http://orcid.org/0000-0003-1526-1898
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ABSTRACT
Large-scale disasters and catastrophic events typically result in a significant shortage of critical resources, posing a great challenge to allocating limited resources among different affected areas to improve the quality of emergency logistics operations. This article pays attention to the performance of resource allocation, which includes three metrics: efficiency, effectiveness, and equity, respectively corresponding to economic cost, service quality, and fairness. In particular, the effectiveness metric considers human suffering by depicting it as deprivation cost, an economic valuation measurement that has been recently proposed and the equity metric concerns about the service equality at the end of planning horizon. A nonlinear integer model is first proposed and then an equivalent dynamic programming model is developed to avoid the nonlinear terms created by the introduction of the deprivation cost. The dynamic programming method can solve small-scale problems to optimality but meets difficulty when solving medium- and large-scale problems, due to the curse of dimensionality. Therefore, an approximate dynamic programming algorithm, called the rollout algorithm, is proposed to overcome this computational difficulty. The computational complexity of the proposed algorithm is theoretically analyzed. Furthermore, a modified version of the rollout algorithm is presented, with its computational complexity analyzed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms, and the experimental results demonstrate that the initially proposed rollout algorithm yields optimal or near-optimal solutions within a reasonable amount of time. In addition, the impacts of some important parameters are investigated and managerial insights are drawn.
Acknowledgements
The authors thank the two anonymous referees for their valuable comments and suggestions, which improved the quality of this article.
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Notes on contributors
Lina Yu
Lina Yu is a Ph.D. candidate in the Department of Industrial Engineering, Tsinghua University, China. Prior to starting her doctoral studies, she received her B.S. degree in the School of Traffic and Transportation, Beijing Jiaotong University, China. Her research interests include humanitarian logistics, resource allocation, dynamic programming, network design and optimization, and multi-objective optimization.
Huasheng Yang
Huasheng Yang is a postdoctoral researcher in the Department of Industrial Engineering, Tsinghua University, China. He received his Ph.D. degree in industrial engineering from Tsinghua University in 2016. His research interests include flexible supply chain management, analysis and application of industrial big data, and manufacturing execution systems.
Lixin Miao
Lixin Miao is a professor in Tsinghua University, China. He received his master's degree from Tongji University, China, in 1987. He is now the head of the Division of Logistics and Transportation of the Graduate School at Shenzhen, Tsinghua University, China. His research interests include the application of information technologies in logistics, optimization in logistic systems, intelligent transportation systems, and traffic and logistics simulation.
Canrong Zhang
Canrong Zhang is an associate professor in the Graduate School at Shenzhen, Tsinghua University, China. He received his Ph.D. degree in industrial engineering from Tsinghua University in 2010. His research interests include large-scale optimization, linear and integer programming, and dynamic programming with applications to container terminal operations, production planning and scheduling, and network design and optimization.