A parallelizable dynamic fleet management model with random travel times |
| |
| Institution: | 1. School of Mathematics and Statistics, Shandong University of Technology, Zibo 255000, China;2. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;3. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;4. State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China;5. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China;1. Department of Operations, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands;2. Rise Lab – Department of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy;3. Panalpina Centre for Manufacturing and Logistics Research, Cardiff Business School, Cardiff University, Cardiff CF10 3EU, United Kingdom;4. IBIS Lab – Department of Industrial Engineering, University of Florence, Viale Morgagni 44, 50134 Florence, Italy |
| |
| Abstract: | In this paper, we present a stochastic model for the dynamic fleet management problem with random travel times. Our approach decomposes the problem into time-staged subproblems by formulating it as a dynamic program and uses approximations of the value function. In order to deal with random travel times, the state variable of our dynamic program includes all individual decisions over a relevant portion of the history. We show how to approximate the value function in a tractable manner under this new high-dimensional state variable.Under our approximation scheme, the subproblem for each time period decomposes with respect to locations, making our model very appealing for large-scale applications. Numerical work shows that the proposed approach provides high-quality solutions and performs significantly better than standard benchmark methods. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
