Modeling and Simulation Innovations for Advanced Logistics Systems Management

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 752

Special Issue Editors


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Guest Editor
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Hum 999077, Hong Kong
Interests: maritime cluster analysis; investment decision-making and maritime behavioral analytics

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Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639789, Singapore
Interests: renewable energy and sustainability; industrial engineering and design; machine-person interoperability; supply chain management; system resilience
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Special Issue Information

Dear Colleagues,

Modern logistics systems have become complex, highly integrated networks requiring sophisticated management strategies to ensure seamless supply chain operations. Modeling and simulation techniques are vital for optimizing logistics systems, enabling better decision-making, efficiency, and resilience in an ever-evolving market. With the rapid advancement of digital twins, predictive analytics, and agent-based modeling technologies, logistics systems managers can now proactively address challenges like demand fluctuations, route optimization, and risk management.

This Special Issue will explore the latest advances in modeling and simulation for logistics systems management, emphasizing applications that enhance adaptability, sustainability, and strategic planning. We welcome contributions that examine innovative methodologies, case studies on real-world implementations, and research tackling the unique logistics modeling challenges. The goal is to provide theoretical insights and practical solutions that can help shape the future of logistics systems management.

Dr. Yusheng Zhou
Dr. Kum Fai Yuen
Guest Editors

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Keywords

  • logistics systems modeling
  • supply chain simulation
  • decision support for logistics systems
  • predictive analytics in logistics
  • risk management in logistics

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Published Papers (1 paper)

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Research

22 pages, 525 KiB  
Article
Growth Mechanism in Transformation and Upgrading of Logistics Industry
by Fangzhou Li, Xiaojia Yang, Ruili Zhu, Tao Li and Jingyi Liu
Systems 2025, 13(3), 202; https://doi.org/10.3390/systems13030202 - 15 Mar 2025
Viewed by 424
Abstract
Despite the crucial contribution of the logistics industry to economic development, existing research has yet to comprehensively explore how the integration of basic and emerging business models fuels growth during the transformation and upgrading process. To address this research gap, this study utilizes [...] Read more.
Despite the crucial contribution of the logistics industry to economic development, existing research has yet to comprehensively explore how the integration of basic and emerging business models fuels growth during the transformation and upgrading process. To address this research gap, this study utilizes provincial panel data from 30 regions covering the period from 2008 to 2022. By employing an intermediary effect model and a moderation effect model, we aim to uncover the underlying mechanisms driving growth. The findings reveal that the logistics industry can be categorized into traditional and emerging logistics elements, with the integration of traditional elements forming the fundamental business model. This foundational model serves as the primary driver of the logistics industry’s growth, exerting both direct and indirect influences on its expansion. Moreover, the level of economic development positively moderates these direct and indirect effects. These insights underscore the importance of enhancing infrastructure development, fostering business innovation, and promoting region-specific differentiated growth strategies. Full article
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