Logistics

26 pages, 4675 KB

Open AccessEditor’s ChoiceArticle

Examining Container Terminal Efficiency with Diverse Data Sources: Vessel, Truck, and Container Turnaround Times in Japanese Terminals

by Daigo Shiraishi, Wenru Zhang, Ryuichi Shibasaki and Yesim Elhan-Kayalar

Logistics 2026, 10(2), 51; https://doi.org/10.3390/logistics10020051 - 18 Feb 2026

Viewed by 1766

Abstract

Background: Improving container terminal efficiency requires a comprehensive understanding of the interactions between vessel, truck, and container operations, yet existing studies often analyzed these components separately. In Japanese container terminals, where digitalization initiatives are progressing, empirical evidence based on integrated operational data [...] Read more.

Background: Improving container terminal efficiency requires a comprehensive understanding of the interactions between vessel, truck, and container operations, yet existing studies often analyzed these components separately. In Japanese container terminals, where digitalization initiatives are progressing, empirical evidence based on integrated operational data remains limited. Methods: This study empirically analyzes turnaround times for vessels, trucks, and containers at five major Japanese container terminals using a composite dataset that integrates terminal operating system data, automatic identification system data, and liner service information. Descriptive statistical analyses and regression models are applied to examine vessel berthing time, truck arrival patterns and turnaround time, container dwell time within terminals, and container round-trip time outside terminals. Results: The analysis reveals distinct temporal patterns in terminal operations, including systematic morning–afternoon asymmetries and differences across cargo flows. Truck turnaround times increase with vessel calls and vary by time of day, while container dwell times are strongly influenced by terminal policies such as free-time rules. Regression analyses indicate that turnaround times are primarily affected by terminal-controlled factors. Conclusions: These findings demonstrate the importance of synchronizing quayside and landside operations. The study contributes integrated empirical evidence to the port digitalization literature and provides actionable insights for enhancing container terminal efficiency. Full article

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

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27 pages, 1919 KB

Open AccessArticle

An Optimization Model for Efficient Relocation of Hazardous Materials and Valuable Assets During Natural Disaster Warning Periods

by Ali Al Kalbani, Hakan Gultekin and Nasr Al Hinai

Logistics 2026, 10(2), 50; https://doi.org/10.3390/logistics10020050 - 18 Feb 2026

Viewed by 943

Abstract

Background: Natural disasters can trigger hazardous material (Hazmat) releases and damage valuable assets, increasing human, environmental, and economic losses. Effective pre-disaster relocation planning is therefore critical but operationally challenging. Methods: This study develops a mixed-integer programming model for the pre-disaster relocation [...] Read more.

Background: Natural disasters can trigger hazardous material (Hazmat) releases and damage valuable assets, increasing human, environmental, and economic losses. Effective pre-disaster relocation planning is therefore critical but operationally challenging. Methods: This study develops a mixed-integer programming model for the pre-disaster relocation of Hazmat and valuable assets (HVAs). The model jointly optimizes safe-location activation, fleet allocation, and trip scheduling within a limited warning period, subject to vehicle availability, storage and capacity limits. The objective minimizes total cost, including facility activation, transportation, and penalties for unrelocated inventories. The model is solved using the Gurobi Optimizer. A base scenario and sensitivity analyses on fleet size and safe-location capacity are conducted using data from a cyclone-prone region in Oman. Results: In the base scenario, 73.4% of HVAs are relocated by activating 10 safe locations. Sensitivity analysis shows rapid gains at small fleet sizes, followed by diminishing returns beyond a threshold. Over 95% of HVAs are relocated by doubling safe-location capacities with 80 vehicles or tripling capacities with 65 vehicles. Conclusions: Total vehicle capacity, time-window, and safe-location capacity constraints become binding at different thresholds, highlighting the need for balanced investments. The proposed model provides an analytics-driven decision-support tool for risk-aware, time-bounded disaster relocation planning. Full article

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

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21 pages, 586 KB

Open AccessEditor’s ChoiceArticle

The Role of AI-Driven Supply Chains in Shaping Agility, Adaptability, and Technology Adoption Under Market Turbulence

by Ahmed Adnan Zaid and Luay Jum’a

Logistics 2026, 10(2), 49; https://doi.org/10.3390/logistics10020049 - 17 Feb 2026

Cited by 1 | Viewed by 3257

Abstract

Background: This study examines the influence of AI-driven supply chains on the adoption of automation and robotics within Jordanian manufacturing firms, emphasizing the role of supply chain adaptability and agility as mediators and market turbulence as a moderator. Methods: Drawing on dynamic capabilities [...] Read more.

Background: This study examines the influence of AI-driven supply chains on the adoption of automation and robotics within Jordanian manufacturing firms, emphasizing the role of supply chain adaptability and agility as mediators and market turbulence as a moderator. Methods: Drawing on dynamic capabilities theory and institutional theory, the study develops a conceptual model and tests it using data collected from 337 managers through an online survey. The analysis was carried out through partial least squares structural equation modeling (PLS-SEM). Results: The results show that AI-driven supply chains significantly enhance both adaptability and agility. However, only agility has a direct and significant effect on the adoption of automation and robotics, while market turbulence significantly moderates the connection between supply chain adaptability and the adoption of automation and robotics, but not the relationship between agility and adoption. Conclusions: Theoretically, the study provides insight into the interplay among internal dynamic capabilities in shaping technology adoption under external uncertainty. These results provide actionable implications for managers operating in developing economies like Jordan, highlighting the significance of building agile capabilities and adopting AI technologies to support innovation. The study is limited by its focus on a single country and sector; future research should explore other industries and incorporate additional moderating or mediating variables. Full article

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23 pages, 319 KB

Open AccessArticle

Artificial Intelligence Adoption in Event Logistics: Barriers, Critical Success Factors, and Expert Consensus from a Delphi Study

by Sofia Matias, Alvaro Lopes Dias and Leandro Pereira

Logistics 2026, 10(2), 48; https://doi.org/10.3390/logistics10020048 - 12 Feb 2026

Viewed by 1716

Abstract

Background: Artificial Intelligence (AI) is increasingly adopted across logistics and service operations, yet limited research explains how it supports back-end event logistics or what factors enable or hinder its implementation. This study investigates how AI can be applied across event logistics processes [...] Read more.

Background: Artificial Intelligence (AI) is increasingly adopted across logistics and service operations, yet limited research explains how it supports back-end event logistics or what factors enable or hinder its implementation. This study investigates how AI can be applied across event logistics processes and identifies the key barriers and critical success factors shaping its adoption. Methods: A sequential exploratory qualitative design was employed. First, semi-structured interviews with experienced event professionals generated context-specific insights. These findings informed a two-round Delphi study with 10 experts, where items were prioritised and consensus assessed using Kendall’s coefficient of concordance (W) and chi-square tests. Results: The results indicate that AI delivers the greatest value in pre-event planning activities, particularly scheduling and supplier coordination. Resistance to change and the lack of industry-specific AI tools emerged as the main adoption barriers, while technological infrastructure, system integration, and change management were identified as critical success factors. Conclusions: The study provides practical guidance for event organisers and technology providers by highlighting where AI investments are most likely to generate operational benefits and how organisational readiness can be strengthened. It also underscores the need for improved sustainability-focused tools and better data practices. Full article

34 pages, 3679 KB

Open AccessArticle

Freight Allocation Logistics for HSR Intermodal Networks: GNN-RL Implementation and Ottawa–Quebec Corridor Case Study

by Yong Lin Ren and Anjali Awasthi

Logistics 2026, 10(2), 47; https://doi.org/10.3390/logistics10020047 - 12 Feb 2026

Cited by 1 | Viewed by 750

Abstract

Background: Freight allocation is a vital decision in distribution logistics to minimize costs and gain environmental benefits. In this paper, we address the problem of freight allocation optimization on an HSR intermodal network with application for the Ottawa–Quebec City corridor where the [...] Read more.

Background: Freight allocation is a vital decision in distribution logistics to minimize costs and gain environmental benefits. In this paper, we address the problem of freight allocation optimization on an HSR intermodal network with application for the Ottawa–Quebec City corridor where the HSR system will be constructed. Methods: We develop a novel allocation method in which GNNs encode the intermodal network topology and spatial features, while RL agents learn adaptive freight routing policies through reward optimization, which is enhanced by fractal accessibility metrics for spatial connectivity and MCDM for balancing cost, emissions, and service objectives as well as optimizing dynamic freight flows. The model incorporates geospatial data (population, distance), operational factors (demand, costs), and environmental or policy considerations. Addressing the gap in dynamic, multi-criteria cold-climate HSR freight allocation models for North America, we test our framework on the Ottawa–Quebec corridor. Results: The result shows that compared to traditional methods, the five-hub configuration reduces costs by 15–22% and emissions by 20–28%, while the 11-hub model maintains 94%+ service coverage with an 8–12% efficiency trade-off. Conclusions: The conclusion indicates that the HSR intermodal network is more efficient than road only. Sensitivity analysis highlights that key allocation offers policymakers and logistics planners actionable insights for balancing efficiency and accessibility in HSR freight networks. Full article

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24 pages, 1684 KB

Open AccessArticle

Incentive Strategies and Dynamic Game Analysis for Supply Chain Quality Governance from the Perspective of Agricultural Product Liability

by Jianlan Zhong and Hong Liu

Logistics 2026, 10(2), 46; https://doi.org/10.3390/logistics10020046 - 12 Feb 2026

Viewed by 650

Abstract

Background: From the perspective of product liability, this study explores how agricultural product e-commerce enterprises can enhance the quality of the agricultural product supply chain through quality incentive strategies. Methods: Based on a tripartite evolutionary game model, the strategic interactions among [...] Read more.

Background: From the perspective of product liability, this study explores how agricultural product e-commerce enterprises can enhance the quality of the agricultural product supply chain through quality incentive strategies. Methods: Based on a tripartite evolutionary game model, the strategic interactions among farmers, agricultural product e-commerce enterprises, and the government are analyzed. Results: The research finds that whether the system converges to the ideal equilibrium of “high-quality production—ex-ante quality cost-sharing—collaborative governance” depends on the combined effects of revenue distribution, liability costs, and external incentives or penalties. Among these, government-led collaborative governance plays a key guiding role in incentivizing enterprises and influencing farmers’ behaviors. The incentive measures implemented by e-commerce enterprises and government penalties can effectively curb farmers’ low-quality production behaviors. Conclusions: The study further reveals how factors such as ex-ante cost-sharing, liability allocation, and farmers’ conformity psychology affect the stability of agricultural product supply chain quality, thereby providing theoretical support for constructing a “policy-platform-farmer” collaborative governance framework. Full article

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29 pages, 4622 KB

Open AccessArticle

A Risk-Integrated Supplier Selection Framework for Shipbuilding Materials: A Hybrid BWM–TOPSIS Approach

by Sri Rejeki Wahyu Pribadi, Budi Santosa, Budisantoso Wirjodirdjo, Erwin Widodo, Sjarief Widjaja and Teguh Putranto

Logistics 2026, 10(2), 45; https://doi.org/10.3390/logistics10020045 - 11 Feb 2026

Viewed by 1235

Abstract

Background: Supplier selection in shipbuilding is a high-stakes decision problem due to stringent quality requirements, compressed construction schedules, and elevated project risks. This study develops a systematic decision-support framework for selecting shipbuilding material suppliers while enhancing supply-chain resilience. Methods: A hybrid [...] Read more.

Background: Supplier selection in shipbuilding is a high-stakes decision problem due to stringent quality requirements, compressed construction schedules, and elevated project risks. This study develops a systematic decision-support framework for selecting shipbuilding material suppliers while enhancing supply-chain resilience. Methods: A hybrid multi-criteria decision-making framework integrating the Best Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. BWM is used to derive consistent criteria weights with fewer pairwise comparisons, while TOPSIS ranks supplier alternatives based on their distances from ideal and negative-ideal solutions. Results: Quality emerges as the most influential criterion (weight = 0.460), followed by risk-related factors, underscoring the importance of compliance, reliability, and risk mitigation in shipbuilding procurement. The TOPSIS results indicate that Supplier 3 achieves the highest closeness coefficient (Ci = 0.592), followed by Supplier 4, Supplier 2, and Supplier 1, with strong consistency observed in expert judgments. Conclusions: The proposed BWM–TOPSIS framework is rigorous, transparent, and replicable, supporting a Quality–Risk-Oriented multi-sourcing strategy to enhance supply continuity and operational resilience. Full article

(This article belongs to the Special Issue Investment, Risk, and Sustainability in Maritime Logistics and Supply Chain)

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20 pages, 3878 KB

Open AccessArticle

Emergency Medical Logistics of Helicopter Air Ambulance Response-Time Reliability: A Monte Carlo Simulation

by James Cline and Dothang Truong

Logistics 2026, 10(2), 44; https://doi.org/10.3390/logistics10020044 - 11 Feb 2026

Viewed by 1028

Abstract

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies [...] Read more.

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

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27 pages, 808 KB

Open AccessArticle

Beyond the Buffer: A Hierarchical Blueprint for Resilient Supply Chain

by Narassima Madhavarao Seshadri, Anbuudayasankar Singanallur Palaniswamy, Olivia McDermott, Thenarasu Mohanavelu and Sumesh Arangot

Logistics 2026, 10(2), 43; https://doi.org/10.3390/logistics10020043 - 10 Feb 2026

Cited by 1 | Viewed by 1088

Abstract

Background: Supply Chain Flexibility (SCF) has transitioned from being viewed merely as a competitive edge to now being essential for survival during the current “high-impact, low-frequency” disruption era. Most research treats Supply Chain Flexibility enablement as a list of unrelated elements. Methods [...] Read more.

Background: Supply Chain Flexibility (SCF) has transitioned from being viewed merely as a competitive edge to now being essential for survival during the current “high-impact, low-frequency” disruption era. Most research treats Supply Chain Flexibility enablement as a list of unrelated elements. Methods: To address this gap, this study develops an Interpretive Structural Modelling (ISM) to illustrate how Supply Chain Flexibility elements impact “Data-Driven Organisational Culture” so they can provide core technology and operational capabilities to Supply Chain Flexibility. Results: The hierarchical structure developed indicates that the “Nature of Customers” is the main building block of Supply Chain Flexibility, whereas “Strategic Redundancies” are viewed as the result of an advanced Supply Chain Flexibility system rather than as an initial factor in Supply Chain Flexibility. This study develops an integrated framework to align the macro-level Supply Chain Flexibility elements with the five operational areas such as market, delivery, logistics, organisational and volume flexibility. Conclusions: The finding provides practical guidance for practitioners to prioritise foundational cultural and strategic investments before adopting software tools or other surface-level solutions, thereby supporting the systematic development of robust and sustainable Supply Chain Flexibility. Full article

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

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Graphical abstract

21 pages, 1220 KB

Open AccessArticle

Bridging Digitalization and Sustainability in Supply Chain Performance Measurement: An MLP-Based Predictive Model

by Mariem Mrad, Rym Belgaroui, Younes Boujelbene and Nagwa Amin Abelkawy

Logistics 2026, 10(2), 42; https://doi.org/10.3390/logistics10020042 - 9 Feb 2026

Viewed by 1117

Abstract

Background: The transition toward Industry 4.0 and Supply Chain 5.0 requires performance measurement frameworks that integrate efficiency, digitalization, and sustainability indicators. Although the SCOR^® 4.0 model provides standardized metrics, it lacks predictive capabilities under complex and nonlinear conditions. This study addresses [...] Read more.

Background: The transition toward Industry 4.0 and Supply Chain 5.0 requires performance measurement frameworks that integrate efficiency, digitalization, and sustainability indicators. Although the SCOR^® 4.0 model provides standardized metrics, it lacks predictive capabilities under complex and nonlinear conditions. This study addresses this gap by extending the SCOR^® framework and integrating it into an AI-based predictive model. Methods: A Multilayer Perceptron (MLP) neural network was developed to forecast Supply Chain Performance (SCP) using an expanded set of SCOR^® 4.0 indicators. Additional Level 1 and Level 2 metrics, capturing digitalization and sustainability (including carbon footprint and waste reduction), were incorporated. The MLP model was optimized and trained using the Levenberg–Marquardt algorithm on a synthetically generated dataset derived from deterministic Extended SCOR^® 4.0 formulations, in order to capture complex nonlinear relationships under controlled, simulation-based conditions. Results: Simulation-based validation demonstrates high predictive accuracy, achieving low RMSE, MAE, and MAPE values and strong correlation coefficients. Conclusions: The findings demonstrate the methodological feasibility and internal consistency of integrating extended SCOR^® metrics with an optimized MLP architecture for forecasting multidimensional SCP under simulated conditions in digital and sustainability-oriented supply chains; external validity to real operational environments remains to be established in future empirical studies. Full article

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36 pages, 3147 KB

Open AccessReview

Key Performance Indicators for Sustainable Supply Chain Management in SMEs: A Bibliometric Review

by Wipada Sompong, Siwarit Pongsakornrungsilp, Pimlapas Pongsakornrungsilp, Chukiat Siriwong, Vikas Kumar and Shishank Shishank

Logistics 2026, 10(2), 41; https://doi.org/10.3390/logistics10020041 - 9 Feb 2026

Cited by 1 | Viewed by 3602

Abstract

Background: This study presents a bibliometric analysis of key performance indicators (KPIs) for sustainable supply chain management (SSCM) in small- and medium-sized enterprises (SMEs). Despite growing academic attention, particularly after 2020, important gaps remain in how sustainability performance is measured and assessed in [...] Read more.

Background: This study presents a bibliometric analysis of key performance indicators (KPIs) for sustainable supply chain management (SSCM) in small- and medium-sized enterprises (SMEs). Despite growing academic attention, particularly after 2020, important gaps remain in how sustainability performance is measured and assessed in SME contexts. Methods: Using the Scopus database, we identified 169 relevant studies published between 2004 and 2025. The dataset was obtained through sustainability- and SME-related keyword filtering, followed by manual screening based on predefined eligibility criteria. Results: The findings reveal a research landscape dominated by economic and technological KPI dimensions, with Italy, India, and Indonesia emerging as leading contributors. However, the results also indicate limited research attention to social sustainability, organizational capabilities, and governance within SME supply chains. Overall, eight underexplored KPI domains are identified as opportunities for future research and practical development. Conclusions: This analysis clarifies the intellectual landscape of SSCM KPI research and provides evidence-based insights for researchers and practitioners regarding which KPI dimensions are emphasized and which remain underdeveloped for practical application in SME supply chains, without developing or validating a new KPI framework. 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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19 pages, 264 KB

Open AccessArticle

AI Diffusion and the New Triad of Supply Chain Transformation: Productivity, Perspective, and Power in the Era of Claude, ChatGPT, Gemini, LLaMA, and Mistral

by Paul C. Hong, Young B. Choi and Young Soo Park

Logistics 2026, 10(2), 40; https://doi.org/10.3390/logistics10020040 - 5 Feb 2026

Cited by 1 | Viewed by 2129

Abstract

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less [...] Read more.

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone. Full article

(This article belongs to the Special Issue Artificial Intelligence and Business Analytics Applications in Supply Chain Operations)

19 pages, 558 KB

Open AccessArticle

The Role of Supply Chain Risk Management in Shaping Supply Chain Resilience and Robustness: Empirical Evidence from Moroccan Manufacturing Firms

by Soukaina Sahab and Salah Oulfarsi

Logistics 2026, 10(2), 39; https://doi.org/10.3390/logistics10020039 - 5 Feb 2026

Viewed by 1873

Abstract

Background: this study aims to investigate the impact of supply chain risk management (SCRM) on supply chain resilience and robustness providing empirical evidence from an underexplored emerging economy. Methods: drawing on empirical data collected through a survey of 110 Moroccan manufacturing [...] Read more.

Background: this study aims to investigate the impact of supply chain risk management (SCRM) on supply chain resilience and robustness providing empirical evidence from an underexplored emerging economy. Methods: drawing on empirical data collected through a survey of 110 Moroccan manufacturing firms, the study tests a conceptual framework proposed using SmartPLS. Results: the results show that SCRM practices do not significantly reduce disruption impacts, which contrasts with several previous studies. However, supply chain robustness and resilience are significantly improved by SCRM practices. In examining the direct effects of disruption impacts, results indicate a significant negative influence on robustness, while no significant effect is observed on resilience. Furthermore, the association between supply chain outcomes and SCRM is not supported by the mediation effect of disruption impacts. Conclusions: to the best of the author’s knowledge, few studies have examined SCRM, resilience, and robustness simultaneously. Furthermore, no prior research has investigated the mediating role of disruption impacts, and almost no studies have focused on the Moroccan context. This study therefore bridges these gaps, providing new theoretical insights and practical implications for improving supply chain performance under uncertainty and disruptions. Full article

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35 pages, 944 KB

Open AccessArticle

Sustainable and Safe Last-Mile Delivery: A Multi-Objective Truck–Drone Matheuristic

by Armin Mahmoodi, Mehdi Davoodi, Said M. Easa and Seyed Mojtaba Sajadi

Logistics 2026, 10(2), 38; https://doi.org/10.3390/logistics10020038 - 4 Feb 2026

Cited by 4 | Viewed by 1243

Abstract

Background: The rapid growth of e-commerce has intensified the need for last-mile delivery systems that can navigate urban congestion while minimizing environmental impact. Hybrid truck–drone networks offer a promising solution by combining heavy-duty ground transport with aerial flexibility; however, their deployment faces [...] Read more.

Background: The rapid growth of e-commerce has intensified the need for last-mile delivery systems that can navigate urban congestion while minimizing environmental impact. Hybrid truck–drone networks offer a promising solution by combining heavy-duty ground transport with aerial flexibility; however, their deployment faces significant challenges in jointly managing operational risks, energy limits, and regulatory compliance. Methods: This study proposes a hybrid matheuristic framework to solve this multi-objective problem, simultaneously minimizing transportation cost, service time, energy consumption, and operational risk. A two-phase approach combines a metaheuristic for initial truck routing with a Mixed-Integer Linear Programming (MILP) formulation for optimal drone assignment and scheduling. This decomposition strikes a balance between exact optimization and computational scalability. Results: Experiments across various instance sizes (up to 100 customers) and fleet configurations demonstrate that integrating MILP enhances solution diversity and convergence compared to standalone strategies. Sensitivity analyses reveal significant impacts of drone speed and endurance on system efficiency. Conclusions: The proposed framework provides a practical decision-support tool for balancing complex trade-offs in time-sensitive, risk-constrained delivery environments, thereby contributing to more informed urban logistics planning. Full article

(This article belongs to the Special Issue Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition)

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24 pages, 989 KB

Open AccessArticle

Resilience in Humanitarian Logistics: Adjusting Capacity in the Points of Distribution in Natural Disaster Areas

by Raúl R. J. Heras-Garrido, Marco Serrato, José Holguín-Veras and Miguel Jaller

Logistics 2026, 10(2), 37; https://doi.org/10.3390/logistics10020037 - 3 Feb 2026

Viewed by 1167

Abstract

Background: The administration and management of humanitarian logistical operations are considered critical factors. The extreme and unexpected nature of events such as natural disasters poses logistical challenges to humanitarian support agencies providing aid to the affected area. These logistical challenges are characterized [...] Read more.

Background: The administration and management of humanitarian logistical operations are considered critical factors. The extreme and unexpected nature of events such as natural disasters poses logistical challenges to humanitarian support agencies providing aid to the affected area. These logistical challenges are characterized by fluctuations in demand that generate uncertainty in the required capacity for aid, all of which are emphasized at the last link in the humanitarian supply chain: the point of distribution for humanitarian aid (the so-called last-mile problem). The objective of this research work is to support the decision-making process regarding capacity adjustments and the closure of the distribution points established in the disaster area. Methods: In response, a Markovian Decision Model for Capacity Adjustment was developed, focused not only on reducing traditional logistics costs but also on minimizing human suffering by incorporating so-called deprivation costs. Results: The model establishes adjustment policies for capacity for each aid period, and the existence of a monotonous policy that establishes an optimal threshold for closure decisions was demonstrated. Conclusions: It is possible to efficiently adjust the capacity at the distribution points and minimize the costs (both logistical and deprivation) associated with each decision period. Full article

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

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22 pages, 1553 KB

Open AccessArticle

Framework to Support the Development of Collaborative and Sustainable Biofuels Supply Chains in Ethiopia

by Teshale Tadesse Fufa, Ludovic Montastruc, Stéphane Negny, Léa van der Werf and Abubeker Yimam

Logistics 2026, 10(2), 36; https://doi.org/10.3390/logistics10020036 - 2 Feb 2026

Viewed by 1075

Abstract

Background: Sustainable supply chain development is a global priority driven by resource depletion, socio-economic challenges, and environmental concerns. Existing biofuel supply chain studies, however, often focus on isolated upstream or downstream processes and inadequately address multi-stakeholder engagement. Achieving sustainability requires coordinated participation of [...] Read more.

Background: Sustainable supply chain development is a global priority driven by resource depletion, socio-economic challenges, and environmental concerns. Existing biofuel supply chain studies, however, often focus on isolated upstream or downstream processes and inadequately address multi-stakeholder engagement. Achieving sustainability requires coordinated participation of stakeholders across multiple decision levels, from individuals to society. This study proposes a collaborative framework to support sustainable biofuel development. Methodology: The framework comprises three steps: (i) current-state analysis through stakeholder identification, power–interest mapping, and engagement assessment; (ii) definition of a desired future state; and (iii) development of transition strategies integrating bottom-up and top-down approaches. The framework is applied to a biofuel case study in Ethiopia. Results: Twenty-four stakeholders were identified across nano, micro, meso, and macro levels. Power–interest and engagement analyses revealed key decision-makers and categorized stakeholders as aligned, passive, or militant. The results show that transitioning stakeholders toward active collaboration requires integrated strategies, including capability development, policy alignment, knowledge sharing, and technological advancement. These interventions support coordinated decision-making, improved resource management, and sustainability outcomes such as job creation, energy security, and greenhouse gas reduction. Conclusions: The proposed multi-level interaction framework effectively aligns stakeholders by integrating bottom-up and top-down strategies. It provides a systematic approach to guiding collaborative transitions toward sustainable development. Full article

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26 pages, 5240 KB

Open AccessArticle

Designing Sustainable Healthcare Additive Manufacturing Networks Using a Multi-Objective Spatial Routing Framework

by Kasin Ransikarbum, Chanipa Nivasanon and Pornthep Anussornnitisarn

Logistics 2026, 10(2), 35; https://doi.org/10.3390/logistics10020035 - 2 Feb 2026

Viewed by 769

Abstract

Background: This study evaluates an additive manufacturing (AM) network designed to balance economic performance, lead time, and environmental impact within the healthcare logistics and supply chain. Methods: An integrated framework is proposed that identifies optimal AM facility locations using spatial K-means [...] Read more.

Background: This study evaluates an additive manufacturing (AM) network designed to balance economic performance, lead time, and environmental impact within the healthcare logistics and supply chain. Methods: An integrated framework is proposed that identifies optimal AM facility locations using spatial K-means clustering and optimizes delivery routes through a multi-objective vehicle routing problem with time windows (MOVRPTW). This framework was applied to a case study in Phra Nakhon Si Ayutthaya, Thailand, utilizing hospital geocoordinates, demand profiles, and CO₂ emission factors to evaluate centralized versus decentralized network configurations. Results: Findings demonstrate that hub structures derived from K-means clustering achieve the highest economic efficiency, reducing the AM part cost per unit to 698.51 Baht. In contrast, a fully centralized network resulted in a significantly higher unit cost of 4759.79 Baht, while clustering based on hospital types yielded a unit cost of 959.34 Baht. Quantitative results indicate that the multi-objective approach provides a superior trade-off, achieving lead time requirements while maintaining operational costs and emissions. Conclusions: The results indicate that the proposed framework, particularly through spatial clustering, offers a practical decision-support tool for designing AM networks that achieve a balance between operational efficiency and sustainability objectives in healthcare logistics. Full article

(This article belongs to the Special Issue New Progresses and Main Implications in Additive Manufacturing for Operations and Supply Chain Management)

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22 pages, 559 KB

Open AccessEditor’s ChoiceArticle

Tackling Supply Chain Disruptions Through Digital Agility: Evidence from the Hotel Industry

by Ahmed Mohamed Hasanein, Hazem Ahmed Khairy, Abdulaziz Aljoghaiman and Bassam Samir Al-Romeedy

Logistics 2026, 10(2), 34; https://doi.org/10.3390/logistics10020034 - 2 Feb 2026

Cited by 1 | Viewed by 1689

Abstract

Background: Digital transformation has become a vital driver of competitiveness in the hospitality industry. This study investigates the role of digital agility in enhancing competitive advantage in Egypt’s luxury hotel sector, focusing on the parallel mediating effects of supply chain agility and [...] Read more.

Background: Digital transformation has become a vital driver of competitiveness in the hospitality industry. This study investigates the role of digital agility in enhancing competitive advantage in Egypt’s luxury hotel sector, focusing on the parallel mediating effects of supply chain agility and supply chain resilience. Grounded in the Dynamic Capabilities Theory (DCT), the research explores how digital capabilities promote flexibility, responsiveness, and strategic performance. Methods: Data were collected from 325 senior managers in supply chain, procurement, operations, logistics, and digital transformation across luxury hotels in Egypt. The conceptual framework was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via WarpPLS software. Results: Findings reveal that digital agility significantly enhances competitive advantage, as well as supply chain agility and resilience. Both supply chain agility and resilience positively influence competitive advantage and partially mediate the relationship between digital agility and competitiveness. Conclusions: The study highlights the strategic importance of digital agility in fostering agile and resilient supply chains, which serve as key mechanisms for achieving sustained competitive advantage in the luxury hotel industry. Investing in digital technologies and adaptive capabilities is essential for long-term success in a dynamic market environment. Full article

(This article belongs to the Special Issue Tackling Disruptions in Supply Chain Networks Through Resilient, Sustainable and Innovative Methods and Practices)

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23 pages, 3417 KB

Open AccessArticle

Automatic Inventory of Wiring Harness Components Using UHF RFID Technology

by Ioana Iorga, Cicerone Laurentiu Popa, Constantin-Adrian Popescu, Florina Chiscop, Tiberiu Gabriel Dobrescu and Costel Emil Cotet

Logistics 2026, 10(2), 33; https://doi.org/10.3390/logistics10020033 - 2 Feb 2026

Viewed by 1035

Abstract

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic [...] Read more.

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics. Full article

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19 pages, 1214 KB

Open AccessEditor’s ChoiceArticle

The Impact of Digital Transformation on the Business Performance of Logistics Enterprises: A Multi-Criteria Approach

by Khanh Han Nguyen and Long Quang Pham

Logistics 2026, 10(2), 32; https://doi.org/10.3390/logistics10020032 - 26 Jan 2026

Viewed by 2119

Abstract

Background: In the era of rapid technological advancement, digital transformation has emerged as a pivotal strategy for enhancing operational efficiency and competitiveness in logistics enterprises, particularly amid globalization and post pandemic recovery; this study aims to evaluate its multifaceted impact on business [...] Read more.

Background: In the era of rapid technological advancement, digital transformation has emerged as a pivotal strategy for enhancing operational efficiency and competitiveness in logistics enterprises, particularly amid globalization and post pandemic recovery; this study aims to evaluate its multifaceted impact on business performance using a multi-criteria framework focused on Vietnamese firms. Methods: Employing structural equation modeling on primary survey data from 346 middle and senior level managers, alongside the Malmquist productivity index derived from data envelopment analysis on secondary financial indicators spanning 2020 to 2024, the research integrates latent variables such as organizational capability, technological innovation capability, institutional pressure, digital transformation, and business performance. Results: Key findings reveal a strong positive correlation between technological innovation capability and organizational capability (path coefficient 0.522), with organizational capability directly influencing business performance (0.359), while institutional pressure positively affects digital transformation (0.321) but negatively impacts business performance (−0.152); overall, digital transformation exhibits limited optimization, contributing to modest productivity gains and a potential 23% cost reduction through technologies like Internet of Things and artificial intelligence. Conclusions: These results underscore the necessity for logistics enterprises to strengthen organizational integration and training to maximize digital transformation benefits, thereby fostering sustainable competitiveness in global supply chains. Full article

(This article belongs to the Special Issue Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition)

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26 pages, 1596 KB

Open AccessArticle

Technological Pathways to Low-Carbon Supply Chains: Evaluating the Decarbonization Impact of AI and Robotics

by Mariem Mrad, Mohamed Amine Frikha, Younes Boujelbene and Mohieddine Rahmouni

Logistics 2026, 10(2), 31; https://doi.org/10.3390/logistics10020031 - 26 Jan 2026

Viewed by 1279

Abstract

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. [...] Read more.

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

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29 pages, 2594 KB

Open AccessArticle

The Value Addition of Healthcare 4.0 Loyalty Programs: Implications for Logistics Management

by Maria João Vieira, Ana Luísa Ramos and João Amaral

Logistics 2026, 10(2), 30; https://doi.org/10.3390/logistics10020030 - 26 Jan 2026

Viewed by 1020

Abstract

Background: Digital transformation is reshaping healthcare operations, with loyalty programs increasingly used to strengthen patient engagement and streamline administrative workflows. However, fragmented information systems and manual verification routines continue to create bottlenecks, inconsistencies, and extended lead times. Methods: This study applies [...] Read more.

Background: Digital transformation is reshaping healthcare operations, with loyalty programs increasingly used to strengthen patient engagement and streamline administrative workflows. However, fragmented information systems and manual verification routines continue to create bottlenecks, inconsistencies, and extended lead times. Methods: This study applies a mixed-methods approach within the Business Process Management (BPM) lifecycle to redesign the eligibility verification process for a loyalty program at Casa de Saúde São Mateus Hospital. Quantitative time measurements were collected during peak periods, while qualitative insights from staff observations and discussions supported process discovery and bottleneck identification. The proposed solution integrates a centralized SQL database, automated verification routines, and a dedicated administrative interface synchronized with the MedicineOne system. Results: The redesigned process reduced eligibility verification time by approximately 80% and improved Flow Efficiency by around 11.7%. Manual interventions, data fragmentation, and discount-application errors decreased substantially. The centralized database improved data reliability, while automated checks enhanced consistency and reduced staff workload. The system also enabled more accurate beneficiary management and improved coordination across administrative activities. Conclusions: Integrating Healthcare 4.0 principles with BPM enhances internal logistics, reduces lead times, and improves operational reliability. The proposed model offers a replicable framework for modernizing healthcare service delivery. Full article

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

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21 pages, 846 KB

Open AccessSystematic Review

Operational AI for Multimodal Urban Transport: A Systematic Literature Review and Deployment Framework for Multi-Objective Control and Electrification

by Alexandros Deligiannis and Michael Madas

Logistics 2026, 10(2), 29; https://doi.org/10.3390/logistics10020029 - 23 Jan 2026

Cited by 1 | Viewed by 1576

Abstract

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links [...] Read more.

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

(This article belongs to the Special Issue Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition)

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Graphical abstract

21 pages, 6553 KB

Open AccessArticle

Analyzing Key Factors for Warehouse UAV Integration Through Complex Network Modeling

by Chommaphat Malang and Ratapol Wudhikarn

Logistics 2026, 10(2), 28; https://doi.org/10.3390/logistics10020028 - 23 Jan 2026

Viewed by 888

Abstract

Background: The integration of unmanned aerial vehicles (UAVs) into warehouse management is shaped by a broad spectrum of influencing factors, yet practical adoption lagged behind its potential due to scarce quantitative models of factor interdependencies. Methods: This study systematically reviewed academic [...] Read more.

Background: The integration of unmanned aerial vehicles (UAVs) into warehouse management is shaped by a broad spectrum of influencing factors, yet practical adoption lagged behind its potential due to scarce quantitative models of factor interdependencies. Methods: This study systematically reviewed academic literature to identify key factors affecting UAV adoption and explored their interrelationships using complex network and social network analysis. Results: Sixty-six distinct factors were identified and mapped into a weighted network with 527 connections, highlighting the multifaceted nature of UAV integration. Notably, two factors, i.e., Disturbance Prediction and System Resilience, were found to be isolated, suggesting they have received little research attention. The overall network is characterized by low density but includes a set of 25 core factors that strongly influence the system. Significant interconnections were uncovered among factors such as drone design, societal factors, rack characteristics, environmental influences, and simulation software. Conclusions: These findings provide a comprehensive understanding of the dynamics shaping UAV adoption in warehouse management. Furthermore, the open-access dataset and network model developed in this research offer valuable resources to support future studies and practical decision-making in the field. Full article

(This article belongs to the Topic Decision Science Applications and Models (DSAM))

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25 pages, 1249 KB

Open AccessArticle

An Adaptive Fuzzy Multi-Objective Digital Twin Framework for Multi-Depot Cold-Chain Vehicle Routing in Agri-Biotech Supply Networks

by Hamed Nozari and Zornitsa Yordanova

Logistics 2026, 10(2), 27; https://doi.org/10.3390/logistics10020027 - 23 Jan 2026

Cited by 3 | Viewed by 1051

Abstract

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated [...] Read more.

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

(This article belongs to the Special Issue Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition)

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23 pages, 1191 KB

Open AccessArticle

Smart Port and Digital Transition: A Theory- and Experience-Based Roadmap

by Basma Belmoukari, Jean-François Audy, Pascal Forget and Vicky Adam

Logistics 2026, 10(2), 26; https://doi.org/10.3390/logistics10020026 - 23 Jan 2026

Viewed by 2128

Abstract

Background: Port digital transition is central to competitiveness and sustainability, yet existing frameworks devoted to such transition toward smart port are descriptive, technology-centered, or weak on data governance. This study designs and empirically refines a comprehensive and novel ten-step roadmap relative to [...] Read more.

Background: Port digital transition is central to competitiveness and sustainability, yet existing frameworks devoted to such transition toward smart port are descriptive, technology-centered, or weak on data governance. This study designs and empirically refines a comprehensive and novel ten-step roadmap relative to existing Port/Industry 4.0 models, synthesized from 14 partial frameworks that each cover only subsets of the transition, by considering data governance and consolidating cost, time, and impact in the selection step. Methods: We synthesized recent Industry 4.0 and smart port-related frameworks into a normalized sequence of steps embedded in the so-called roadmap, then examined it in an exploratory case of a technology deployment project in a Canadian port using stakeholder interviews and project documents. Evidence was coded with a step-aligned scheme, and stakeholder feedback and implementation observations assessed whether each step’s outcomes were met. Results: The sequence proved useful yet revealed four recurrent hurdles: limited maturity assessment, uneven stakeholder engagement, ad hoc technology selection and integration, and under-specified data governance. The refined roadmap adds a diagnostic maturity step with target-state setting and gap analysis, a criteria-based selection worksheet, staged deployment with checkpoints, and compact indicators of transformation performance, such as reduced logistics delays, improved energy efficiency, and technology adoption. Conclusions: The work couples theory-grounded synthesis with empirical validation and provides decision support to both ports and public authorities to prioritize investments, align stakeholders, propose successful policies and digitalization supporting programs, and monitor outcomes, while specifying reusable steps and indicators for multi-port testing and standardized metrics. Full article

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22 pages, 700 KB

Open AccessArticle

A Conceptual Framework for Evaluating Green Logistics Practices Through Multi-Criteria Decision-Making Methods

by Laura Jefimovaitė and Milita Vienažindienė

Logistics 2026, 10(2), 25; https://doi.org/10.3390/logistics10020025 - 23 Jan 2026

Viewed by 1845

Abstract

Background: Green logistics practices are crucial for achieving the EU’s Green Deal objectives, addressing environmental challenges, improving supply chain efficiency, and fostering business sustainability. This paper presents a conceptual framework for green logistics practices and their application for ensuring sustainable organisational development. Methods: [...] Read more.

Background: Green logistics practices are crucial for achieving the EU’s Green Deal objectives, addressing environmental challenges, improving supply chain efficiency, and fostering business sustainability. This paper presents a conceptual framework for green logistics practices and their application for ensuring sustainable organisational development. Methods: Using the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methodologies, this study assesses the importance of green logistics practices in Lithuanian SMEs and their future application. The AHP method facilitates pairwise comparisons to determine the weights of green logistics criteria, while the SAW method evaluates the final sub-criteria by aggregating normalized scores according to the identified weights. Results: A survey of ten companies revealed that green transportation is the most developed green logistics practice, with the focus on infrastructure, skills and transport optimisation. Green warehousing is the second most significant practice, with SMEs considering it vital to green logistics because of its sustainable warehousing measures. Green packaging is considered third in terms of importance, due to the attention paid to the packaging materials used. Conclusions: The full potential of green logistics has yet to be realised. Adopting a more balanced approach could enhance environmental outcomes and bolster the resilience of the long-term supply chain. Full article

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Logistics, Volume 10, Issue 2 (February 2026) – 27 articles

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