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Logistics, Volume 9, Issue 3 (September 2025) – 49 articles

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17 pages, 536 KB  
Article
Leveraging Household Food Waste Consumer Behaviour to Optimise Logistics
by Sotiris Ntai, Maria Kontopanou and Foivos Anastasiadis
Logistics 2025, 9(3), 126; https://doi.org/10.3390/logistics9030126 - 2 Sep 2025
Abstract
Background: This study explores how consumer behaviour influences household food waste and its ripple effects on the efficiency of the agri-food supply chain. Methods: Using survey data, we applied regression analysis to analyse the links between shopping habits, household demographics, waste reduction [...] Read more.
Background: This study explores how consumer behaviour influences household food waste and its ripple effects on the efficiency of the agri-food supply chain. Methods: Using survey data, we applied regression analysis to analyse the links between shopping habits, household demographics, waste reduction goals, and disposal practices. Results: Results show that purchasing driven by promotions significantly boosts household waste, while waste reduction goals strongly reduce disposal behaviours. These results illustrate how irregular consumer purchasing patterns create upstream demand fluctuations, making inventory management and production planning more complex. The findings highlight opportunities for logistics improvements, such as demand-based inventory systems, optimised purchasing routines, adjusted promotional strategies, and consumer-involved forecasting models to cut waste and promote resource sustainability. Conclusions: This research connects consumer behaviour with supply chain management, offering practical insights for building more sustainable and efficient food supply chains through targeted logistics actions. Full article
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19 pages, 271 KB  
Article
Conceptualizing Warehouse 4.0 Technologies in the Third-Party Logistics Industry: An Empirical Study
by Erika Marie Strøm, Julie Amanda Busch, Lars Hvam and Anders Haug
Logistics 2025, 9(3), 125; https://doi.org/10.3390/logistics9030125 - 2 Sep 2025
Abstract
Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly [...] Read more.
Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly conceptual and speculative, rather than grounded in empirical research. To address this gap, this study contributes to defining L4.0 through the sub-area of Warehouse 4.0 (W4.0), focusing on the challenges of adopting I4.0 technologies in warehouses. Methods: Through the I4.0 and L4.0 literature, an initial classification of W4.0 technologies in third-party logistics (3PL) was developed. This was refined using a case study of a global logistics service provider (LSP) in the 3PL industry, through semi-structured interviews with stakeholders. Results: The empirical findings identify new application areas for I4.0 technology in 3PL warehouses, including horizontal and vertical system integration, big data, and cybersecurity, technologies that can enhance 3PL competitiveness. Conclusions: This study offers a structured classification of W4.0 technologies and insights into the application areas of W4.0 in 3PLs. It contributes practical insights into which I4.0 technologies are relevant for the 3PL warehouse industry and their potential application areas. Full article
22 pages, 2039 KB  
Article
ML and Statistics-Driven Route Planning: Effective Solutions Without Maps
by Péter Veres
Logistics 2025, 9(3), 124; https://doi.org/10.3390/logistics9030124 - 1 Sep 2025
Abstract
Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying [...] Read more.
Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying on full map-based infrastructure. Methods: A dataset of over 5000 Hungarian postal locations was used to evaluate five models: Haversine-based scaling with circuity, linear regression, second- and third-degree polynomial regressions, and a trained artificial neural network. Models were tested on the full dataset, and three example routes representing short, medium, and long distances. Both statistical accuracy and route-level performance were assessed, including a practical optimization task. Results: Statistical models maintained internal consistency, but systematically overestimated longer distances. The ANN model provided significantly better accuracy across all scales and produced routes more consistent with map-based paths. A new evaluation method was introduced to directly compare routing outputs. Conclusions: Practical route planning can be achieved without GIS services. ML-based estimators offer a cost-effective alternative, with potential for further improvement using larger datasets, additional input features, and the integration of travel time prediction. This approach bridges the gap between simplified approximations and commercial routing systems. Full article
(This article belongs to the Section Artificial Intelligence, Logistics Analytics, and Automation)
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25 pages, 526 KB  
Article
Integrating CRM, Lean Practices, and Use of IT to Enhance Operational Performance: The Mediating Role of Quality Information Sharing
by A. H. M. Yeaseen Chowdhury, M. M. Hussain Shahadat, Saurav Chandra Talukder, Arnold Csonka and Maria Fekete Farkas
Logistics 2025, 9(3), 123; https://doi.org/10.3390/logistics9030123 - 1 Sep 2025
Abstract
Background: This study explores the relationship among various supply chain management practices, including customer relationship management, lean practices, use of information technology, and quality of information sharing with operational performance in the readymade garments industry of Bangladesh. It also examines the mediating [...] Read more.
Background: This study explores the relationship among various supply chain management practices, including customer relationship management, lean practices, use of information technology, and quality of information sharing with operational performance in the readymade garments industry of Bangladesh. It also examines the mediating role of quality of information sharing in these relationships. Methods: Data were collected from 80 readymade garment companies across five different geographical locations, with companies of varying sizes (large, medium, and small), involving 365 respondents with a response rate of 65%. A self-administered questionnaire survey was conducted, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for the analysis. Results: The results indicate that all four practices significantly enhance operational performance, while customer relationship management and use of information technology also improve performance indirectly through quality of information sharing, unlike lean practices. Conclusions: The findings suggest that supply chain managers and stakeholders can improve operational performance by implementing supply chain management practices and understanding the complexities of their interrelationships. Full article
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27 pages, 1506 KB  
Article
Port Performance and Its Influence on Vessel Operating Costs and Emissions
by Livia Rauca, Catalin Popa, Dinu Atodiresei and Andra Teodora Nedelcu
Logistics 2025, 9(3), 122; https://doi.org/10.3390/logistics9030122 - 1 Sep 2025
Abstract
Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly [...] Read more.
Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO2 emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO2 emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO2 emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article
(This article belongs to the Section Sustainable Supply Chains and Logistics)
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16 pages, 1251 KB  
Article
Carbon Pricing and the Truckload Spot Market
by Andrew Balthrop, Justin T. Kistler, Yemisi Bolumole, Alex Scott and Chad W. Autry
Logistics 2025, 9(3), 121; https://doi.org/10.3390/logistics9030121 - 28 Aug 2025
Viewed by 277
Abstract
Background: Carbon pricing in the form of fuel taxes is an important tool for abating climate change. This study examines the impact and pass-through of fuel taxes in the truckload freight market. Methods: State-level truckload market data, integrated with retail diesel prices, are [...] Read more.
Background: Carbon pricing in the form of fuel taxes is an important tool for abating climate change. This study examines the impact and pass-through of fuel taxes in the truckload freight market. Methods: State-level truckload market data, integrated with retail diesel prices, are analyzed using fixed-effects regression modeling. Results: Taxes and fuel costs are not only passed on by diesel retailers to motor carriers; the results reveal the overshifting of diesel taxes from motor carriers to shippers. Conclusions: The findings are consistent with inelastic short-term demand for long-haul carriage, indicating that relatively large price increases will be necessary to reduce diesel consumption in the trucking industry. Full article
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31 pages, 2557 KB  
Article
A Simulated Annealing Solution Approach for the Urban Rail Transit Rolling Stock Rotation Planning Problem with Deadhead Routing and Maintenance Scheduling
by Alyaa Mohammad Younes, Amr Eltawil and Islam Ali
Logistics 2025, 9(3), 120; https://doi.org/10.3390/logistics9030120 - 22 Aug 2025
Viewed by 771
Abstract
Background: Urban rail transit ensures efficient mobility in densely populated metropolitan areas. This study focuses on the Cairo Metro Network and addresses the Rolling Stock Rotation Planning Problem (RSRPP), aiming to improve operational efficiency and service quality. Methods: A Mixed-Integer Linear [...] Read more.
Background: Urban rail transit ensures efficient mobility in densely populated metropolitan areas. This study focuses on the Cairo Metro Network and addresses the Rolling Stock Rotation Planning Problem (RSRPP), aiming to improve operational efficiency and service quality. Methods: A Mixed-Integer Linear Programming (MILP) model is developed to integrate rolling stock rotation, deadhead routing, and maintenance scheduling. Two single-objective formulations are introduced to separately minimize denied passengers and the number of Electric Multiple Units (EMUs) used. To address scalability for larger instances, a Simulated Annealing (SA) metaheuristic is designed using a list-based solution representation and customized neighborhood operators that preserve feasibility. Results: Computational experiments based on real-world data validate the practical relevance of the model. The MILP achieves optimal solutions for small and medium-sized instances but becomes computationally infeasible for larger ones. In contrast, the SA algorithm consistently produces high-quality solutions with significantly reduced solve times. Conclusions: To the best of the authors’ knowledge, this is the first study to apply SA to the urban rail RSRPP while jointly integrating deadhead routing and maintenance scheduling. The proposed approach proves to be robust and scalable for large metro systems such as Cairo’s. Full article
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24 pages, 396 KB  
Article
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
Viewed by 341
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 KB  
Article
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
Viewed by 392
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 KB  
Article
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
Viewed by 611
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 KB  
Article
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
Viewed by 286
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 KB  
Systematic 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
Viewed by 464
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 KB  
Article
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
Viewed by 290
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 KB  
Article
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
Viewed by 712
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 KB  
Article
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
Viewed by 642
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 KB  
Review
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
Viewed by 756
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 KB  
Article
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
Viewed by 428
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 KB  
Article
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
Viewed by 319
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 KB  
Article
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
Viewed by 561
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 KB  
Article
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
Viewed by 529
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 KB  
Article
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
Viewed by 539
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 KB  
Article
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
Viewed by 699
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 KB  
Review
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
Viewed by 916
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 KB  
Article
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
Viewed by 564
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 KB  
Article
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
Viewed by 757
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 KB  
Article
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
Viewed by 537
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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28 pages, 1431 KB  
Article
From Mine to Market: Streamlining Sustainable Gold Production with Cutting-Edge Technologies for Enhanced Productivity and Efficiency in Central Asia
by Mohammad Shamsuddoha, Adil Kaibaliev and Tasnuba Nasir
Logistics 2025, 9(3), 100; https://doi.org/10.3390/logistics9030100 - 29 Jul 2025
Viewed by 704
Abstract
Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and [...] Read more.
Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and disruptions, and incorporating modernized waste management and advancements in gold bar processing technologies. This study explores how advanced technologies and improved logistical processes can enhance efficiency and sustainability. Method: This paper examines gold production processes in Kyrgyzstan, a gold-producing country in Central Asia. The case study approach combines qualitative interviews with industry stakeholders and a system dynamics (SD) simulation model to compare current operations with a technology-based scenario. Results: The simulation model shows improved outcomes when innovative technologies are applied to ore processing, waste refinement, and gold bar production. The results also indicate an approximate twenty-five percent reduction in transport time, a thirty percent decrease in equipment downtime, a thirty percent reduction in emissions, and a fifteen percent increase in gold extraction when using artificial intelligence, smart logistics, and regional smelting. Conclusions: The study concludes with recommendations to modernize equipment, localize processing, and invest in digital logistics to support sustainable mining and improve operational performance in Kyrgyzstan’s gold sector. Full article
(This article belongs to the Topic Sustainable Supply Chain Practices in A Digital Age)
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32 pages, 2875 KB  
Article
Achieving Sustainable Supply Chains: Applying Group Concept Mapping to Prioritize and Implement Sustainable Management Practices
by Thompson McDaniel, Edit Süle and Gyula Vastag
Logistics 2025, 9(3), 99; https://doi.org/10.3390/logistics9030099 - 28 Jul 2025
Viewed by 749
Abstract
Background: Sustainability in supply chain management (SCM) practices is becoming increasingly important as environmental responsibility and social concerns, as well as enterprises’ competitiveness in terms of innovation, risk, and economic performance, become increasingly urgent. This paper aims to identify and prioritize concepts [...] Read more.
Background: Sustainability in supply chain management (SCM) practices is becoming increasingly important as environmental responsibility and social concerns, as well as enterprises’ competitiveness in terms of innovation, risk, and economic performance, become increasingly urgent. This paper aims to identify and prioritize concepts for implementing sustainable supply chains, drawing on sustainable supply chain management (SSCM) and green supply chain management (GSCM) techniques. Corporate supply chain managers across various industries, markets, and supply chain segments brainstormed management practices to enhance the sustainability of their supply chains. Four industry sectors were surveyed across five different value chain segments. Methods: A group concept mapping (GCM) approach incorporating multi-dimensional scaling (MDS) and hierarchical cluster analysis (HCA) was used. A hierarchy of practices is proposed, and hypotheses are developed about achievability and impact. Results: A decision-making matrix prioritizes eight solution concepts based on two axes: impact (I) and ease of implementation (EoI). Conclusions: Eight concepts are prioritized based on the optimal effectiveness of implementing the solutions. Pattern matching reveals differences between emerging and developed markets, as well as supply chain segments, that decision-makers should be aware of. By analyzing supply chains from a multi-part perspective, this research goes beyond empirical studies based on a single industry, geographic region, or example case. Full article
(This article belongs to the Section Sustainable Supply Chains and Logistics)
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39 pages, 936 KB  
Article
Prioritizing ERP System Selection Challenges in UAE Ports: A Fuzzy Delphi and Relative Importance Index Approach
by Nadin Alherimi, Alyaa Alyaarbi, Sara Ali, Zied Bahroun and Vian Ahmed
Logistics 2025, 9(3), 98; https://doi.org/10.3390/logistics9030098 - 23 Jul 2025
Viewed by 946
Abstract
Background: Selecting enterprise resource planning (ERP) systems for complex port environments is a significant challenge. This study addresses a key research gap by identifying and prioritizing the critical factors for ERP selection within the strategic context of United Arab Emirates (UAE) ports, which [...] Read more.
Background: Selecting enterprise resource planning (ERP) systems for complex port environments is a significant challenge. This study addresses a key research gap by identifying and prioritizing the critical factors for ERP selection within the strategic context of United Arab Emirates (UAE) ports, which function as vital hubs in global trade. Methods: A hybrid methodology was employed, first using the Fuzzy Delphi Method (FDM) to validate thirteen challenges with five industry experts. Subsequently, the Relative Importance Index (RII) was used to rank these challenges based on survey data from 48 UAE port professionals. Results: The analysis revealed “Cybersecurity concerns” as the highest-ranked challenge (RII = 0.896), followed by “Engagement with external stakeholders” (RII = 0.842), and both “Process optimization” and “Technical capabilities” (RII = 0.808). Notably, factors traditionally seen as critical in other sectors, such as “Organizational readiness” (RII = 0.746), were ranked significantly lower. Conclusions: The findings indicate a strategic shift in ERP selection priorities toward digital resilience and external integration rather than internal organizational factors. This research provides a sector-specific decision-support framework and offers actionable insights for port authorities, vendors, and policymakers to enhance ERP implementation in the maritime industry. Full article
(This article belongs to the Section Maritime and Transport Logistics)
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18 pages, 840 KB  
Article
Centralized vs. Decentralized Black-Mass Production: A Comparative Analysis of Lithium Reverse Logistics Supply Chain Networks
by Oluwatosin S. Atitebi and Erick C. Jones, Jr.
Logistics 2025, 9(3), 97; https://doi.org/10.3390/logistics9030097 - 23 Jul 2025
Viewed by 533
Abstract
Background: The transition to renewable energy is intensifying demand for lithium-ion batteries (LIBs), thereby increasing the need for sustainable lithium sourcing. Traditional mining practices pose environmental and health risks, which can be mitigated through efficient end-of-life recycling systems. Methods: This study [...] Read more.
Background: The transition to renewable energy is intensifying demand for lithium-ion batteries (LIBs), thereby increasing the need for sustainable lithium sourcing. Traditional mining practices pose environmental and health risks, which can be mitigated through efficient end-of-life recycling systems. Methods: This study proposes a modified lithium reverse logistics network that decentralizes black-mass production at distributed facilities before centralized extraction, contrasting with conventional models that transport raw LIBs directly to central processing sites. Using the United States as a case study, two mathematical optimization (mixed-integer linear programming) models were developed to compare the traditional and modified networks in terms of cost efficiency and carbon emissions. Results: The model indicates that the proposed network significantly reduces both operational costs and emissions. Conclusions: This study highlights its potential to support a greener economy and inform policy development. Full article
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