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Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition

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A special issue of Logistics (ISSN 2305-6290).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 20017

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Dr. Mladen Krstić

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Logistics Department, Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia
Interests: logistics; supply chain; intermodal transport; logistics centers; dry ports; humanitarian logistics; e-commerce logistics; multi-criteria decision-making
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Dr. Željko Stević

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Faculty of Transport and Traffic Engineering, University of East Sarajevo, 74000 Doboj, Bosnia and Herzegovina
Interests: logistics; transport; supply chain; multi-criteria decision making; sustainable engineering; fuzzy set; rough set; transport of dangerous goods
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Dr. Snežana Tadić

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Logistics Department, Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia
Interests: logistics; supply chain; intermodal transport; logistics centers; city logistics; dry ports; humanitarian logistics; e-commerce logistics; multi-criteria decision-making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past, when globalization was still in its initial stages of development, market competition was less intense, rendering logistics operations much simpler and more primitive. The completion of activities with the lowest possible logistics costs or delivery time was emphasized as the primary goal (Ho et al., 2010). However, today logistics are faced with significantly more complex challenges that arise as a result of the efforts of the economy and society to fit into the requirements of the trends in modern logistics. On the one hand, there are increasingly strict requirements regarding the quality of logistics services which arise as a result of accelerated globalization (Bykova et al., 2021), the growth of competition (Barker et al., 2021), and the development of e-commerce (Cao et al., 2021). These have helped to generate an increased volume of global commodity and transport flows. On the other hand, trends such as digitization (de Andres Gonzalez et al., 2021) and the application of Industry 4.0 and 5.0 technologies in logistics (Jafari et al., 2022), improvements to sustainability via the principles of the triple bottom line (economic, environmental, and social sustainability) (Khan et al., 2022) and the circular economy (Mishra et al., 2022), the growth in resilience of supply chains in response to global challenges (wars and pandemics, such as COVID-19) (Mena et al., 2022 ), etc., contribute to the growing complexity of goods and transport flows. Solutions for the abovementioned problems should be sought in the development and application of new technologies, the networking of technologies, and the design and deployment of new strategies, concepts, initiatives, measures, etc., for solving institutional, legal, organizational, and technological problems. In addition, efforts should be made for the education of all interest groups, i.e., participants in logistics chains who may participate in creating sustainable smart logistics solutions. Smart logistics refers to the use of advanced technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data analytics to optimize and streamline supply chain and transportation processes. The goal of smart logistics is to increase efficiency, reduce costs, improve customer satisfaction, and enhance overall supply chain visibility and control. As a practice, it involves real-time monitoring and control of supply chain operations, predictive analysis for optimizing routes and schedules, and automating many manual tasks for improved accuracy and speed.

Since these problems are complex and often include multiple alternatives, criteria, and stakeholders, they could be solved only by applying different techniques and methods of operational research (OR). Among potential methods, the multi-criteria decision-making (MCDM) methods stand out. MCDM is one of the most important and fastest-growing sub-fields of OR. Accordingly, the aim of this Special Issue is to define innovative solutions in the field of MCDM and outline their applications in achieving sustainable smart logistics solutions. If successful, we will contribute to the literature and practice by defining new MCDM methodologies and models for solving real-life problems of contemporary logistics. Although articles should deal primarily with expanding the body of literature primarily in the area of logistics, submissions are also welcome in the areas of OR, MCDM, Industry 4.0, or any of the following topics:

Applications of existing and developments of new MCDM methods for solving various problems in sustainable smart logistics.
Development of hybrid MCDM models in the conventional environment or the environment of intuitionistic and interval sets for solving various problems in sustainable smart logistics.
Development of optimization models for various problems in sustainable smart logistics.
Applications of heuristics and metaheuristics for solving various problems in sustainable smart logistics.
Applications of MCDM methods for sustainable transportation planning and routing.
Integration of sustainability criteria into MCDM models for smart logistics.
Analysis of the trade-off between economic efficiency and environmental sustainability in smart logistics.
Development of novel MCDM methods for sustainable smart logistics.
Evaluating the impact of smart logistics on supply chain sustainability.
Case studies of the implementation of MCDM methods in smart logistics.
Comparison of different MCDM methods for sustainable smart logistics.
The role of big data and machine learning in sustainable smart logistics decision-making.
Optimization of energy consumption in smart logistics using MCDM methods.
Sustainable smart logistics in urban areas: challenges and opportunities.

Keywords

contemporary logistics
sustainability
Industry 4.0
circular economy
operartions research
optimization
decision making
multi-criteria analysis
expert systems
heuristics, metaheuristics

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Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics in Logistics (25 articles)

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22 pages, 3840 KB

Open AccessArticle

An Integrated Vision–Mobile Fusion Framework for Real-Time Smart Parking Navigation

by Oleksandr Laptiev, Ananthakrishnan Thuruthel Murali, Nathalie Saab, Nihad Soltanov and Agnė Paulauskaitė-Tarasevičienė

Logistics 2026, 10(4), 84; https://doi.org/10.3390/logistics10040084 - 9 Apr 2026

Viewed by 409

Abstract

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. 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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22 pages, 1332 KB

Open AccessArticle

Identifying Barriers to Shipbuilding in India: A Delphi–DEMATEL Approach

by Rupesh Kumar and Saroj Koul

Logistics 2026, 10(4), 80; https://doi.org/10.3390/logistics10040080 - 3 Apr 2026

Viewed by 392

Abstract

Background: This study examines the systemic barriers constraining the development of India’s shipbuilding industry and identifies leverage points for effective policy intervention. Methods: A mixed-methods design was adopted, combining the Delphi technique with fuzzy DEMATEL to capture expert consensus and causal interdependencies among barriers. A panel of 20 experts, drawn from academia, the government, shipbuilding and ship repair, ports, logistics, and maritime consultancy, participated in two iterative Delphi rounds. An initial list of 21 barriers was refined to 10 based on convergence thresholds. These barriers were then analysed using a seven-step fuzzy DEMATEL procedure to distinguish causal drivers from dependent factors. Results: High raw material costs emerged as the most dominant causal barrier, with the highest net influence (R−C = 0.540), followed by high working capital requirements (R−C = 0.103) and complex regulatory frameworks (R−C = 0.275). Shortages of skilled labour, inefficiencies in ship design, and delays in clearances were largely effect-type barriers shaped by upstream structural conditions. Sensitivity analysis confirmed the stability of barrier rankings under alternative expert weighting scenarios. Conclusions: Policy efforts should prioritise reducing input cost disadvantages, strengthening long-term policy support, and rationalising regulatory processes, rather than focusing solely on downstream operational symptoms. The study is limited to expert judgement in the Indian shipbuilding sector. Future research could extend this framework to comparative country settings or integrate causal analysis with econometric evidence to further strengthen policy design. Contribution: Unlike prior thematic studies, this research provides an integrated causal mapping of structural, financial, and institutional barriers specific to Indian shipbuilding, enabling policy sequencing rather than simple ranking. 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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27 pages, 3523 KB

Open AccessArticle

Optimizing Inventory in Convenience Stores to Maximize ROI Using Random Forest and Genetic Algorithms

by Kelly Zavaleta-Zarate, Jesus Escobal-Vera and Eliseo Zarate-Perez

Logistics 2026, 10(3), 64; https://doi.org/10.3390/logistics10030064 - 13 Mar 2026

Viewed by 724

Abstract

Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics. 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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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 2 | Viewed by 885

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 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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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 1456

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 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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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 1 | Viewed by 785

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 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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18 pages, 380 KB

Open AccessArticle

Scheduling Jobs on Unreliable Machines Subject to Linear Risk

by Alessandro Agnetis and Ilaria Salvadori

Logistics 2025, 9(4), 157; https://doi.org/10.3390/logistics9040157 - 4 Nov 2025

Viewed by 797

Abstract

Background: This paper addresses a new class of scheduling problems in the context of machines subject to (unrecoverable) interruptions; i.e., when a machine fails, the current and subsequently scheduled work on that machine is lost. Each job has a certain processing time and a reward that is attained if the job is successfully completed. Methods: For the failure process, we considered the linear risk model, according to which the probability of machine failure is uniform across a certain time horizon. Results: We analyzed both the situation in which the set of jobs is given, and that in which jobs must be selected from a pool of jobs, at a certain selection cost. Conclusions: We characterized the complexity of various problems, showing both hardness results and polynomial algorithms, and pointed out some open problems. 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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22 pages, 826 KB

Open AccessArticle

Integrating Machine Learning with Multi-Criteria Decision-Making Models for Sustainable Supplier Selection in Dynamic Supply Chains

by Osheyor Joachim Gidiagba, Lagouge Tartibu and Modestus Okwu

Logistics 2025, 9(4), 152; https://doi.org/10.3390/logistics9040152 - 24 Oct 2025

Cited by 7 | Viewed by 4368

Abstract

Background: Supplier evaluation and selection are pivotal processes in supply chain management, profoundly influencing organisational efficiency and sustainability. This study addresses the limitations of traditional multi-criteria decision-making approaches, particularly the Technique for Order Preference by Similarity to an Ideal Solution, which often lacks dimensional reduction capability and assumes uniform weight distribution across criteria. Methods: To overcome these challenges, a hybrid model integrating non-negative matrix factorisation, random forest, and the Technique for Order Preference by Similarity to an Ideal Solution is developed for supplier evaluation in the pharmaceutical sector. The method first applies non-negative matrix factorisation to condense twenty-four evaluation criteria into eight core dimensions, enhancing analytical efficiency and reducing complexity. Random forest is then employed to derive data-driven weights for each criterion, ensuring accurate prioritisation. Finally, the Technique for Order Preference by Similarity to an Ideal Solution ranks suppliers and provides actionable insights for decision-makers. Results: Results from real-world pharmaceutical data validate the model’s effectiveness and demonstrate superior performance over conventional evaluation methods. Conclusions: The findings confirm that integrating machine learning techniques with established decision-making frameworks enhances precision, interpretability, and sustainability in supplier selection while requiring adequate data quality and computational resources for implementation. 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, 505 KB

Open AccessArticle

Cost Modeling for Pickup and Delivery Outsourcing in CEP Operations: A Multidimensional Approach

by Ermin Muharemović, Amel Kosovac, Muhamed Begović, Snežana Tadić and Mladen Krstić

Logistics 2025, 9(3), 96; https://doi.org/10.3390/logistics9030096 - 17 Jul 2025

Viewed by 2088

Abstract

Background: The growth of parcel volumes in urban areas, largely driven by e-commerce, has increased the complexity of pickup and delivery operations. To meet demands for cost efficiency, flexibility, and sustainability, CEP (Courier, Express, and Parcel) operators increasingly outsource segments of their last-mile networks. Methods: This study proposes a novel multidimensional cost model for outsourcing, integrating five key variables: transport unit type (parcel/pallet), service phase (pickup/delivery), vehicle category, powertrain type, and delivery point type. The model applies correction coefficients based on internal operational costs, further adjusted for location and service quality using a bonus/malus mechanism. Results: Each cost component is calculated independently, enabling full transparency and route-level cost tracking. A real-world case study was conducted using operational data from a CEP operator in Bosnia and Herzegovina. The model demonstrated improved accuracy and fairness in cost allocation, with measurable savings of up to 7% compared to existing fixed-price models. Conclusions: The proposed model supports data-driven outsourcing decisions, allows tailored cost structuring based on operational realities, and aligns with sustainable last-mile delivery strategies. It offers a scalable and adaptable tool for CEP operators seeking to enhance cost control and service efficiency in complex urban environments. 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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17 pages, 682 KB

Open AccessArticle

The Role of Walkability in Shaping Shopping and Delivery Services: Insights into E-Consumer Behavior

by Leise Kelli de Oliveira, Rui Colaço, Gracielle Gonçalves Ferreira de Araújo and João de Abreu e Silva

Logistics 2025, 9(3), 88; https://doi.org/10.3390/logistics9030088 - 1 Jul 2025

Viewed by 2355

Abstract

Background: As e-commerce expands and delivery services diversifies, understanding the factors that shape consumer preferences becomes critical to designing efficient and sustainable urban logistics. This study examines how perceived walkability influences consumers’ preferences for shopping channels (in-store or online) and delivery methods (home delivery versus pickup points). Method: The analysis is based on structural equation modeling and utilizes survey data collected from 444 residents of Belo Horizonte, Brazil. Results: The findings emphasize the importance of walkability in supporting weekday store visits, encouraging pickup for online purchases and fostering complementarity between different modes of purchase and delivery services. Perceived walkability positively affects the preference to buy in physical stores and increases the likelihood of using pickup points. Educated men, particularly those living in walkable areas, are the most likely to adopt pickup services. In contrast, affluent individuals and women are less likely to forgo home delivery in favor of pickup points. Conclusions: The results highlight the role of perceived walkability in encouraging in-person pickup as a sustainable alternative to home delivery, providing practical guidance for retailers, urban planners, and logistics firms seeking to align consumer convenience with sustainable delivery strategies. 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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19 pages, 529 KB

Open AccessArticle

Mapping Decision-Making Structures in Supply Chain Contexts: A Fuzzy DEMATEL Approach

by Claudemir Leif Tramarico, Aneirson Francisco Da Silva and José Eduardo Holler Branco

Logistics 2025, 9(2), 76; https://doi.org/10.3390/logistics9020076 - 16 Jun 2025

Cited by 3 | Viewed by 3502

Abstract

Background: Effective decision-making in supply chain contexts requires understanding how criteria interact to shape rational and transparent decision structures. This study investigates how behavioral aspects influence the structuring of decision-making logic and the interdependencies between key criteria in supply chain contexts. Methods: Using Fuzzy DEMATEL, the research models the interactions between five core criteria —classification, definition, specification, decision, and action feedback—based on inputs from experienced professionals in a global chemical company. The approach enables mapping of causal influences while accounting for subjectivity and uncertainty in expert judgments. Results: The analysis identified specification, definition, and action feedback as causal criteria, with classification and decision being primarily influenced by them. The modeling process supported clearer prioritization and revealed how expert-based interactions can reduce decision biases. Conclusions: This study demonstrates how structuring decision-making logic through causal modeling enhances clarity and reduces subjectivity. The findings contribute to the development of decision support tools applicable across strategic supply chain contexts, offering practical implications for professionals seeking to improve decision transparency and effectiveness. 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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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

Viewed by 1109

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 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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