Logistics

16 pages, 1060 KB

Open AccessArticle

Dynamic Resource-Capability View, Agility, and Resilience in Supply Chain: An Organizational Strategy Perspective

by Sudhir Rana and Umesh Bamel

Logistics 2026, 10(5), 112; https://doi.org/10.3390/logistics10050112 - 12 May 2026

Background: Research on what promotes agility and resilience in the supply chain from an organizational strategy perspective is limited. This paper profiles the factors that can enable supply chain agility and resilience, with a special emphasis on organizational strategy. Method: Using [...] Read more.

Background: Research on what promotes agility and resilience in the supply chain from an organizational strategy perspective is limited. This paper profiles the factors that can enable supply chain agility and resilience, with a special emphasis on organizational strategy. Method: Using an exploratory approach, the study first identifies the enablers of supply chain agility and resilience and then applies Fuzzy Total Interpretive Structural Modeling (Fuzzy TISM) to rank them. Data were collected from experts using a literature-derived knowledge base. Results: The findings reveal key resource and knowledge-based enablers (Integration, both internal and external; Knowledge Management; Culture for Flexibility, Risk Management, Innovation, Organizational Ambidexterity, Absorptive Capacity, and Collaborative Communication) that strengthen resilience and agility, offering insights into mitigating disruptions caused by macro- and micro-level factors and global interdependencies. Conclusions: The study contributes by exploring the enablers of supply chain agility and resilience through an organizational strategy lens. By applying a rent-yielding mechanism grounded in resource and dynamic capability theories, the study advances theoretical maturity in this domain from an emerging-country context. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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35 pages, 8046 KB

Open AccessArticle

Digital Pathways to Efficiency: A Multi-Stakeholder Assessment of Sri Lanka’s Marine Fish Supply Chain Logistics

by Kariyawasam Pinikahana Gamage Lahiru Sandaruwan, Robert Jeyakumar Nathan, Shavindya Laksirini Sumanasekara, Thomas Ntangere and Maria Fekete Farkas

Logistics 2026, 10(5), 111; https://doi.org/10.3390/logistics10050111 - 11 May 2026

Abstract

Background: Studies of fish supply chain efficiency often rely on price spreads or frontier-based measures, which do not fully capture actor-level coordination performance in heterogeneous, informal supply chains. This study addresses this gap by developing a composite Market Efficiency Index (MEI) that [...] Read more.

Background: Studies of fish supply chain efficiency often rely on price spreads or frontier-based measures, which do not fully capture actor-level coordination performance in heterogeneous, informal supply chains. This study addresses this gap by developing a composite Market Efficiency Index (MEI) that integrates financial performance, operational quality, service equity, and relational governance. Methods: The MEI, a multidimensional alternative to frontier-based measures, was developed and applied to data collected from 250 supply chain actors in Sri Lanka. Results: The results show a clear efficiency gradient along the supply chain, with fishers scoring the lowest (MEI = 0.44), intermediaries moderate (MEI = 0.54), and retailers the highest (MEI = 0.67), yielding an overall system efficiency of 0.55 and relational governance emerging as the weakest system-level dimension. These results indicate persistent structural differences in value distribution and in how well the fish supply chain functions as a cohesive network, driven by liquidity constraints, information asymmetry, and weak cold-chain infrastructure. Conclusions: A multidimensional supply chain assessment provides a more effective basis for diagnosing coordination constraints and enables targeted digital interventions that offer feasible pathways to improve transparency, liquidity, and inclusiveness in smallholder-dominated fish supply chains. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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32 pages, 474 KB

Open AccessArticle

Demystifying the Digital Transformation of Humanitarian Supply Chains Through AidTech

by Apostolos Panagiotopoulos, Vasileios Karyotis, Georgios N. Dimitrakopoulos, Vassiliki Choleva-Tsiringkaki and Panos Kourouthanassis

Logistics 2026, 10(5), 110; https://doi.org/10.3390/logistics10050110 - 9 May 2026

Abstract

Background: Humanitarian campaigns are more popular with more initiatives organized than ever by public and private bodies. Simultaneously, more peculiar challenges, such as zero-waste and transparency, are required compared to traditional supply chains. This work investigates the current technological landscape for shaping [...] Read more.

Background: Humanitarian campaigns are more popular with more initiatives organized than ever by public and private bodies. Simultaneously, more peculiar challenges, such as zero-waste and transparency, are required compared to traditional supply chains. This work investigates the current technological landscape for shaping the next generation of humanitarian aid management systems, focusing on algorithmic and operational aspects, matching them to a new architecture for a target-platform, called AidTech. Methods: The methodology follows a systematic literature coverage, identifying cross-disciplinary trends from operations research, computer science and sustainable logistics. We examine the convergence of optimization, artificial intelligence, and blockchain-enabled traceability toward efficient and transparent humanitarian logistics. The employed methods include adaptive scheduling, resource allocation algorithms and privacy–preserving collaboration for meeting special constraints imposed by the humanitarian scope. Results: The findings focus on the architectural perspective of humanitarian supply chains and highlight that modern humanitarian infrastructures will increasingly rely on hybrid optimization methods integrating graph theory, dynamic routing under stochastic demand, multi-criteria decision analysis and distributed ledger technologies. Conclusions: We conclude that these paradigms, when combined under a unified cyber–physical architecture, e.g., AidTech, can substantially improve responsiveness, equity and sustainability in crisis management, further shaping future humanitarian logistics. Full article

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

31 pages, 844 KB

Open AccessArticle

Simulation Model and Intelligent Optimization Methods for Freight Transportation Under the Digital Transformation of the Transport System of the Republic of Kazakhstan

by Aizhan Kamysbayeva, Alisher Khussanov, Botagoz Kaldybayeva, Oleksandr Prokhorov, Zhakhongir Khussanov, Aibarsha Dosmakanbetova, Baurzhan Korganbayev and Aikerim Issayeva

Logistics 2026, 10(5), 109; https://doi.org/10.3390/logistics10050109 - 8 May 2026

Abstract

Background: In the context of the digital transformation of transport systems and the increasing complexity of logistics flows, the role of intelligent route forming methods capable of accounting for the spatial structure of transport networks, time constraints and resource limitations is growing. [...] Read more.

Background: In the context of the digital transformation of transport systems and the increasing complexity of logistics flows, the role of intelligent route forming methods capable of accounting for the spatial structure of transport networks, time constraints and resource limitations is growing. This issue is particularly relevant for the Republic of Kazakhstan, which is characterized by a vast territory, a distributed network of transport nodes and significant transit potential. Methods: This article presents an integrated model for the intelligent optimization of freight transportation based on the combined use of the Google OR-Tools library and simulation modeling in the AnyLogic environment with the application of geographic information technologies. The main variants of vehicle routing problems are implemented, including VRPTW, CVRP, and MDVRP. Results: The developed model enables both identification of optimal routes and simulation of their execution in a dynamic environment, forming the basis for a digital twin of the transport system. Experimental studies demonstrate the impact of time constraints, capacity limitations, and spatial structure on routing solutions. Conclusions: The results confirm the effectiveness of the proposed approach for logistics flow distribution in a distributed transport system and its potential for decision support in the digital transport sector. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

33 pages, 2167 KB

Open AccessArticle

Dynamic Analysis of the Impact of U.S. Tariff Policies on Automotive Production in Mexico

by Laura Valentina Bocanegra-Villegas, Cuauhtémoc Sánchez-Ramírez and Jorge Luis García-Alcaraz

Logistics 2026, 10(5), 108; https://doi.org/10.3390/logistics10050108 - 7 May 2026

Abstract

Background: A system dynamics model was developed to assess how United States (U.S.) tariff policies impact production, exports, labor, and Gross Domestic Product (GDP) in the Mexican automotive sector. Methods: Beginning with a baseline scenario without tariffs, the model introduces increases of [...] Read more.

Background: A system dynamics model was developed to assess how United States (U.S.) tariff policies impact production, exports, labor, and Gross Domestic Product (GDP) in the Mexican automotive sector. Methods: Beginning with a baseline scenario without tariffs, the model introduces increases of up to 25% in exports to the U.S. Additionally, it analyzes a tariff increase alongside variations in demand elasticity (−0.5 to −1.6) over a 24-month period. Results: The results indicate that a 25% tariff leads to a sustained decrease in production, GDP, and exports, although adjustments in employment occur with some delay. Specifically, production volume contracts by 34.9%, and exports contract by 31.3%. Furthermore, a greater absolute elasticity of demand corresponds to a more significant decrease in production. Conclusions: The findings indicate that geographic diversification is not an immediate substitute for the current export structure, as it requires significant logistical, organizational, and capital adjustments. Reducing dependence on the U.S. market would involve reconfiguring supply chain management, diversifying suppliers, and adapting distribution networks to gain greater flexibility. The sector’s vulnerability therefore lies in its high concentration in a single destination market, which restricts production adaptability in Mexico and weakens the capacity to respond efficiently to disruptions in the supply chain. Full article

(This article belongs to the Section Supplier, Government and Procurement Logistics)

17 pages, 636 KB

Open AccessArticle

From IT Capability to Manufacturing Flexibility: The Role of Knowledge Acquisition and Customer–Supplier Integration

by Pattama Lenuwat

Logistics 2026, 10(5), 107; https://doi.org/10.3390/logistics10050107 - 5 May 2026

Abstract

Background: This study examines how information technology (IT) capability contributes to manufacturing flexibility (MF) through knowledge acquisition (KA) and customer–supplier integration (CSI), drawing on dynamic capability theory. Methods: Survey data were collected from 137 manufacturing firms in Thailand and analyzed using [...] Read more.

Background: This study examines how information technology (IT) capability contributes to manufacturing flexibility (MF) through knowledge acquisition (KA) and customer–supplier integration (CSI), drawing on dynamic capability theory. Methods: Survey data were collected from 137 manufacturing firms in Thailand and analyzed using structural equation modeling to test the proposed relationships. Results: The findings show that IT capability significantly enhances KA, which subsequently strengthens CSI. While IT does not directly influence MF, its effect is transmitted through a sequential pathway in which IT improves KA, KA facilitates CSI, and CSI is the only construct directly associated with MF. These results identify CSI as the central conversion mechanism through which IT-enabled information and externally acquired knowledge are translated into operational adaptability. Conclusions: The study demonstrates that externally acquired knowledge generates value only when embedded in collaborative interorganizational routines, such as joint planning and synchronized decision-making. It further highlights the complementary roles of learning-oriented and integration capabilities in enabling manufacturing flexibility. Managerially, firms should prioritize IT investments that strengthen customer–supplier integration rather than focusing solely on technological sophistication. 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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24 pages, 390 KB

Open AccessArticle

Medical Aesthetics Clinic Location Selection Using SMART Single-Valued Neutrosophic TOPSIS

by Napat Harnpornchai and Worrawat Saijai

Logistics 2026, 10(5), 106; https://doi.org/10.3390/logistics10050106 - 2 May 2026

Abstract

Background: Location decision plays a key role in strategic logistics and business success. The beauty business in Thailand has continuously grown, and medical aesthetics clinic location is one of the critical factors for business success. The problem is also related to sustainable urban [...] Read more.

Background: Location decision plays a key role in strategic logistics and business success. The beauty business in Thailand has continuously grown, and medical aesthetics clinic location is one of the critical factors for business success. The problem is also related to sustainable urban service accessibility. Methods: This paper presents, for the first time, a systematic selection of medical aesthetics clinic location as a multi-criteria decision-making (MCDM) problem. The Simple Multi-Attribute Rating Technique (SMART) and Single-Valued Neutrosophic TOPSIS (SVN-TOPSIS) are combined to solve the location selection problem. SMART determines criterion weights, whereas SVN-TOPSIS evaluates alternatives using linguistic terms understandable to non-technical decision makers. Results: The proposed SMART SVN-TOPSIS is applied to a real investment problem in which two investors select the best clinic location from five alternatives with nine criteria. Siam Square—the heart of shopping, fashion, and youth culture in Bangkok—is recommended as the top location. Conclusions: The results indicate that the proposed method is capable of generating a consistent ranking of alternatives and differentiating between locations that exhibit similar evaluation characteristics. The findings may also support sustainable urban service planning and healthcare-related facility location decisions. Full article

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

26 pages, 1847 KB

Open AccessArticle

Supply Chain Management Research in the MENA Region (2000–2025): A PRISMA-Guided Systematic Review of Theories, Themes, and Research Gaps

by Sara Elzarka and Islam El-Nakib

Logistics 2026, 10(5), 105; https://doi.org/10.3390/logistics10050105 - 1 May 2026

Abstract

Background: Supply chain management (SCM) research has expanded across the Middle East and North Africa (MENA), yet the field remains fragmented. Limited synthesis exists on how regional conditions shape research themes, theories, and methods. Methods: This study applies the PRISMA 2020 [...] Read more.

Background: Supply chain management (SCM) research has expanded across the Middle East and North Africa (MENA), yet the field remains fragmented. Limited synthesis exists on how regional conditions shape research themes, theories, and methods. Methods: This study applies the PRISMA 2020 protocol to review SCM articles indexed in Scopus and Web of Science from January 2000 to March 2025. After screening and eligibility assessment, 512 peer-reviewed studies were retained. Bibliometric mapping and thematic coding were used to identify publication trends, research streams, theoretical lenses, and methodological patterns. Results: SCM research increased sharply after 2015, reflecting national diversification agendas, logistics reform, digitalization, and exposure to global supply chain disruptions. Three dominant streams were identified: resilience, sustainability, and digital transformation. Research output is concentrated in Saudi Arabia and the United Arab Emirates, while cross-country comparative studies remain scarce. Empirical studies rely mainly on cross-sectional surveys and SEM-based analysis, with limited longitudinal, qualitative, mixed-method, and comparative work across the region. Conclusions: The study develops an integrative SCM capability framework linking regional structural conditions, capability development, and supply chain outcomes. The findings support managers and policymakers seeking resilient, sustainable, and digitally enabled supply chains, and define clear future research priorities for the MENA region. Full article

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15 pages, 796 KB

Open AccessArticle

The Role of Artificial Intelligence in Logistics Firm Performance with Supply Chain Consistency and Logistics Capabilities in Saudi Arabia

by Sura Alayed and Sultan Alateeg

Logistics 2026, 10(5), 104; https://doi.org/10.3390/logistics10050104 - 1 May 2026

Abstract

Background: Rapid adoption of artificial intelligence (AI) in logistics enhances operational efficiency and firm performance; however, empirical evidence on its capability-driven impact remains limited, particularly in Saudi Arabia’s e-commerce sector. This study’s purpose is to examine the influence of AI on logistics firm [...] Read more.

Background: Rapid adoption of artificial intelligence (AI) in logistics enhances operational efficiency and firm performance; however, empirical evidence on its capability-driven impact remains limited, particularly in Saudi Arabia’s e-commerce sector. This study’s purpose is to examine the influence of AI on logistics firm performance through the mediation role of supply chain consistency and logistics capabilities. Methods: A quantitative study was conducted and data were collected using a convenience sampling technique from 275 employees working in the Saudi Arabian logistics firms. Partial Least Squares Structural Equation Modeling was used to perform data analysis. Results: The study findings indicated that AI usage has significant and positive influence on supply chain consistency (β = 0.290) and logistics capabilities (β = 0.303). Furthermore, supply chain consistency (β = 0.115) and logistics capabilities (β = 0.171) play mediating role between AI usage and firm performance. The research model exhibits substantial predictive capability, explaining 74.6% (R² = 0.746) of the variance in firm performance, while AI usage explains a smaller portion of the variance in supply chain consistency 8.4% (R² = 0.084) and logistics capabilities 9.2% (R² = 0.092). Conclusions: The findings demonstrate that AI-based logistics operations provide extensive support to streamline operations and reduce costs. Full article

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24 pages, 4720 KB

Open AccessSystematic Review

Triple A: How Analytics, AI, and Algorithms Are Improving Inventory Management in Healthcare

by Laquanda Leaven Johnson and Oghenetejiri Ebakivie

Logistics 2026, 10(5), 103; https://doi.org/10.3390/logistics10050103 - 1 May 2026

Abstract

Background: Healthcare inventory management is critical for ensuring timely access to supplies and reducing stockouts. As supply chains grow more complex, algorithms, AI, and analytics techniques have emerged as tools for forecasting, tracking, classification, and procurement. Yet empirical validation across diverse contexts [...] Read more.

Background: Healthcare inventory management is critical for ensuring timely access to supplies and reducing stockouts. As supply chains grow more complex, algorithms, AI, and analytics techniques have emerged as tools for forecasting, tracking, classification, and procurement. Yet empirical validation across diverse contexts remains inadequate, and existing reviews treat these approaches as separate streams rather than an integrated system. Methods: To evaluate these capabilities, a systematic review of 64 peer-reviewed articles published between 2011 and 2025 was conducted using a descriptive and content analysis approach on the use of Triple A (Analytics, AI, and Algorithms) techniques in inventory frameworks across various healthcare contexts, such as hospitals, pharmaceutical supply chains, and humanitarian supply chains. Results: Integrating multiple Triple A approaches consistently outperforms single-method strategies, particularly with RFID and IoT tools. Key findings often overlooked are: emergency procurement and classification, which remain neglected despite the highest patient safety stakes, and key procurement drivers—organizational conditions, supplier reliability, and team capacity. Data quality, interoperability, and cybersecurity further constrain generalizability. Conclusions: Bridging these gaps requires integrated Triple A approaches rather than single methods. Phased implementation, cloud-based platforms, and privacy-by-design offer practical pathways for building resilience under real-world constraints. Full article

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

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31 pages, 3161 KB

Open AccessArticle

Integration of Nursing and Pharmacy Inventory Decisions with DDD-Based EOQ: UK Institutional Calibration and Robustness Analysis

by Dilek Gümüş and Öner Gümüş

Logistics 2026, 10(5), 102; https://doi.org/10.3390/logistics10050102 - 1 May 2026

Abstract

Background: This study develops a transparent, decision-focused framework that integrates the World Health Organization’s defined daily dose (DDD) standard with the planned-backorder economic order quantity (EOQ) model to manage nursing and pharmacy workflows within a unified economic and operational scale. Method: Demand was [...] Read more.

Background: This study develops a transparent, decision-focused framework that integrates the World Health Organization’s defined daily dose (DDD) standard with the planned-backorder economic order quantity (EOQ) model to manage nursing and pharmacy workflows within a unified economic and operational scale. Method: Demand was expressed in DDD per year, and process-based costs were monetized according to National Health Service (NHS) workflow steps, where the holding cost was computed as H = r × cu and the delay cost B was derived from the target fill rate via a closed-form shadow-price relationship. The model was calibrated for a typical NHS acute-care hospital with 600 beds (D ≈ 130,305 DDD/year). Results: Calibration resulted in an ideal order quantity of 7554 DDD, an inter-order interval of 21 days, and a minimum annual total cost of £451. In the national conceptual scenario, the fill rate is about 99.4%, and the minimum annual total cost is £26,366. At this optimum, cost components are symmetrically balanced, with order cost and combined holding–delay cost contributing equally. Conclusions: This repeatable framework, based on the DDD scale, enhances management visibility regarding the cost–service balance, thereby confirming the policy’s robustness. Full article

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

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21 pages, 529 KB

Open AccessArticle

Profit Maximization of Ethanol Distribution on Manifold Surfaces: A Stochastic Nonlinear Programming Approach

by Emre Tokgoz, Iddrisu Awudu and Theodore Trafalis

Logistics 2026, 10(5), 101; https://doi.org/10.3390/logistics10050101 - 1 May 2026

Abstract

Background. Ethanol distribution in the energy supply chain can be maximized by solving a Location Routing Problem (LRP). Manifold LRP (MLRP) expands on the classic domain assumptions of LRP to manifold surfaces, and it can be applied to profit maximization in ethanol supply [...] Read more.

Background. Ethanol distribution in the energy supply chain can be maximized by solving a Location Routing Problem (LRP). Manifold LRP (MLRP) expands on the classic domain assumptions of LRP to manifold surfaces, and it can be applied to profit maximization in ethanol supply chains. Methods. In this work, a hybrid MLRP (H-MLRP) is introduced as a new mixed integer nonlinear programming NP-hard problem assuming discrete facility allocation that requires a mix of truck and train transportation for ethanol distribution from the facility to its customers. Ethanol supply chain profit can be maximized by solving a stochastic nonlinear integer programming problem (SNLP) using ethanol raw materials, production quantity, logistics, railcar shipments, and transit times as the decision variables. H-MLRP and SNLP are combined as a two-stage optimization methodology to design a biofuel energy distribution system for making optimal decisions to maximize ethanol profit. Results. A case study demonstrated the effectiveness of the proposed method on the relocation of an ethanol producer that is currently located in North Dakota (ND) to Oklahoma (OK). In this case study, customer demand destinations and suppliers of raw materials are located in different regions of the United States. Conclusions. The results indicate a good use of the new model for decision-making. Full article

16 pages, 1811 KB

Open AccessArticle

Non-Label-Based Goods Identification in Large-Scale Warehousing and Automated Logistics Operations Using Vision-Based OCR

by Mohammad Hori Najafabadi, Paria Mahmoudi and Bernd Noche

Logistics 2026, 10(5), 100; https://doi.org/10.3390/logistics10050100 - 1 May 2026

Abstract

Background: Automated identification of logistics units is a critical requirement in high-volume warehouse operations, particularly in retails that handle millions of cartons annually. Although barcode-based systems are widely used, they generate recurring costs for labeling, printing, quality control and readability issues, often [...] Read more.

Background: Automated identification of logistics units is a critical requirement in high-volume warehouse operations, particularly in retails that handle millions of cartons annually. Although barcode-based systems are widely used, they generate recurring costs for labeling, printing, quality control and readability issues, often leading to manual intervention and delays. Methods: This study presents a low-cost and flexible vision-based identification system that directly reads carton identifiers using optical character recognition (OCR). This system designed for edge deployment on resource-constrained hardware and incorporates a rotation-invariant preprocessing pipeline to support robust recognition under real conditions. Proposed approach was tested in two German retails. Results: Tests show recognition accuracies 96% to 98% under operational conditions, with real-time processing performance in the range of 58 to 125 ms per scan, depending on the hardware. These indicate that the system can be integrated into high-throughput logistics workflows. Additionally, the study provides insights into the economic implications of replacing barcode-based identification. Based on site-specific observations and labeling costs, the system shows the potential to reduce manual intervention and lower operational expenses in large-scale retails. Conclusions: Findings suggest that OCR can serve a cost-efficient alternative to barcode systems in environments where flexibility, robustness, and low deployment cost are critical. Full article

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24 pages, 555 KB

Open AccessArticle

A Mathematical Model to Maximize the Pre-Processing, Storage, and Transportation Associated with Grain Flow in Brazil

by Jonathan Vieira, Alvaro Neuenfeldt Júnior, Paulo Carteri Coradi, Olinto Araújo and Vanessa Alves

Logistics 2026, 10(5), 99; https://doi.org/10.3390/logistics10050099 - 1 May 2026

Abstract

B a c k g r o u n d

: In the grain logistics context, pre-processing operations such as reception, pre-cleaning, drying, storage, and shipping are performed at farm, collecting, intermediate, sub-terminal, and terminal storage units to preserve quality, reduce losses, and [...] Read more.

B a c k g r o u n d

: In the grain logistics context, pre-processing operations such as reception, pre-cleaning, drying, storage, and shipping are performed at farm, collecting, intermediate, sub-terminal, and terminal storage units to preserve quality, reduce losses, and add value in the products. However, high transportation costs and limited static storage capacity reduce the selling prices. The objective of this article is to maximize profit associated with pre-processing, storage, and transportation along the grain flow in Brazil.

M e t h o d s

: A generic post-harvest logistics network is represented as a graph connecting producers, multi-level storage units, agribusiness facilities, and ports. A multi-period, multi-level mathematical model is applied in a case study framework explored in three scenarios, covering pre-cleaning, drying, storage, and transportation costs from production areas to commercialization nodes.

R e s u l t s

: In all three scenarios, road transport resulted in transportation costs ranging from approximately US$ 49 million to US$ 492 million, mainly over long distances.

C o n c l u s i o n s

: The location and static capacity of collecting and intermediate storage units strongly influenced transport, storage use, CO₂ emissions, and post-harvest efficiency. Also, the flow concentration increased heavy-vehicle traffic, reducing overall logistics performance. Full article

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32 pages, 1172 KB

Open AccessArticle

A Simulation-Based Integrated Decision-Support Framework for Auditable Green Logistics

by Gábor Nagy, Akylbek Umetaliev and Szabolcs Szentesi

Logistics 2026, 10(5), 98; https://doi.org/10.3390/logistics10050098 - 1 May 2026

Abstract

Background: Green logistics requires decision-support approaches that jointly address cost efficiency, emissions reduction, service reliability, and reporting transparency under dynamic operating conditions. Existing studies often treat optimization, predictive updating, stakeholder coordination, and emissions traceability separately, limiting integration. Methods: This study develops [...] Read more.

Background: Green logistics requires decision-support approaches that jointly address cost efficiency, emissions reduction, service reliability, and reporting transparency under dynamic operating conditions. Existing studies often treat optimization, predictive updating, stakeholder coordination, and emissions traceability separately, limiting integration. Methods: This study develops a simulation-based integrated decision-support framework that combines multi-objective mixed-integer linear programming (MILP), machine learning-based travel-time prediction in a rolling-horizon setting, cooperative allocation using a Shapley value mechanism, and ISO 14083:2023-aligned emissions accounting. A permissioned blockchain layer is included as a post-decision governance mechanism to support traceability. The framework is evaluated using industry-calibrated synthetic scenarios over a 30-day planning horizon with 50 independent simulation runs. Results: Under the tested scenarios, the integrated configuration reduced average CO₂ emissions per route by 27.6% (±2.4%), improved the cost index by 17.3% relative to the baseline, and increased on-time delivery to 96.8%. Robustness analyses showed average key performance indicator (KPI) deviations below 5%. Component-level analysis suggests that the main operational gains arise from the interaction between predictive updating and prescriptive optimization, while the blockchain layer mainly improves auditability. Conclusions: The framework improves environmental and operational performance under the tested simulation scenarios, although real-world validation remains necessary before deployment-level conclusions can be drawn. Full article

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

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23 pages, 1565 KB

Open AccessArticle

Disturbance-Aware Multi-Criteria Network Optimization for Carrier Selection and Risk-Aware Routing in Multimodal Logistics Systems

by Svitlana Onyshchenko, Oleksiy Melnyk, Martin Jurkovič, Piotr Gorzelanczyk, Viktor Berestenko, Eva Tvrdá and Terézia Debnárová

Logistics 2026, 10(5), 97; https://doi.org/10.3390/logistics10050097 - 1 May 2026

Abstract

Background: Efficient carrier selection and routing in multimodal logistics networks is increasingly complex due to operational uncertainty, fluctuating service reliability, and the need for disturbance-aware decision-support tools. This study develops an integrated optimization framework for simultaneous carrier selection and routing under operational [...] Read more.

Background: Efficient carrier selection and routing in multimodal logistics networks is increasingly complex due to operational uncertainty, fluctuating service reliability, and the need for disturbance-aware decision-support tools. This study develops an integrated optimization framework for simultaneous carrier selection and routing under operational disturbances. Methods: A disturbance-aware multi-criteria network optimization model is proposed that incorporates transportation cost, delivery time, reliability, and economic risk within a unified mathematical formulation. Operational disturbances are represented as parametric perturbations of network costs, enabling recalculation of routing decisions. Computational experiments were conducted on an illustrative multimodal network and additional synthetic networks. Results: The results show that routing decisions remain stable under disturbance levels up to 5% and reconfigure under higher perturbations. Comparative analysis with a classical cost-minimization routing model illustrates a potential reduction in expected economic risk exposure of approximately 25–30% within the illustrative experimental setting while maintaining comparable transportation time. Conclusions: The proposed framework integrates carrier selection and routing decisions in multimodal logistics systems and supports risk-aware decision-making under operational uncertainty. Full article

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22 pages, 2435 KB

Open AccessArticle

An Intuitionistic Fuzzy Analytical Hierarchy Process-Based Model for Environmentally Sustainable Development in Maritime Logistics and Supply Chains

by Muhamad Safuan Shamshol Bahri, S. Sarifah Radiah Shariff, Nazry Yahya, Chang Won Lee and Nur Farizan Tarudin

Logistics 2026, 10(5), 96; https://doi.org/10.3390/logistics10050096 - 1 May 2026

Abstract

Backgrounds: Ports are critical nodes in global logistics and supply chains, yet their operations generate substantial environmental and social externalities. Existing evaluation frameworks have limited capability to address uncertainty, ambiguity, and expert hesitation. Moreover, prior studies frequently examine isolated performance dimensions, overlooking the [...] Read more.

Backgrounds: Ports are critical nodes in global logistics and supply chains, yet their operations generate substantial environmental and social externalities. Existing evaluation frameworks have limited capability to address uncertainty, ambiguity, and expert hesitation. Moreover, prior studies frequently examine isolated performance dimensions, overlooking the interconnected roles of port authorities as landlords, regulators, operators, and community stakeholders. Methods: This study proposes an integrated evaluation framework using the Intuitionistic Fuzzy Analytical Hierarchy Process (IF-AHP) to assess environmentally sustainable port performance under uncertain decision environments. By incorporating membership, non-membership, and hesitation degrees, the approach improves the robustness of expert judgments and applies a dual consistency check to reduce bias. Empirical data are obtained from Malaysian port management professionals, enabling the development of a comprehensive framework that includes four main functions and twenty sub-functions. Results: Results reveal that the landlord function holds the highest priority, while operational sustainability dimensions receive the greatest emphasis, with a global weight of approximately 0.105. In contrast, community engagement and social initiatives are assigned relatively lower importance. Conclusions: The IF-AHP framework offers an uncertainty-aware tool that prioritizes sustainability functions, especially environmental mitigation and energy efficiency, enabling informed resource allocation, strategic planning, and policy formulation for balanced, sustainable port overall performance. Full article

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

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16 pages, 5250 KB

Open AccessArticle

Benchmarking Multi-Platform APIs and Fuzzy-AHP for Enhanced HAZMAT Emergency Logistics: A Case Study of Bangkok’s Expressway Network

by Wipaporn Kitthiphovanonth, Chalermchai Chaikittiporn, Arroon Ketsakorn and Korn Puangnak

Logistics 2026, 10(5), 95; https://doi.org/10.3390/logistics10050095 - 24 Apr 2026

Abstract

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process [...] Read more.

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures. Full article

(This article belongs to the Topic New Technological Solutions, Research Methods, Simulation and Analytical Models That Support the Development of Modern Transport Systems, 2nd Edition)

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21 pages, 950 KB

Open AccessArticle

Mode and Shelter Choice Planning During Evacuation: A Multinomial Logistic Regression Analysis of COVID-19-Induced Migration in India

by Vipulesh Shardeo and Anchal Patil

Logistics 2026, 10(4), 94; https://doi.org/10.3390/logistics10040094 - 21 Apr 2026

Abstract

Background: The COVID-19 pandemic triggered unprecedented mobility disruptions worldwide as governments imposed strict lockdowns to contain the spread of the virus. In India, prolonged restrictions severely affected economic activity, particularly for migrant workers, leading to a large-scale and unplanned exodus from urban [...] Read more.

Background: The COVID-19 pandemic triggered unprecedented mobility disruptions worldwide as governments imposed strict lockdowns to contain the spread of the virus. In India, prolonged restrictions severely affected economic activity, particularly for migrant workers, leading to a large-scale and unplanned exodus from urban employment centres to native places. This sudden population movement undermined containment efforts and contributed to the spatial diffusion of infections. Understanding evacuees’ behavioural responses during such crises is therefore critical for effective emergency logistics and evacuation planning. Methods: This study examines the determinants of transport mode and shelter choice decisions made by migrants during the COVID-19-induced evacuation in India. Using primary survey data, a multinomial logistic regression model is developed to analyze how socio-economic characteristics influence evacuees’ choices of travel mode and shelter type. Results: The results reveal significant heterogeneity in decision-making, highlighting the role of economic vulnerability and accessibility constraints in shaping evacuation behaviour. Conclusions: The findings offer actionable insights for policymakers and emergency planners to design inclusive evacuation strategies, improve crisis-responsive transportation planning, and enhance shelter provisioning in future pandemics or large-scale disruptions. The study contributes to the logistics and humanitarian operations literature by providing empirical evidence on evacuation behaviour under public health emergencies. Full article

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