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Dynamic emergency logistics planning: models and heuristic algorithm

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Abstract

Military force serves an important function in disaster relief operations, such as in delivering relief materials to affected areas, providing medical service, and maintaining orders, in many countries, especially in China. After a disaster occurs, relief materials should be dispatched to destinations as soon as possible. The dynamic emergency logistics planning problem considers the method by which different kinds of resources are utilized to achieve the goal. This study proposes a time-space network model to address this problem. In this model, supplies and demands are time-variant, and different kinds of transportation modes are used to deliver commodities. Thus, we decompose the proposed model into two multi-period multi-commodity network flow problems. The first focuses on dispatching conventional commodities, and the second deals with the routes and schedules of vehicles. We propose a nested partitions-based heuristic to address the computational complexity of the problem. The basic idea of the algorithm is to partition the solution region by fixing some variables and to identify the most promising subregion on the basis of the objective value of the corresponding linear programming relaxation problem. The process is repeated until a feasible solution of high quality is identified. The computational experiments demonstrate the efficiency of the proposed algorithm. Furthermore, we propose a variant of the model with consideration of the demand uncertainty, and we apply robust optimization methodology to address the problem. The proposed models and algorithm provide robust support for decision makers when quick responses are necessary for disaster relief activities.

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Acknowledgments

The work in this paper was partially supported by Special Research Funds in Public Welfare Sector of China under Grant 201313009-7, Special Funds of National Science and Technology Support Plan of China under Grant 2013BAD17B08 and National Science Foundation of China (NSFC) under Grant 71371015. The authors would like to thank anonymous referees for their valuable comments.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing, 100871, China

    Longfei Wang, Jie Song & Leyuan Shi

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  1. Longfei Wang
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  2. Jie Song
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Correspondence to Jie Song.

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Wang, L., Song, J. & Shi, L. Dynamic emergency logistics planning: models and heuristic algorithm. Optim Lett 9, 1533–1552 (2015). https://doi.org/10.1007/s11590-015-0853-z

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