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Application of Data-Driven in Sustainable Logistics and Supply Chain

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 4229

Special Issue Editor

College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Chengdu 611830, China
Interests: supply chain management; sustainable construction management; data-driven operations management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the present global landscape, where environmental pollution, short-term resource availability, and slow economic development represent increasingly severe conflicts, the field of logistics and supply chain management is facing unprecedented challenges. As such, the execution of a sustainable development model for logistics and supply chains has emerged as a shared international goal.

Data-driven technologies play a crucial role in advancing sustainable logistics and supply chains. By collecting and analyzing big data, companies can enhance their ability to respond to market fluctuations, optimize supply chain collaboration, streamline operational efficiency, and achieve cost reduction. For example, by using big data and artificial intelligence technology, companies can analyze logistics paths and find optimal solutions for transportation routes; by integrating data from all parts of the supply chain, an intelligent decision-making system can be established; by analyzing historical data, potential supply chain risks can be predicted and assessed, and contingency plans can be generated. Despite the huge scope of data-driven technologies available, there have been few attempts to apply them to sustainable logistics and supply chains.

Therefore, this Special Issue aims to explore in depth the application of data-driven technologies in sustainable logistics and supply chains. Technologies, such as the Internet of Things, blockchains, and big data technologies, are redefining how logistics and supply chain businesses operate. As the field continues to evolve, it is essential that researchers and practitioners stay abreast of the latest developments and contribute to the growing knowledge of data-driven applications in logistics and supply chains.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Sustainable logistics;
  • Data-driven supply chain management;
  • Design and application of artificial intelligence, big data, and Internet of Things in supply chain optimization;
  • Data-driven forecasting and analysis;
  • A data-driven approach to sustainable supply chains;
  • Big data analytics in logistics and supply chain;
  • Data-driven design and optimization;
  • Data-driven impact on all aspects of social value in logistics and supply chain management.

I look forward to receiving your submissions.

Dr. Wen Jiang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable logistic
  • sustainable inventory management
  • logistics route optimization
  • data-driven technology
  • operation management and optimization
  • sustainable supply chain
  • supply chain coordination
  • sustainable supply chain network design
  • supply chain visualization
  • carbon emission reduction strategy

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Published Papers (3 papers)

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Research

17 pages, 2351 KiB  
Article
Future Rail Signaling: Cyber and Energy Resilience Through AI Interoperability
by Pavlo Holoborodko, Darius Bazaras and Nijolė Batarlienė
Sustainability 2025, 17(10), 4643; https://doi.org/10.3390/su17104643 - 19 May 2025
Viewed by 763
Abstract
In today’s world, everything changes at lightning speed, making what is relevant today potentially obsolete tomorrow. This author’s scientific article addresses the issues of energy resilience and cybersecurity in railway signaling. A new proposal based on artificial intelligence is made to improve the [...] Read more.
In today’s world, everything changes at lightning speed, making what is relevant today potentially obsolete tomorrow. This author’s scientific article addresses the issues of energy resilience and cybersecurity in railway signaling. A new proposal based on artificial intelligence is made to improve the fault tolerance of rail transport signaling infrastructure by ensuring increased energy efficiency and detecting cyber-attacks in real time. A linearly coupled neural network model was designed and implemented in a railway signaling simulation to simultaneously track the energy characteristics of signaling and detect abnormal behavior. The authors’ model was validated based on MATLAB(24.2.0.2863752 (R2024b) Update 5) simulations of a real double-track railway line under normal operating conditions and in a ransomware cyber-attack scenario. The AI simulation model correctly predicted the resilience of the signaling system, achieving an average absolute error of 0.0331 in predicting the fundamental performance indicator, and successfully identified an upcoming cyber-attack 20 min before the incident. This study demonstrates the promising architecture of the AI-based signaling system, which provides a significant increase in resilience to emergency situations in relation to power supply and cyber-attacks. By optimizing the signaling infrastructure with AI, it is possible to ensure safe and continuous movement of trains, including emergency situations, representing a promising approach to improving the resilience and safety of railways. Full article
(This article belongs to the Special Issue Application of Data-Driven in Sustainable Logistics and Supply Chain)
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55 pages, 482 KiB  
Article
A Practical and Sustainable Approach to Industrial Engineering Discrete-Event Simulation with Free Mathematical and Programming Software
by Jérémie Schutz, Christophe Sauvey, Eduard Laurențiu Nițu and Ana Cornelia Gavriluță
Sustainability 2025, 17(9), 3973; https://doi.org/10.3390/su17093973 - 28 Apr 2025
Viewed by 1122
Abstract
Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to [...] Read more.
Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world. Full article
(This article belongs to the Special Issue Application of Data-Driven in Sustainable Logistics and Supply Chain)
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29 pages, 12354 KiB  
Article
Data-Driven Order Consolidation with Vehicle Routing Optimization
by Changhee Yang, Yongjin Lee and Chulung Lee
Sustainability 2025, 17(3), 848; https://doi.org/10.3390/su17030848 - 22 Jan 2025
Viewed by 1610
Abstract
This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. [...] Read more.
This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear programming (MILP) model, sensitivity analyses, and scenario-based experiments, the study demonstrates that the time-based model excels in stable conditions, while the quantity-based model performs better in highly variable demand scenarios. These findings provide actionable insights for selecting consolidation strategies that optimize delivery operations and enhance supply chain performance based on market dynamics. Full article
(This article belongs to the Special Issue Application of Data-Driven in Sustainable Logistics and Supply Chain)
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