The optimization of warehouse location and resources distribution for emergency rescue under uncertainty |
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| Affiliation: | 1. Beijing Academy of Safety Science and Technology, Yunhe East Street 57, Beijing 101101, China;2. School of Management, Shanghai University, Shang Da Road 99, Shanghai 200444, China;1. Department of Civil & Environmental Engineering, National University of Singapore, Block E1A, #07-03, No.1 Engineering Drive 2, Singapore 117576, Singapore;2. Future Cities Laboratory, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, #06-01, Singapore 138602, Singapore;3. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;1. Applied Mechanics and Construction, University of Vigo, Spain;2. Chair of Computational Modelling and Simulation, Technical University of Munich, Germany;1. Faculty of Science, Agriculture, and Engineering, Newcastle University, Singapore 599493, Singapore;2. Xylem Inc, USA;3. Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, TX 78712, USA |
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| Abstract: | China is one of the countries that suffer the most natural disasters in the world. The situation of emergency response and rescue is extremely tough. Establishing the emergency warehouse is one of the important ways to cope with rapid-onset disasters. In this paper, a mixed integer programming (MIP) model based on time cost under uncertainty is proposed, which help solve the emergency warehouse location and distribution problem. Comprehensive consideration of factors such as time cost, penalty cost for lack of resources, alternative origins of resources from both suppliers and emergency warehouses, different means of transportation and multiple resources types are involved in our study. We also introduce uncertain scenarios to describe the severity of the disaster. Particle swarm optimization (PSO) and variable neighborhood search (VNS) are designed to solve the MIP model of different scales of instances. Numerous examples have been tested to compare two heuristics with commercial solver (CPLEX). Both of two algorithms can obtain the exact solution same as CPLEX in small-scale instances while show great performance on larger instances with 10 candidate warehouses, 25 disasters and 50 scenarios. |
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| Keywords: | Warehouse location Resources distribution Variable neighborhood search Particle swarm optimization |
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