Logistics

24 pages, 396 KiB

Open AccessArticle

Learning Decision Rules for a Stochastic Multiperiod Capacitated Traveling Salesperson Problem with Irregularly Clustered Customers

by Subei Mutailifu, Paolo Brandimarte and Aili Maimaiti

Logistics 2025, 9(3), 119; https://doi.org/10.3390/logistics9030119 - 19 Aug 2025

Abstract

Background: We consider a variant of the traveling salesperson problem motivated by the case of a company delivering furniture. The problem is both dynamic, due to random arrivals of delivery requests, and multiperiod, due to flexibility in delivering items within a time [...] Read more.

Background: We consider a variant of the traveling salesperson problem motivated by the case of a company delivering furniture. The problem is both dynamic, due to random arrivals of delivery requests, and multiperiod, due to flexibility in delivering items within a time window of a few days. A sequence of daily routes must be selected over time, and both volume and route duration constraints are relevant. Moreover, customers are irregularly distributed in clusters with high or low density. When receiving a request from a low-density cluster, we may consider the possibility of waiting for further requests from the same cluster, which involves a tradeoff between total traveled distance and service quality. Methods: We designed alternative decision policies based on approximate dynamic programming principles. We compared policy and cost function approximations, tuning their parameters by simulation-based optimization. Results: We compared the decision policies by realistic out-of-sample simulations. A simple trigger-based decision policy was able to achieve a good compromise among possibly conflicting objectives, without resorting to full-fledged multiobjective models. Conclusions: The insights into the relative strengths and weaknesses of the tested policies pave the way to practical extensions. Due to its computational efficiency, the trigger policy may be improved by base-policy rollout and integrated within a multi-vehicle routing architecture. Full article

(This article belongs to the Section Last Mile, E-Commerce and Sales Logistics)

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25 pages, 3177 KiB

Open AccessArticle

Designing Competitive Nanostore Networks for Enhanced Food Accessibility: Insights from a Competitive Facility Location Model

by Agatha Clarice da Silva-Ovando, Daniela Granados-Rivera, Gonzalo Mejía, Christopher Mejía-Argueta and Edgar Gutiérrez-Franco

Logistics 2025, 9(3), 118; https://doi.org/10.3390/logistics9030118 - 19 Aug 2025

Abstract

Background: Access to healthy food in emerging-economy cities is challenged by last-mile constraints and poor infrastructure. Aligned with the UN SDGs on Zero Hunger and Sustainable Cities, this study examines how a strategically located nanostores network can help close these gaps while [...] Read more.

Background: Access to healthy food in emerging-economy cities is challenged by last-mile constraints and poor infrastructure. Aligned with the UN SDGs on Zero Hunger and Sustainable Cities, this study examines how a strategically located nanostores network can help close these gaps while fostering local resilience. Focusing on Colombia’s Sabana Centro region, we designed a nanostore network that maximizes spatial coverage, proximity, and affordability. Methods: A competitive facility-location model combined with a discrete choice model captures consumer heterogeneity in price and location preferences. Results: Results show that locating nanostores in peripheral rather than central areas improves equity: the proposed network meets about 65,400 kg of weekly demand—51% fruit, 36% vegetables, 13% tubers—representing 16% of total regional demand and reaching underserved municipalities. This is notable given that existing nanostores already satisfy roughly 37% of household needs. Conclusions: By linking consumer behavior with sustainable spatial planning, the research offers both theoretical insight and practical tools for equitable distribution. Future work should evaluate supportive policies and supply chain innovations to secure nanostores’ long-term viability and community impact. Full article

(This article belongs to the Section Last Mile, E-Commerce and Sales Logistics)

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24 pages, 703 KiB

Open AccessArticle

The Role of Air Traffic Controllers’ Mindfulness in Enhancing Air Traffic Safety: JDR Theory in the Saudi Arabian Aviation Context

by Bader Alaydi, Siew-Imm Ng and Xin-Jean Lim

Logistics 2025, 9(3), 117; https://doi.org/10.3390/logistics9030117 - 15 Aug 2025

Abstract

Background: Air traffic control is a stressful job and vital to aviation safety. Although technological developments have been introduced to enhance and facilitate the tasks of air traffic control officers (ATCOs), ATCOs still experience high levels of job stress. This study explores [...] Read more.

Background: Air traffic control is a stressful job and vital to aviation safety. Although technological developments have been introduced to enhance and facilitate the tasks of air traffic control officers (ATCOs), ATCOs still experience high levels of job stress. This study explores the influence of mindfulness and social work support (SWS) on the job performance and job stress of ATCOs in Saudi Arabia. Methods: Grounded in Job Demands–Resources (JDR) theory, this study used a cross-sectional design to survey 324 ATCOs, with a 72% response rate. Mindfulness and SWS were treated as individual and situation-specific resources that influence stress and performance outcomes. Results: The results indicated that mindfulness could reduce workplace stress and improve performance. Moreover, SWS was also critical in reducing the adverse impacts of stress on job performance, reflecting the buffering effect posited by JDR theory. Conclusions: This study demonstrates that JDR theory is applicable to the context of ATC since it validates the importance of mindfulness and SWS as critical resources in minimizing stress levels and improving performance. The findings have implications for the viability of mindfulness-based training interventions and peer-support programs in supporting the health of ATCOs and their ability to deal with highly stressful situations. Full article

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20 pages, 2641 KiB

Open AccessArticle

Multi-Objective Decision Support Model for Operating Theatre Resource Allocation: A Post-Pandemic Perspective

by Phongchai Jittamai, Sovann Toek, Kingkan Kongkanjana and Natdanai Chanlawong

Logistics 2025, 9(3), 116; https://doi.org/10.3390/logistics9030116 - 14 Aug 2025

Abstract

Background: Healthcare systems are increasingly strained by limited operating room resources and rising demand, a situation intensified by the COVID-19 pandemic. These pressures have resulted in overcrowded surgical departments, prolonged waiting times for elective procedures, worsened patient health outcomes, and increased hospital [...] Read more.

Background: Healthcare systems are increasingly strained by limited operating room resources and rising demand, a situation intensified by the COVID-19 pandemic. These pressures have resulted in overcrowded surgical departments, prolonged waiting times for elective procedures, worsened patient health outcomes, and increased hospital expenditure costs. Methods: To address these challenges, this study proposes a multi-objective mathematical optimization model as the analytical core of a decision support approach for OR resource allocation. The model considers multiple constrained resources, including OR time, intensive care units, medium care units, and nursing staff, and aims to minimize both elective patients’ waiting times and total incurred costs over a one-week planning horizon. Developed using real hospital data from a large facility in Thailand, the model was implemented in LINGO version 16.0, and a sensitivity analysis was conducted to assess the impact of surgical department priorities and overtime allowances. Results: Compared to current practices, the optimized OR schedule reduced average waiting times by approximately 7% and total costs by 5%, while balancing resource utilization. Conclusions: This study provides a data-driven tool to support hospital resource planning, improve OR efficiency, and respond effectively to future healthcare crises. Full article

(This article belongs to the Section Humanitarian and Healthcare Logistics)

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16 pages, 2058 KiB

Open AccessSystematic Review

Transforming Humanitarian Supply Chains Through Green Practices: A Systematic Review

by Angie Ramirez-Villamil and Anicia Jaegler

Logistics 2025, 9(3), 115; https://doi.org/10.3390/logistics9030115 - 14 Aug 2025

Abstract

Background: This systematic review explores the integration of green practices into humanitarian supply chains to mitigate environmental impacts and contribute to global decarbonization efforts. Methods: This review focused on peer-reviewed articles published between 2011 and 2024 that addressed the environmental dimension [...] Read more.

Background: This systematic review explores the integration of green practices into humanitarian supply chains to mitigate environmental impacts and contribute to global decarbonization efforts. Methods: This review focused on peer-reviewed articles published between 2011 and 2024 that addressed the environmental dimension of humanitarian logistics. Studies were included if they examined environmental practices within humanitarian supply chains and excluded if they lacked focus on environmental impact or logistics. A comprehensive search of the Scopus database in April 2024 yielded 291 records, of which 51 studies met the inclusion criteria. A thematic synthesis was conducted; due to the qualitative nature of the data, no formal risk-of-bias assessment was conducted. Results: The analysis revealed increasing adoption of environmentally focused practices, such as emissions monitoring, waste reduction, and resource-efficient transportation. Key barriers included operational complexity, inadequate digital infrastructure, and the absence of standardized environmental frameworks. The review identified digital innovation, inter-organizational collaboration, and integrated environmental performance metrics as promising pathways for improvement. Despite growing awareness, significant gaps remain in the standardization and measurement of environmental performance across humanitarian supply chains. Conclusions: The findings highlight the need for further research and coordinated efforts to develop consistent, scalable green practices in the humanitarian context. Full article

(This article belongs to the Section Humanitarian and Healthcare Logistics)

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37 pages, 3590 KiB

Open AccessArticle

Efficient Simulation Algorithm and Heuristic Local Optimization Approach for Multiproduct Pipeline Networks

by András Éles and István Heckl

Logistics 2025, 9(3), 114; https://doi.org/10.3390/logistics9030114 - 12 Aug 2025

Abstract

Background: Managing multiproduct pipeline systems is a complex task of critical importance in the petroleum industry. Experts frequently rely on simulation tools to design and validate pumping operation schedules. However, existing tools are often problem-specific and too slow to be effectively used for [...] Read more.

Background: Managing multiproduct pipeline systems is a complex task of critical importance in the petroleum industry. Experts frequently rely on simulation tools to design and validate pumping operation schedules. However, existing tools are often problem-specific and too slow to be effectively used for optimization purposes. Methods: In this paper, a new scheduling model is introduced, which inherently eliminates all conflicts except for tank overflows and underflows. A Discrete-Event Simulation algorithm was developed, capable of handling mesh-like pipeline topologies, reverse flows, and interface tracking. The computational performance of the new method is demonstrated using three local search-based optimization variants, including a simulated annealing metaheuristic. Results: A case study was made involving four problems, with 4–6 sites and 5–7 products in mesh-like and straight topologies, respectively, and a large-scale instance. Scheduling horizons of 2–28 days were used. The proposed simulation algorithm significantly outperforms a prior approach in speed, and the optimization algorithms effectively converged to feasible, high-quality schedules for most instances. Conclusions: This paper proposes a novel simulation technique for multiproduct pipeline scheduling along with three local search algorithm variants that demonstrate optimization capabilities. Full article

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33 pages, 1079 KiB

Open AccessArticle

Enhancing Coordination and Decision Making in Humanitarian Logistics Through Artificial Intelligence: A Grounded Theory Approach

by Panagiotis Pantiris, Petros L. Pallis, Panos T. Chountalas and Thomas K. Dasaklis

Logistics 2025, 9(3), 113; https://doi.org/10.3390/logistics9030113 - 11 Aug 2025

Abstract

Background: The adoption of artificial intelligence (AI) in humanitarian logistics is essential for improving coordination and decision making, especially in the challenging landscape of disaster-relief settings. However, the current literature offers limited empirical evidence with respect to the specific impact of AI on [...] Read more.

Background: The adoption of artificial intelligence (AI) in humanitarian logistics is essential for improving coordination and decision making, especially in the challenging landscape of disaster-relief settings. However, the current literature offers limited empirical evidence with respect to the specific impact of AI on coordination and decision making for real-life humanitarian problems. Based on evidence from the humanitarian sector, this paper focuses on how AI could help humanitarian organizations collaborate better, streamline relief supply-chain operations and use resources more effectively. Methods: Twelve key themes influencing AI integration are identified by the study using a Grounded Theory (GT) approach based on interviews with experts from the humanitarian sector. These themes include data reliability, operational limitations, ethical considerations and cultural sensitivities, among others. Results: The findings suggest that AI improves forecasting, planning and inter-organizational coordination and is especially useful during the preparedness and mitigation stages of relief operations. Successful adoption, however, depends on adjusting tools to actual field conditions, building trust and training and striking a balance between algorithmic support and human expertise. Conclusions: The paper offers useful and practical advice for humanitarian organizations looking to use AI technologies in an ethical way while taking into account workforce capabilities, cross-agency cooperation and field-level realities. Full article

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17 pages, 2673 KiB

Open AccessArticle

Green Cold Chain Logistics: Minimising Greenhouse Gas Emissions of Fresh Food Products in Transport Refrigeration Units

by Manu Mohan and Shohel Amin

Logistics 2025, 9(3), 112; https://doi.org/10.3390/logistics9030112 - 11 Aug 2025

Abstract

Background: The growing demand for fresh food leads to extensive use of cold chain logistics (CCL) that significantly contributes to greenhouse gas (GHG) emissions due to its dependence on energy-intensive transport refrigeration units (TRUs). Understanding the need to balance food preservation with [...] Read more.

Background: The growing demand for fresh food leads to extensive use of cold chain logistics (CCL) that significantly contributes to greenhouse gas (GHG) emissions due to its dependence on energy-intensive transport refrigeration units (TRUs). Understanding the need to balance food preservation with environmental sustainability, this paper explores practical strategies for reducing GHG emissions in CCL, focusing on fresh food products. Methods: The quantitative and qualitative analyses are applied to analyse data from Transport for London and Transport Scotland. Emission data were assessed to evaluate the impact of alternative TRU technologies and route optimisation practices. Results: The findings reveal that electric and cryogenic TRUs, along with improved route planning and operational practices, can significantly reduce the emissions of carbon dioxide, nitrogen oxides and particulate matter. These results highlight the potential strategy for industry-led emission reductions without compromising food quality. Conclusions: This paper recommends the coordination of government policy and industry to support technological adaptation and infrastructure upgrades and to research into real-time monitoring and renewable energy integration in CCL systems. Full article

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55 pages, 2402 KiB

Open AccessReview

Planning of Logistic Networks with Automated Transport Drones: A Systematic Review of Application Areas, Planning Approaches, and System Performance

by Lukas Ostermann, Asrat Gobachew, Andreas Schwung and Stefan Lier

Logistics 2025, 9(3), 111; https://doi.org/10.3390/logistics9030111 - 8 Aug 2025

Abstract

Background: The increasing integration of automated transport drones into logistics networks presents transformative potential for addressing contemporary logistics challenges, particularly in last-mile delivery, healthcare, disaster response, urban mobility, and postal services. However, their effective integration into varied logistics contexts remains hindered by [...] Read more.

Background: The increasing integration of automated transport drones into logistics networks presents transformative potential for addressing contemporary logistics challenges, particularly in last-mile delivery, healthcare, disaster response, urban mobility, and postal services. However, their effective integration into varied logistics contexts remains hindered by infrastructure, regulatory, and operational limitations. This study aims to explore how drone-based logistics systems can be systematically planned and evaluated across diverse operational environments. Methods: A structured literature review was conducted, employing thematic synthesis to analyze current research on drone logistics. The analysis focused on identifying the key planning dimensions and interrelated components that influence the deployment of drone-based transport systems. Results: The review identified seven central planning dimensions: areas of application, system components, transport configuration, geographic areas, optimization and analysis methods, logistical planning, and performance assessment. These dimensions inform a conceptual framework designed to guide the planning and assessment of drone logistics networks. Conclusions: While existing studies contribute valuable insights into route optimization and drone deployment strategies, they often overlook integrative approaches that account for societal and environmental factors. The study emphasizes the need for interdisciplinary collaboration and context-specific planning frameworks to enhance the sustainable and effective implementation of drone-based logistics systems. Full article

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30 pages, 4687 KiB

Open AccessArticle

A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals

by Ilias Alexandros Parmaksizoglou, Alessandro Bombelli and Alexei Sharpanskykh

Logistics 2025, 9(3), 110; https://doi.org/10.3390/logistics9030110 - 8 Aug 2025

Abstract

Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes [...] Read more.

Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. Methods: This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. Results: Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. Conclusions: By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments. Full article

(This article belongs to the Section Maritime and Transport Logistics)

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16 pages, 2222 KiB

Open AccessArticle

Integration of Data Analytics and Data Mining for Machine Failure Mitigation and Decision Support in Metal–Mechanical Industry

by Sidnei Alves de Araujo, Silas Luiz Bomfim, Dimitria T. Boukouvalas, Sergio Ricardo Lourenço, Ugo Ibusuki and Geraldo Cardoso de Oliveira Neto

Logistics 2025, 9(3), 109; https://doi.org/10.3390/logistics9030109 - 7 Aug 2025

Abstract

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies [...] Read more.

Background: The growing complexity of production processes in the metal–mechanical industry demands ever more effective strategies for managing machine and equipment maintenance, as unexpected failures can incur high operational costs and compromise productivity by interrupting workflows and delaying deliveries. However, few studies have combined end-to-end data analytics and data mining methods to proactively predict and mitigate such failures. This study aims to develop and validate a comprehensive framework combining data analytics and data mining to prevent machine failures and support decision-making in a metal–mechanical manufacturing environment. Methods: First, exploratory data analytics were performed on the sensor and logistics data to identify significant relationships and trends between variables. Next, a preprocessing pipeline including data cleaning, data transformation, feature selection, and resampling was applied. Finally, a decision tree model was trained to identify conditions prone to failures, enabling not only predictions but also the explicit representation of knowledge in the form of decision rules. Results: The outstanding performance of the decision tree (82.1% accuracy and a Kappa index of 78.5%), which was modeled from preprocessed data and the insights produced by data analytics, demonstrates its ability to generate reliable rules for predicting failures to support decision-making. The implementation of the proposed framework enables the optimization of predictive maintenance strategies, effectively reducing unplanned downtimes and enhancing the reliability of production processes in the metal–mechanical industry. Full article

(This article belongs to the Special Issue Optimizations and Operations Management of Modern Logistic Systems and Supply Chains)

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18 pages, 2653 KiB

Open AccessArticle

Clustering of Countries Through UMAP and K-Means: A Multidimensional Analysis of Development, Governance, and Logistics

by Enrique Delahoz-Domínguez, Adel Mendoza-Mendoza and Delimiro Visbal-Cadavid

Logistics 2025, 9(3), 108; https://doi.org/10.3390/logistics9030108 - 7 Aug 2025

Abstract

Background: Growing disparities in development, governance, and logistics performance across countries pose challenges for global policymaking and Sustainable Development Goal (SDG) monitoring. This study proposes a classification of 137 countries based on multiple structural dimensions. The dataset for 2023 includes six components [...] Read more.

Background: Growing disparities in development, governance, and logistics performance across countries pose challenges for global policymaking and Sustainable Development Goal (SDG) monitoring. This study proposes a classification of 137 countries based on multiple structural dimensions. The dataset for 2023 includes six components of the Logistics Performance Index (LPI), six dimensions of the Worldwide Governance Indicators (WGIs), and four proxies of the Human Development Index (HDI). Methods: The Uniform Manifold Approximation and Projection (UMAP) technique was used to reduce dimensionality and allow for meaningful clustering. Based on the reduced space, the K-means algorithm was employed to group countries with similar development characteristics. Results: The classification process allowed the identification of three distinct groups of countries, supported by a Hopkins statistic of 0.984 and an explained variance ratio of 87.3%. These groups exhibit structural differences in the quality of governance, logistics capacity, and social development conditions. Internal consistency checks and multivariate statistical analyses (ANOVA and MANOVA) confirmed the robustness and statistical significance of the clustering. Conclusions: The resulting classification offers a practical analytical tool for policymakers to design differentiated strategies aligned with national contexts. Furthermore, it provides a data-driven approach for comparative monitoring of the SDGs from an integrated and empirical perspective. Full article

(This article belongs to the Special Issue Optimizations and Operations Management of Modern Logistic Systems and Supply Chains)

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33 pages, 26160 KiB

Open AccessArticle

Adaptive Intermodal Transportation for Freight Resilience: An Integrated and Flexible Strategy for Managing Disruptions

by Siyavash Filom, Satrya Dewantara, Mahnam Saeednia and Saiedeh Razavi

Logistics 2025, 9(3), 107; https://doi.org/10.3390/logistics9030107 - 6 Aug 2025

Abstract

Background: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances [...] Read more.

Background: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances Synchromodal Freight Transport (SFT) by integrating real-time disruption management. Methods: Building on recent advances, we propose two novel strategies: (1) Reassign with Delay Buffer, which enables dynamic rerouting of shipments within a user-defined delay tolerance, and (2) (De)Consolidation, which allows splitting or merging of shipments across services depending on available capacity. These strategies are incorporated into a re-planning module that complements a baseline optimization model and a continuous disruption-monitoring system. Numerical experiments conducted on a Great Lakes-based case study evaluate the performance of the proposed strategies against a benchmark approach. Results: Results show that under moderate and high-disruption conditions, the proposed strategies reduce delay and disruption-incurred costs while increasing the percentage of matched shipments. The Reassign with Delay Buffer strategy offers controlled flexibility, while (De)Consolidation improves resource utilization in constrained environments. Conclusions: Overall, the AIT framework demonstrates strong potential for improving operational resilience in intermodal freight systems by enabling adaptive, disruption-aware planning decisions. Full article

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14 pages, 849 KiB

Open AccessArticle

Autonomous Last-Mile Logistics in Emerging Markets: A Study on Consumer Acceptance

by Emerson Philipe Sinesio, Marcele Elisa Fontana, Júlio César Ferro de Guimarães and Pedro Carmona Marques

Logistics 2025, 9(3), 106; https://doi.org/10.3390/logistics9030106 - 6 Aug 2025

Abstract

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business [...] Read more.

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

(This article belongs to the Section Last Mile, E-Commerce and Sales Logistics)

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27 pages, 815 KiB

Open AccessArticle

Material Flow Analysis for Demand Forecasting and Lifetime-Based Inflow in Indonesia’s Plastic Bag Supply Chain

by Erin Octaviani, Ilyas Masudin, Amelia Khoidir and Dian Palupi Restuputri

Logistics 2025, 9(3), 105; https://doi.org/10.3390/logistics9030105 - 5 Aug 2025

Abstract

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined [...] Read more.

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

(This article belongs to the Section Sustainable Supply Chains and Logistics)

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25 pages, 5349 KiB

Open AccessReview

A Comprehensive Survey of Artificial Intelligence and Robotics for Reducing Carbon Emissions in Supply Chain Management

by Mariem Mrad, Mohamed Amine Frikha and Younes Boujelbene

Logistics 2025, 9(3), 104; https://doi.org/10.3390/logistics9030104 - 4 Aug 2025

Abstract

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence [...] Read more.

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

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26 pages, 3478 KiB

Open AccessArticle

Rethinking Routes: The Case for Regional Ports in a Decarbonizing World

by Dong-Ping Song

Logistics 2025, 9(3), 103; https://doi.org/10.3390/logistics9030103 - 4 Aug 2025

Abstract

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in [...] Read more.

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

(This article belongs to the Section Maritime and Transport Logistics)

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28 pages, 694 KiB

Open AccessArticle

Artificial Intelligence-Enabled Digital Transformation in Circular Logistics: A Structural Equation Model of Organizational, Technological, and Environmental Drivers

by Ionica Oncioiu, Diana Andreea Mândricel and Mihaela Hortensia Hojda

Logistics 2025, 9(3), 102; https://doi.org/10.3390/logistics9030102 - 1 Aug 2025

Abstract

Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a [...] Read more.

Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. Methods: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. Results: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. Conclusions: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains. Full article

(This article belongs to the Special Issue Advancing Circular Supply Chains: Integrating Logistics, Supply Chain Management and Circular Economy Practices)

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17 pages, 1142 KiB

Open AccessArticle

Logistical Challenges in Home Health Care: A Comparative Analysis Between Portugal and Brazil

by William Machado Emiliano, Thalyta Cristina Mansano Schlosser, Vitor Eduardo Molina Júnior, José Telhada and Yuri Alexandre Meyer

Logistics 2025, 9(3), 101; https://doi.org/10.3390/logistics9030101 - 31 Jul 2025

Abstract

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured [...] Read more.

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article

(This article belongs to the Section Humanitarian and Healthcare Logistics)

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