View in EDS

Tactical and operational planning of resilient multimodal dry port transportation network.

Saved in:
Bibliographic Details
Title: Tactical and operational planning of resilient multimodal dry port transportation network.
Authors: Zhang, Xinming1,2 (AUTHOR) zhangxinming@zwu.edu.cn, Guo, Gaicong3,4 (AUTHOR) Gaicong.Guo@nottingham.edu.cn, Irawan, Chandra Ade3,4 (AUTHOR) Chandra.Irawan@nottingham.edu.cn, Chan, Hing Kai3,5 (AUTHOR) hingkaichan@wku.edu.cn, Zeng, Fangli1,2 (AUTHOR) fangli.zeng@zwu.edu.cn, Gu, Xinbing3,6 (AUTHOR) Xinbing.Gu@nottingham.edu.cn
Source: Industrial Management & Data Systems. 2026, Vol. 126 Issue 1, p66-96. 31p.
Subjects: Stochastic programming, Intermodal freight terminals, Inventory control, Supply chain management, Ecological resilience, Choice of transportation, Containerization, Network analysis (Planning)
Abstract: Purpose: This paper investigates the complexities of a multimodal dry port transportation network, operating under the challenging conditions posed by the availability of transportation modes. Design/methodology/approach: Our study focuses on a network structure that includes multiple foreign seaports, local seaports, dry ports and manufacturers. We identify a significant research gap in the existing literature on dry port–based multimodal transportation networks. We first develop a mixed-integer non-linear programming (MINLP) model that adeptly integrates freight departure and demand scheduling, inventory management and backlog, while considering the availability of transportation modes in every node. To account for disruptions from uncertain events like extreme weather, we extend this deterministic foundation to a two-stage stochastic programming model. This stochastic model explicitly incorporates uncertainty in transportation mode availability, allowing proactive and adaptive strategies. Findings: The computational study, conducted across two distinct setups, demonstrates our models effectively avoiding substantial backlog penalties as well as delivering robust, cost-balanced solutions to handle uncertainty and complexity. In conclusion, the paper presents a comprehensive analysis including sensitivity analysis, dynamic planning evaluation and cross-regional validation, providing insightful guidance for strategic decision-making and demonstrating the model's adaptability across different operational contexts. Originality/value: The insights gained from this analysis not only underline the practicality and strategic significance of our framework but also suggest key strategies for businesses. [ABSTRACT FROM AUTHOR]
Copyright of Industrial Management & Data Systems is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Purpose: This paper investigates the complexities of a multimodal dry port transportation network, operating under the challenging conditions posed by the availability of transportation modes. Design/methodology/approach: Our study focuses on a network structure that includes multiple foreign seaports, local seaports, dry ports and manufacturers. We identify a significant research gap in the existing literature on dry port–based multimodal transportation networks. We first develop a mixed-integer non-linear programming (MINLP) model that adeptly integrates freight departure and demand scheduling, inventory management and backlog, while considering the availability of transportation modes in every node. To account for disruptions from uncertain events like extreme weather, we extend this deterministic foundation to a two-stage stochastic programming model. This stochastic model explicitly incorporates uncertainty in transportation mode availability, allowing proactive and adaptive strategies. Findings: The computational study, conducted across two distinct setups, demonstrates our models effectively avoiding substantial backlog penalties as well as delivering robust, cost-balanced solutions to handle uncertainty and complexity. In conclusion, the paper presents a comprehensive analysis including sensitivity analysis, dynamic planning evaluation and cross-regional validation, providing insightful guidance for strategic decision-making and demonstrating the model's adaptability across different operational contexts. Originality/value: The insights gained from this analysis not only underline the practicality and strategic significance of our framework but also suggest key strategies for businesses. [ABSTRACT FROM AUTHOR]
ISSN:02635577