz Soulhi

Eng. Proc. 2025, 97(1), 40; https://doi.org/10.3390/engproc2025097040 - 23 Jun 2025

Viewed by 1061

Abstract

A fuzzy prediction model has been developed considering the concentration of acetic acid (pH), temperature, and biopolishing time as input variables, while the color change, measured with DEcmc, between samples before and after biopolishing, was used as the output variable. The parameters influencing [...] Read more.

A fuzzy prediction model has been developed considering the concentration of acetic acid (pH), temperature, and biopolishing time as input variables, while the color change, measured with DEcmc, between samples before and after biopolishing, was used as the output variable. The parameters influencing the color change in knitted cotton fabrics exhibit significant non-linearity. The fuzzy inference system proves to be an effective modeling tool, capable of representing non-linear relationships with a limited amount of experimental data. For the variables, triangular and trapezoidal membership functions were adopted, and a total of 27 rules were established in this research. It was observed that the impact of cellulase concentration on color change is relatively low, but it is strongly influenced by temperature, even at a constant concentration of cellulase. The model developed in this study was validated with an additional experimental data set. The developed system is capable of predicting shade changes with an accuracy of over 90%, which helps to reduce rework and reprocessing in the wet processing sectors. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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8 pages, 673 KB

Open AccessProceeding Paper

A Hybrid Genetic Algorithm for Multi-Objective Multi-Manned Assembly Line Worker Allocation and Balancing Problem

by Sana El Machouti, Mustapha Hlyal and Jamila El Alami

Eng. Proc. 2025, 97(1), 41; https://doi.org/10.3390/engproc2025097041 - 25 Jun 2025

Cited by 3 | Viewed by 1197

Abstract

Industrial progress has increased the need to improve the productivity of production systems. In this context, optimizing the problem of allocating and balancing workers on a multi-manned assembly line (MALW-a-BP) is a major challenge. This paper introduces a mathematical model for this issue, [...] Read more.

Industrial progress has increased the need to improve the productivity of production systems. In this context, optimizing the problem of allocating and balancing workers on a multi-manned assembly line (MALW-a-BP) is a major challenge. This paper introduces a mathematical model for this issue, as well as a hybrid genetic algorithm (h-GA) dedicated to its solution, with a particular focus on multi-objective optimization, which includes minimizing the cycle time and the total squared workload. Experimental results show that the proposed method outperforms the classical version of the GA, thus confirming the robustness and efficiency of the h-GA. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 209 KB

Open AccessProceeding Paper

The Integration of Ethical and Trustworthy AI in Industrial Engineering: Practical Approaches

by Silvia Di Salvatore, Oumayma Drissi Yahyaoui, Matteo De Marchi and Erwin Rauch

Eng. Proc. 2025, 97(1), 42; https://doi.org/10.3390/engproc2025097042 - 24 Jun 2025

Viewed by 2509

Abstract

The fast growth of artificial intelligence during recent years has resulted in its implementation across various sectors. The broad implementation of AI systems has generated substantial ethical issues because AI algorithm decisions can affect basic rights such as privacy, fairness, security and individual [...] Read more.

The fast growth of artificial intelligence during recent years has resulted in its implementation across various sectors. The broad implementation of AI systems has generated substantial ethical issues because AI algorithm decisions can affect basic rights such as privacy, fairness, security and individual autonomy. With these concerns, governments, international organizations, and academic institutions have established guidelines and regulations to ensure that artificial intelligence systems are designed and implemented in a manner that upholds fundamental ethical principles. This work presents the results of a Systematic Literature Review using the PRISMA approach and aims to identify which approaches/methods are the most suitable ones for being used to integrate ethics and trustworthiness into AI tools for industrial engineering applications. Therefore, the review considered 38 pertinent scientific works published between 2019 and the end of August 2024. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

7 pages, 174 KB

Open AccessProceeding Paper

Industry 4.0 Enablers and Lean Manufacturing Tools in Respect of Human Resources

by Sanaa Jamari and Faycal Fedouaki

Eng. Proc. 2025, 97(1), 43; https://doi.org/10.3390/engproc2025097043 - 30 Jun 2025

Cited by 2 | Viewed by 2441

Abstract

The integration of Industry 4.0 (I4.0) and lean manufacturing (LM) has become a crucial approach for industries aiming to enhance accuracy, customization, competitiveness, and environmental sustainability. Manufacturers want to make their factories smarter and their operations more efficient by adopting advanced intelligent solutions. [...] Read more.

The integration of Industry 4.0 (I4.0) and lean manufacturing (LM) has become a crucial approach for industries aiming to enhance accuracy, customization, competitiveness, and environmental sustainability. Manufacturers want to make their factories smarter and their operations more efficient by adopting advanced intelligent solutions. The objectives of this article are to illustrate the impact of I4.0 tools on LM organizations and to clarify the importance of balancing technological progress with human-centered practices. Drawing on academic research, we propose a framework for human resources (HR) integration that fosters adaptability, continuous improvement, and employee engagement in the digital age. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

11 pages, 404 KB

Open AccessProceeding Paper

Enhanced Supplier Clustering Using an Improved Arithmetic Optimizer Algorithm

by Asmaa Akiki, Kaoutar Douaioui, Achraf Touil, Mustapha Ahlaqqach and Mhammed El Bakkali

Eng. Proc. 2025, 97(1), 44; https://doi.org/10.3390/engproc2025097044 - 30 Jun 2025

Viewed by 901

Abstract

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. [...] Read more.

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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5 pages, 1345 KB

Open AccessProceeding Paper

Improving Predictive Maintenance Performance Using Machine Learning and Vibration Analysis Algorithms

by Ibtissam Elharnaf, Khadija Achtaich and Samir Tetouani

Eng. Proc. 2025, 97(1), 45; https://doi.org/10.3390/engproc2025097045 - 2 Jul 2025

Cited by 2 | Viewed by 5056

Abstract

This research examines advanced machine learning techniques utilized for the predictive maintenance of industrial machinery. A hybrid model combining long-term memory networks (LSTM) and gated recurrent unit (GRU) networks alongside a random forest classifier has been created utilizing vibration data collected from sensors [...] Read more.

This research examines advanced machine learning techniques utilized for the predictive maintenance of industrial machinery. A hybrid model combining long-term memory networks (LSTM) and gated recurrent unit (GRU) networks alongside a random forest classifier has been created utilizing vibration data collected from sensors for fault classification purposes. The method includes feature extraction, time series analysis, and classification, utilizing the benefits of these models to efficiently manage sequential data. The results show significant improvements in forecasting accuracy, reduced downtime, and better-aligned maintenance schedules. These advancements demonstrate the capabilitie of integrating AI-driven solutions into industrial systems, consistent with Industry 4.0 principles, to improve operational capabilities. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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14 pages, 311 KB

Open AccessProceeding Paper

Enterprise-Wide Data Integration for Smart Maintenance: A Scalable Architecture for Predictive Maintenance Applications at Toyota Manufacturing

by Soufiane Douimia, Abdelghani Bekrar, Yassin El Hilali and Abdessamad Ait El Cadi

Eng. Proc. 2025, 97(1), 46; https://doi.org/10.3390/engproc2025097046 - 2 Jul 2025

Viewed by 2503

Abstract

Manufacturing enterprises implementing Industry 4.0 technologies face significant challenges in integrating heterogeneous maintenance data sources and deploying AI solutions effectively. While various AI methods exist for predictive maintenance, the fundamental challenge lies in creating a cohesive architecture that enables seamless data flow and [...] Read more.

Manufacturing enterprises implementing Industry 4.0 technologies face significant challenges in integrating heterogeneous maintenance data sources and deploying AI solutions effectively. While various AI methods exist for predictive maintenance, the fundamental challenge lies in creating a cohesive architecture that enables seamless data flow and AI deployment. This paper presents a standardized architecture framework with initial implementation steps at Toyota Motor Manufacturing France. The proposed architecture introduces a four-layer approach: (1) a unified data acquisition layer integrating IoT sensors, CMMS, and legacy systems through standardized interfaces (OPC UA/MQTT), (2) a data quality and standardization layer ensuring consistent formats and automated validation, (3) a modular AI deployment layer supporting anomaly detection (Wavelet-based analysis and Deep Learning) and remaining useful life prediction (LSTM networks), and (4) a maintenance workflow integration layer with bi-directional feedback. Key innovations include a unified maintenance data model, configurable data quality pipelines, and human-in-the-loop decision support. A conceptual validation suggests this architecture can improve integration efficiency and reduce equipment downtime. This research contributes to smart maintenance by providing a scalable architecture that balances interoperability, data quality, and practical deployment in brownfield environments. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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18 pages, 5755 KB

Open AccessProceeding Paper

Rule-Based Decisional Assistance for Sustainable Tire Management in the Transportation Sector

by Issam Mallouk, Fatimaezahraa Aboumejd, Chaima Zormati, Yves Sallez, Badr Abou El Majd, Ali El Oualidi and Mustapha Ahlaqqach

Eng. Proc. 2025, 97(1), 47; https://doi.org/10.3390/engproc2025097047 - 2 Jul 2025

Viewed by 1546

Abstract

The transportation sector is considered the number one consumer of tires worldwide. At present, 1.1 billion vehicles relying on this industry, although this number is increasing significantly following supply chain demands and the rapid economic growth. Among the main selection standards for fleet-operating [...] Read more.

The transportation sector is considered the number one consumer of tires worldwide. At present, 1.1 billion vehicles relying on this industry, although this number is increasing significantly following supply chain demands and the rapid economic growth. Among the main selection standards for fleet-operating enterprises are the quality, durability, and sustainability of tires, in efforts to ensure efficiency and safety for continuous operations. Lifecycle assessments (LCAs) are the optimal solution to optimize tire usage for extended performance and increased lifespan. In this paper, we developed a prediction model aiming at managing tires during the middle-of-life phase (use phase) while minimizing the negative environmental impact. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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16 pages, 439 KB

Open AccessProceeding Paper

Demand Forecasting in Multi-Retailer Supply Chains Within the Dairy Sector

by Abdeslam Mozher, Saad Fquiyeh, Jamal Lmariouh and Aziz Soulhi

Eng. Proc. 2025, 97(1), 48; https://doi.org/10.3390/engproc2025097048 - 27 Jun 2025

Cited by 1 | Viewed by 3083

Abstract

The dairy industry faces unique challenges in demand forecasting due to the perishability of products and the variability in consumer demand. This paper explores the state of the art in demand forecasting for multi-retailers in the dairy industry, highlighting the role of technological [...] Read more.

The dairy industry faces unique challenges in demand forecasting due to the perishability of products and the variability in consumer demand. This paper explores the state of the art in demand forecasting for multi-retailers in the dairy industry, highlighting the role of technological innovations and advanced methodologies. The integration of machine learning and data analytics is transforming demand forecasting, enabling more accurate predictions and efficient supply chain management. By addressing the complexities of the dairy supply chain and adapting to changing consumer preferences, retailers can optimize inventory levels, reduce waste, and enhance product availability. This study provides insights into current trends and methodologies, offering a comprehensive overview of demand forecasting in the dairy industry. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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12 pages, 1001 KB

Open AccessProceeding Paper

The Hub Location Problem in Air Transportation: A Review

by Mohamed Anas Khalfi, Jamila El Alami and Mustapha Hlyal

Eng. Proc. 2025, 97(1), 49; https://doi.org/10.3390/engproc2025097049 - 21 Jul 2025

Viewed by 1986

Abstract

The hub location problem is constantly examined in the field of air transportation, especially when designing networks for passenger airlines or express cargo providers. The competition that characterizes these businesses combined with the small benefit margins of the industry puts more pressure on [...] Read more.

The hub location problem is constantly examined in the field of air transportation, especially when designing networks for passenger airlines or express cargo providers. The competition that characterizes these businesses combined with the small benefit margins of the industry puts more pressure on finding innovative optimization tools when designing networks, locating hubs, and opening new routes with the minimum cost, usually under strict capacity constraints. This review covers the hub location problem in air transportation and its different mathematical models in preparation for a detailed SLR. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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8 pages, 934 KB

Open AccessProceeding Paper

Optimizing Order Scheduling in Morocco’s Garment Industry for Fast Fashion: A K-Means Clustering-Driven Approach

by Abdelfattah Mouloud, Yasmine El Belghiti, Samir Tetouani, Omar Cherkaoui and Aziz Soulhi

Eng. Proc. 2025, 97(1), 50; https://doi.org/10.3390/engproc2025097050 - 21 Jul 2025

Cited by 1 | Viewed by 1066

Abstract

The Moroccan garment industry faces challenges in scheduling small order batches, often hindered by traditional product family-based methods that increase downtime by 15–20%. This study proposes a clustering-based scheduling approach, grouping garments by technological times rather than product families to reduce changeovers and [...] Read more.

The Moroccan garment industry faces challenges in scheduling small order batches, often hindered by traditional product family-based methods that increase downtime by 15–20%. This study proposes a clustering-based scheduling approach, grouping garments by technological times rather than product families to reduce changeovers and downtime by 30–35%. A case study in a Moroccan factory with 50–100-unit batches showed a 20% lead time reduction and a 15% productivity boost. Using methods like K-Means, the approach enhances planning flexibility and resource use. This methodology offers a scalable solution for optimizing production and maintaining competitiveness in fast fashion markets. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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13 pages, 1900 KB

Open AccessProceeding Paper

Advanced Apple Conformity Detection Through Fuzzy Logic: A Novel Approach to Post-Harvest Quality Control

by El Mehdi Iyoubi, Raja El Boq, Samir Tetouani, Omar Cherkaoui and Aziz Soulhi

Eng. Proc. 2025, 97(1), 51; https://doi.org/10.3390/engproc2025097051 - 23 Jul 2025

Cited by 1 | Viewed by 1042

Abstract

Evaluating the approach of the apple’s maturity is a crucial aspect of enhancing agricultural efficiency, especially in the context of harvesting. Traditional approaches depend on fixed criteria that fail to account for the natural growth conditions of the fruit. To address this limitation, [...] Read more.

Evaluating the approach of the apple’s maturity is a crucial aspect of enhancing agricultural efficiency, especially in the context of harvesting. Traditional approaches depend on fixed criteria that fail to account for the natural growth conditions of the fruit. To address this limitation, a fuzzy logic-based system was introduced to evaluate apple ripeness. This model highlights a notable disparity between these factors and maturity. It incorporates the essential elements anticipated to correlate with ripeness, while maintaining the integrity of the inputs to create a holistic framework for assessing maturity. This system ensures that apples are harvested at the optimal time, thereby improving their overall quality. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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10 pages, 393 KB

Open AccessProceeding Paper

Artificial Intelligence for Optimal Water Resource Management: A Literature Review

by Wissal Ed-Dehbi, Mustapha Ahlaqqach and Jamal Benhra

Eng. Proc. 2025, 97(1), 52; https://doi.org/10.3390/engproc2025097052 - 24 Jul 2025

Cited by 2 | Viewed by 5577

Abstract

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and [...] Read more.

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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7 pages, 370 KB

Open AccessProceeding Paper

Multi-Criteria Decision-Making Using Fuzzy Logic for Production Order Planning in a Garment Workshop

by Bessem Kordoghli, Amel Babay, Mustapha Ahlaqqach and Mustapha Hlayal

Eng. Proc. 2025, 97(1), 53; https://doi.org/10.3390/engproc2025097053 - 30 Jul 2025

Viewed by 1156

Abstract

This work presents a new approach to introducing a new product in a production workshop, taking into account the products already introduced in the production lines. The proposal is based on a study of the skill requirements of the workforce and the mechanical [...] Read more.

This work presents a new approach to introducing a new product in a production workshop, taking into account the products already introduced in the production lines. The proposal is based on a study of the skill requirements of the workforce and the mechanical modifications of the product, with a multi-criteria decision-making process using fuzzy logic. This approach makes it possible to select the most suitable production line, minimise the number of machine changes, and reduce the time required to adapt the workforce, while ensuring the shortest possible order processing time. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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9 pages, 1238 KB

Open AccessProceeding Paper

Optimization of Mold Changeover Times in the Automotive Injection Industry Using Lean Manufacturing Tools and Fuzzy Logic to Enhance Production Line Balancing

by Yasmine El Belghiti, Abdelfattah Mouloud, Samir Tetouani, Mehdi El Bouchti, Omar Cherkaoui and Aziz Soulhi

Eng. Proc. 2025, 97(1), 54; https://doi.org/10.3390/engproc2025097054 - 30 Jul 2025

Cited by 1 | Viewed by 3178

Abstract

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are [...] Read more.

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are improved using fuzzy logic and AI for rapid changeover optimization on the NEGRI BOSSI 650 machine. A decrease in downtime by 65% and an improvement in the Process Cycle Efficiency by 46.8% followed the identification of bottlenecks, externalizing tasks, and streamlining workflows. AI-driven analysis could make on-the-fly adjustments, which would ensure that resources are better allocated, and thus sustainable performance is maintained. The findings highlight how integrating Lean methods with advanced technologies enhances operational agility and competitiveness, offering a scalable model for continuous improvement in industrial settings. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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22 pages, 1614 KB

Open AccessProceeding Paper

Integrated Blockchain, IoT, and Green Hydrogen Approach for Sustainable and Connected Supply Chain—Application Case in Morocco

by Abdellah Tetouani, Achraf Touil, Naoufal Rouky and Mouhsene Fri

Eng. Proc. 2025, 97(1), 55; https://doi.org/10.3390/engproc2025097055 - 11 Aug 2025

Cited by 5 | Viewed by 2940

Abstract

The global energy transition and digitalization are reshaping traditional production and consumption paradigms. Green hydrogen is emerging as a key element for decarbonizing sectors like industry and transportation, offering a viable alternative to fossil fuels and a pathway toward mitigating climate change. However, [...] Read more.

The global energy transition and digitalization are reshaping traditional production and consumption paradigms. Green hydrogen is emerging as a key element for decarbonizing sectors like industry and transportation, offering a viable alternative to fossil fuels and a pathway toward mitigating climate change. However, implementing green hydrogen supply chains presents challenges related to traceability, operational efficiency, and process certification. This paper explores how blockchain and the Internet of Things can address these challenges and transform the green hydrogen supply chain. Using Morocco as a case study—a country with abundant renewable resources and a strategic focus on green hydrogen—this article proposes innovative technological solutions to support a sustainable energy transition and contribute to a more secure and energy-efficient future. We analyze the current state of research on blockchain, IoT, and green hydrogen, identify key areas for advancement, and present a proposed framework for integrating these technologies. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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7 pages, 979 KB

Open AccessProceeding Paper

Transport Optimization in the Supply Chain Using the Ant Colony Algorithm

by Mourad Lahdhiri, Mohamed Jmali, Amel Babay and Mustapha Hlyal

Eng. Proc. 2025, 97(1), 56; https://doi.org/10.3390/engproc2025097056 - 30 Sep 2025

Viewed by 1158

Abstract

The shortest path problem is algorithmic and involves finding the least costly path (in terms of distance, time, cost, or other criteria) between two nodes in a graph. This problem is fundamental in graph theory and has applications in logistics, networks, mapping, and [...] Read more.

The shortest path problem is algorithmic and involves finding the least costly path (in terms of distance, time, cost, or other criteria) between two nodes in a graph. This problem is fundamental in graph theory and has applications in logistics, networks, mapping, and more. Several methods have been used to solve this problem. In this paper, we applied the ant colony algorithm to optimize the travel path of product quality technicians in a textile company specializing in washing and dyeing denim items. The company aims to minimize distances and costs between its subcontractors. The method has demonstrated a significant impact on distance and cost reduction while contributing to the reduction of the environmental effects. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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