A solution approach for dynamic vehicle and crew scheduling |
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| Institution: | 1. OR division, M.A.I.O.R. Srl, Lucca, Italy;2. Dipartimento di Informatica, Università di Pisa, Largo B. Pontecorvo 3, Pisa, 56127, Italy;1. Centro de Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, C6, 1749-016 Portugal;2. ISA, Universidade de Lisboa, Grupo da Matemática, Tapada da Ajuda, 1349-017 Lisboa, Portugal;3. ISEG, Universidade de Lisboa, Rua do Quelhas 6, 1200-781 Lisboa, Portugal;4. DEIO, Faculdade de Ciências, Universidade de Lisboa, C6, 1749-016 Lisboa, Portugal;1. Department of Geography and the Environment, University of Denver, Denver, CO, USA;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;3. Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;4. Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA |
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| Abstract: | In this paper, we discuss the dynamic vehicle and crew scheduling problem and we propose a solution approach consisting of solving a sequence of optimization problems. Furthermore, we explain why it is useful to consider such a dynamic approach and compare it with a static one. Moreover, we perform a sensitivity analysis on our main assumption that the travel times of the trips are known exactly a certain amount of time before actual operation.We provide extensive computational results on some real-world data instances of a large public transport company in the Netherlands. Due to the complexity of the vehicle and crew scheduling problem, we solve only small and medium-sized instances with such a dynamic approach. We show that the results are good in the case of a single depot. However, in the multiple-depot case, the dynamic approach does not perform so well. We investigate why this is the case and conclude that the fact that the instance has to be split in several smaller ones, has a negative effect on the performance. |
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