Search Results (93)

We developed an optimization method with value stream mapping (VSM), dynamic system simulation, and processing time prediction. First, VSM was used to quantitatively analyze the production process, identify value-added and non-value-added activities, and build a mathematical model to describe the flow of resources and waste at various stages. Then, a discrete event simulation (DES) was applied to simulate changes in the production process under different improvement conditions and to assess the effect of improved production efficiency using stochastic event modeling. As a result, we identified potential bottlenecks based on the flow of resources and waste sources throughout the production process and proposed improvement solutions for higher efficiency based on production simulations by predicting processing times for stability of production plans. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

Airports are facing significant challenges due to the increasing number of air travel passengers. After a significant downturn during the COVID-19 pandemic, airports are implementing measures to enhance security and improve their level of service in response to rising demand. However, the rising passenger volume has led to increased congestion and longer waiting times, undermining operational efficiency and passenger satisfaction. While most previous studies have focused on static modeling or infrastructure improvements, few have addressed the problem of dynamically allocating passengers in real-time. To tackle this issue, this study proposes a mathematical model with a dynamic rescheduling framework to balance the workload across multiple departure areas where security screening takes place, while minimizing the negative impact on passenger satisfaction resulting from increased walking distances. The proposed model strategically allocates departure areas for passengers in advance, utilizing data-based predictions. A mixed integer linear programming (MILP) model was developed and evaluated through discrete event simulation (DES). Real operational data provided by Incheon International Airport Corporation (IIAC) were used to validate the model. Comparative simulations against four baseline strategies demonstrated superior performance in balancing workload, reducing waiting passengers, and minimizing walking distances. In conclusion, the proposed model has the potential to enhance the efficiency of the security screening stage in the passenger departure process. Full article

This paper examines the feasibility of transitioning road cargo to waterborne transport in the UK, aiming to reduce emissions and alleviate road congestion. Key objectives include (1) developing a modal shift technology to establish freight highways across the UK, (2) designing a small, decarbonized barge vessel concept that complements the logistics framework, and (3) assessing the economic and environmental viability of a multimodal logistics network. Using discrete event simulation (DES), four transportation scenarios were analyzed to evaluate the efficiency and sustainability of integrating coastal and inland waterways into the logistics framework. Results indicate that waterborne transport is more cost-effective and environmentally sustainable than road transport. A sweeping design study was conducted to optimize time, cost, and emissions. This model was applied to a case study, providing insights into optimal pathways for transitioning to waterborne freight by finding the optimized number of TEUs. Consequently, our study identified 96 TEUs as the optimal capacity to initiate barge design, balancing cost, time, and emissions, while 126 TEUs emerged as the best option for scalability. Findings offer critical guidance for supporting the UK’s climate goals and governmental policies by advancing sustainable transportation solutions. Full article

The study focuses on the redesign of the plant layout of a glass manufacturing company using the systematic layout planning (SLP) methodology and discrete event simulation (DES). Deficiencies in the current layout were identified, such as inefficient material flows and excessive distances travelled by workers. Detailed data on dimensions and operating times were collected and analysed using tools such as Stat-Fit to statistically validate the data and simulate it in FlexSim V 22.2.0. The SLP methodology allowed the identification of relationships between areas and the proposal of three alternative layout designs. The selected layout, with 90% material flow efficiency, significantly reduced the distances travelled by operators and improved organisation and location. Simulation results showed an average reduction of 52% in the distances travelled by workers, as well as a better distribution of workloads. This study demonstrates how the integration of simulation and systematic planning tools can improve operational efficiency and productivity in manufacturing companies. Full article

In the era of Industry 4.0, the integration of intelligent systems with human elements presents both opportunities and challenges. This study explores this interplay through the application of an industrial engineering technique to a real process issue, demonstrating originality in problem selection and solution tools, as well as the relevance of the results. An operational framework is proposed to drive digital transformation in manufacturing by balancing automated systems efficiency with the complexity of human activities, which include decision-making flexibility, adaptability, tacit knowledge and collaborative interaction. It examines Industry 4.0 domains to find solutions that use smart technology while enhancing human experience. A key element is the use of discrete-event simulation to create a digital replica of the existing process. This enabled a detailed analysis and the development of innovative, validated approaches through what-if scenarios. The implemented solutions led to a significant annual increase in productivity, the result of an overall improvement in process efficiency, which was also achieved through the identification and resolution of key process bottlenecks, confirming the method’s effectiveness. The research offers a scalable model for various sectors, emphasizing the need to integrate human aspects into intelligent systems. It highlights how technological progress should enrich, not overshadow, human contribution, contributing to a deeper understanding of digital transformation in intelligent manufacturing and service systems, where technology and humanity evolve together. Full article

Under the “dual-carbon” strategy, accurately quantifying carbon emissions in water conservancy projects is crucial to promoting low-carbon construction. However, existing life cycle assessment (LCA) methods for carbon emissions during the mechanical construction stage often fail to reflect actual processes and are limited by high costs and lengthy data collection, potentially leading to inaccurate estimates. To address these challenges, this paper proposes a carbon emission evaluation method for the mechanical construction stage, based on carbon footprint theory and discrete event simulation (DES). This method quantifies equipment operation time and energy consumption during the drilling and blasting processes, enabling a detailed and dynamic emission analysis. Using the Fumin Pumped Storage Power Station Tunnel Project as a case study, a comparative analysis is conducted to examine the carbon emission characteristics of drilling and blasting operations under different surrounding rock conditions based on DES. The validity of the proposed model is confirmed by comparing its results with monitoring data and LCA results. The results show a clear upward trend in carbon emission intensity as surrounding rock conditions deteriorate, with emission intensity rising from 8405.82 kgCO₂e/m for Class II to 16,189.30 kgCO₂e/m for Class V in the headrace tunnel. The total carbon emissions of the water conveyance tunnels reach 40,019.64 tCO₂e, with an average intensity of 13,565.98 kgCO₂e/m. This study presents a refined and validated framework for assessing the carbon emissions of pumped storage tunnels. It addresses key limitations of traditional LCA methods in the mechanical construction stage and provides a practical tool to support the green transition of hydraulic infrastructure. Full article

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world. Full article

The expected growth of electric vehicle (EV) usage will not only increase the energy demand but also bring the requirement to provide the necessary electrical infrastructure to handle the load. While charging infrastructure is becoming increasingly present in urban areas, special attention is required for transit traffic, not just for passengers but also for freight transport. Differences in the nature of battery charging compared to that of classical refueling require careful planning in order to provide a resilient electrical infrastructure that will supply enough energy at critical locations during peak hours. This paper presents a hybrid simulation model for analyzing fast-charging demand based on traffic flow, projected EV adoption, battery characteristics, and environmental conditions. The model integrates a probabilistic model for evaluating the charging requirements based on traffic flows with a discrete-event simulation (DES) framework to analyze charger utilization, waiting queues, and energy demand. The presented case of traffic flow on Slovenian motorways explored the expected power demands at various seasonal traffic intensities. The findings provide valuable insight for planning the charging infrastructure, the electrical grid, and also the layout by anticipating the number of vehicles seeking charging services. The modular design of the model allowed replacing key parameters with different traffic projections, supporting a robust scenario analysis and adaptive infrastructure planning. Replacing the parameters with real-time data opens the path for integration into a digital twin framework of individual EV charging hubs, providing the basis for development of an EV charging hub network digital twin. Full article

The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing. Full article

Background: Remanufacturing products for sustainability involves layout and production planning, tools and equipment, material arrangement and handling, inventory management, technology integration, and more. This study presents an empirical vision through a discrete event simulation (DES) model integrating lean manufacturing (LM) and supply chain (SC) strategies with industry 4.0 (I4.0) technologies, applied to a case in a railway company. Methods: The work presents scenarios following a methodology with an incremental approach to implement strategies of lean manufacturing (LM) and supply chain (SC) in the context of I4.0 and their effects represented in DES models with applicability in remanufacturing and production line management. Five simulation scenarios were analyzed according to strategies layered incrementally. Results: Behaviors and outcomes were compared across the scenarios considering the remanufactured engines, percentage of process time, human labor occupation, and the statistical analysis of the process capability. Scenario five achieved the objective of remanufacturing 40 engines in one year with a cycle time of 214.45 h. Conclusions: The purpose was to design an engine remanufacturing line incorporating LM and SC strategies via a DES model, highlighting the importance of their gradual adoption toward I4.0 implementation. The integration of previous strategies improves flexibility and productivity in manufacturing processes. Full article

The healthcare sector, as a complex industrial system, faces significant challenges in delivering quality care amid resource constraints, driving the increased adoption of discrete event simulation (DES) as a tool for enhancing operational efficiency. While DES has proven valuable in healthcare operations, there is limited understanding of the statistical distributions employed in its implementation and its technological evolution. This study conducts an innovative review examining the diffusion of DES in health services, analyzing both the statistical distributions used in medical services simulation and the advancement of DES technologies. Through a comprehensive analysis of 616 publications from 2010 to 2022, we investigated DES utilization patterns and technological evolution and conducted a comparative analysis between pre- and post-COVID-19 pandemic periods, evaluating publication trends by country. The results reveal a significant increase in DES publications, an expansion of journals publishing DES-related articles, and notable technological advancements in simulation capabilities, particularly following the COVID-19 pandemic. These findings demonstrate the growing relevance of DES in healthcare research and its crucial role in process automation and decision-making within the industrial healthcare environment. Full article

We engaged with health sector stakeholders and public health professionals within the health system through a participatory modeling approach to support policy-making in the early COVID-19 pandemic in Saskatchewan, Canada. The objective was to use simulation modeling to guide the implementation of public health measures and short-term hospital capacity planning to mitigate the disease burden from March to June 2020. We developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM). SD models the population-level transmission of COVID-19, ABM simulates individual-level disease progression and contact tracing intervention, and DES captures COVID-19-related hospital patient flow. We examined the impact of mixed mitigation strategies—physical distancing, testing, conventional and digital contact tracing—on COVID-19 transmission and hospital capacity for a worst-case scenario. Modeling results showed that enhanced contact tracing with mass testing in the early pandemic could significantly reduce transmission, mortality, and the peak census of hospital beds and intensive care beds. Using a participatory modeling approach, we not only directly informed policy-making on contact tracing interventions and hospital surge capacity planning for COVID-19 but also helped validate the effectiveness of the interventions adopted by the provincial government. We conclude with a discussion on lessons learned and the novelty of our hybrid approach. Full article

Production scheduling is a critical task in the management of manufacturing systems. It is difficult to derive an optimal schedule due to the problem complexity. Computationally expensive and time-consuming solutions have created major issues for companies trying to respect their customers’ demands. Simple dispatching rules have typically been applied in manufacturing practice and serve as a good scheduling option, especially for small and midsize enterprises (SMEs). However, in recent years, the progress in smart systems enabled by artificial intelligence (AI) and machine learning (ML) solutions has revolutionized the scheduling approach. Under different production circumstances, one dispatch rule may perform better than others, and expert knowledge is required to determine which rule to choose. The objective of this work is to design and implement a framework for the modeling and deployment of a deep reinforcement learning (DRL) agent to support short-term production scheduling. The DRL agent selects a dispatching rule to assign jobs to manufacturing resources. The model is trained, tested and evaluated using a discrete event simulation (DES) model that simulates a pilot case from the bicycle production industry. The DRL agent can learn the best dispatching policy, resulting in schedules with the best possible production makespan. Full article