Emergency logistics considering traffic congestion

• Main Author: Wang, Qingyi
• Other Authors: Huang Weimin
• Format: Theses and Dissertations
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Industrial engineering::Operations research; DRNTU::Engineering::Industrial engineering::Engineering logistics
• Online Access: https://hdl.handle.net/10356/88118; http://hdl.handle.net/10220/45687
• Institution: Nanyang Technological University

Emergency logistics (EL) is the key to alleviate disaster impacts and to accelerate pre- and post-disaster relief operations. Particularly, plannings of emergency supply and evacuation serve a fundamental role to increase effectiveness and efficiency of EL in field practice, and various models have been developed to facilitate the plannings in the past few decades. However, challenges of developing more practical planning models, which incorporate realistic factors and relationships, for real-world applications still exist. Due to the prevalence and huge impacts of traffic congestion phenomena under emergency situations, the thesis focuses on proposing traffic congestion delays incorporated EL planning models with the goal of enhancing EL performances in practice. Based on an introduction of EL and traffic congestion, the thesis builds on three aspects. First, we conduct a structured literature review on various supply and evacuation planning models to reveal research gaps. Second, a multi-commodity two-stage stochastic programming model that explicitly incorporates traffic congestion delays and a decomposition-based algorithm are proposed to address a two-stage emergency supply planning problem. With a real-world case study, the superiority of the proposed model is verified, and some managerial insights are given. Third, a novel evacuation planning model is developed to address a dynamic evacuation planning problem of debris flow disasters. The model not only integrates three evacuation strategies (mobilization, staging, and routing) to accelerate evacuation but also incorporates human behavior and traffic congestion delays to enhance practicability of resulting plans. The proposed model can assist debris flow early warning systems, and its effectiveness is verified with an illustrative case study, which also aids in generating insights and policy suggestions for improving evacuation performances. In all, the thesis highlights the importance of considering the traffic congestion factor in formulating more practical and realistic EL planning models. Although incorporating traffic congestion brings about challenges in model formulation and solvability, its benefits of producing insights and enhancing real-world EL operations worth the challenges.