Path selection model and algorithm for emergency logistics management |
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| Authors: | Yuan Yuan Dingwei Wang |
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| Affiliation: | 1. Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang, China;2. Institute of Systems Engineering, School of Information Science and Engineering, Northeastern University, Shenyang, China;1. School of Business Administration, Northeastern University, PR China;2. Department of Industrial Engineering Management, Shanghai JiaoTong University, China;3. Liaoning Key Laboratory of Manufacturing System and Logistics, The Logistics Institute, Northeastern University, PR China;1. School of Industrial Engineering and Engineering Optimization Research Group, College of Engineering, University of Tehran, Tehran, Iran;2. LCFC, Arts et Métiers ParisTech, Metz, France;3. Department of Operations Management & Strategy, School of Management, State University of New York at Buffalo, Buffalo, NY, USA;1. Texas A&M University, TX, USA;2. Peter F. Drucker and Masatoshi Ito Graduate School of Management, Claremont Graduate University, Claremont, CA 91711, USA |
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| Abstract: | Path selection is one of the fundamental problems in emergency logistics management. Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time of disaster. First a single-objective path selection model is presented taking into account that the travel speed on each arc will be affected by disaster extension. The objective of the model is to minimize total travel time along a path. The travel speed on each arc is modeled as a continuous decrease function with respect to time. A modified Dijkstra algorithm is designed to solve the model. Based on the first model, we further consider the chaos, panic and congestions in time of disaster. A multi-objective path selection model is presented to minimize the total travel time along a path and to minimize the path complexity. The complexity of the path is modeled as the total number of arcs included in the path. An ant colony optimization algorithm is proposed to solve the model. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper. |
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