Optimization and Mathematical Modelling in Transport and Logistics Network

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 795

Special Issue Editors


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Guest Editor
School of Economics and Management, Chang'an University, Xi’an 710064, China
Interests: logistics engineering; information technology and data processing; construction management
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Guest Editor
Lazaridis School of Business & Economics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
Interests: supply chain management; management science; inventory management; operation research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transport and logistics networks are fundamental to modern economies, facilitating the efficient movement of goods and people across diverse regions. However, these networks face increasing pressure from growing demand, rapid urbanization, and environmental constraints. The need for a green and smart transformation of these systems has been urgent.

Achieving this transformation requires addressing the management challenges in the planning, location, and construction of transport infrastructure, as well as optimizing interconnected transport and logistics networks.

Optimization and mathematical modeling offer tools to address these issues, providing innovative solutions for the design, management, and improvement of transport and logistics networks. The aim of this Special Issue is to collect articles with innovative methods and unique perspectives in this field.

Topics of interest for this Special Issue include, but are not limited to, the following:

Logistics project management;

Low-carbon transportation and logistics;

Intelligent transportation and logistics management;

Dynamic and stochastic modeling;

Supply chain network design and optimization;

Data-driven and machine learning approaches;

Resilience and risk management.

We encourage submissions that provide novel insights, real-world applications, or comparative analyses of existing methods. Both theoretical and empirical studies are welcome, as are interdisciplinary works combining mathematics, operations research, and computer science.

Prof. Dr. Libiao Bai
Prof. Dr. Victor Shi
Guest Editors

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Keywords

  • low-carbon management
  • sustainable logistics
  • transport infrastructure
  • network optimization
  • risk management
  • data-driven models
  • dynamic modeling

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Published Papers (1 paper)

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Research

18 pages, 1929 KiB  
Article
Low-Carbon Transport for Prefabricated Buildings: Optimizing Capacitated Truck–Trailer Routing Problem with Time Windows
by Jiajie Zhou, Qiang Du, Qian Chen, Zhongnan Ye, Libiao Bai and Yi Li
Mathematics 2025, 13(7), 1210; https://doi.org/10.3390/math13071210 - 7 Apr 2025
Viewed by 266
Abstract
The transportation of prefabricated components is challenged by the particularity of large cargo transport and urban road conditions, restrictions on parking, height, and weight. To address these challenges and to promote low-carbon logistics, this paper investigates the transportation of prefabricated components by leveraging [...] Read more.
The transportation of prefabricated components is challenged by the particularity of large cargo transport and urban road conditions, restrictions on parking, height, and weight. To address these challenges and to promote low-carbon logistics, this paper investigates the transportation of prefabricated components by leveraging separable fleets of trucks and trailers. Focusing on real-world constraints, this paper formulates the capacitated truck and trailer routing problem with time windows (CTTRPTW) incorporating carbon emissions, and designs a dynamic adaptive hybrid algorithm combining simulated annealing with tabu search (DASA-TS) to solve this model. The efficiency and robustness of the methodology are validated through two computational experiments. The results indicate that the DASA-TS consistently demonstrates excellent performance across all evaluations, with significant reductions in both transportation costs and carbon emissions costs for prefabricated components, particularly in large-scale computational instances. This study contributes to promoting the optimization of low-carbon transport for prefabricated components, offering guidance for routing design involving complex and large cargo, and supporting the sustainable development of urban logistics. Full article
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