Abstract

Port–hinterland container logistics transportation systems (PHCLTSs) are significant to economic and social development. However, various kinds of unconventional emergency events (UEEs), such as natural or human-caused disasters, threaten PHCLTSs. This study aims to measure and improve the resilience of PHCLTSs. Bi-level programming models with two different lower level models are established to help PHCLTSs recover their capacity efficiently in the face of UEEs. In the upper level model, the government makes immediate recovery decisions about a damaged PHCLTS with the goal of improving the resilience of the PHCLTS. In the lower level models, truck carriers make decisions about transportation routes and freight volume in the recovered PHCLTS. They cooperate fully to pursue the maximization of total profit and are coordinated by a central authority, or they make their own decisions to pursue maximization of their own profit noncooperatively. An algorithm combining particle swarm optimization (PSO) and traditional optimization algorithms is proposed to solve the bi-level programming models. The numerical experimental results show the validity of the proposed models.

Highlights

  • World trade has maintained a growth trend since the 21st century

  • This study focuses on a crucial subsystem of maritime transportation systems called the “port–hinterland container logistics transportation system” (PHCLTS)

  • This study analyzes the resilience problem of PHCLTSs by using the bi-level programming approach. It aims to learn about three aspects from the relevant literature: the resilience of transportation systems corresponding to the goal of the government, logistics transportation system assignment and equilibrium corresponding to the behaviors of PHCLTS users, and the bi-level programming model for logistics transportation system problems corresponding to the approach used in this study

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Summary

Introduction

World trade has maintained a growth trend since the 21st century. The World Trade. This study aims to provide an optimal decision-making reference for Logistics transportation system participants, including organizations and individuals, governments to improve the resilience of PHCLTSs in the face of UEEs to recover the opwill affect the resilience of the system through their actions and interactions [5]. The shippers make decisions about the demands for containers in hinterlands, decision on immediate recovery activities to improve the resilience of PHCLTSs. For the and the carriers complete the actual transportation process of containers in the recovered lower level model, the decision makers are the users of PHCLTSs, i.e., shippers and carriPHCLTS that are from port to hinterland points in accordance with the shippers’ demand.

Literature Review
Resilience of Transportation Systems
Logistics Transportation System Assignment and Equilibrium
Bi-Level Programming Model for Logistics Transportation System Problems
Model Description and Assumptions
Time Resilience of a PHCLTS
Upper Level Model
A UEE scenario
Lower Level Model 1
Lower Level Model 2
Solution Algorithm
Description of Standard PSO
An Algorithm Combining PSO and Traditional Optimization Algorithms
Illustration
Description of Numerical Studies
Analysis of the Influence of Maximum Budget for Recovery Activities
Comparison between the Lower Level SO and UE Models
Comparison withrecovery
Analysis of the Influence of Demand in Hinterland Points
11. Resilience levels ofdifferent different demand leve
Findings
Managerial Insights and Conclusions
Full Text
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