Robust optimization for fleet planning under uncertainty |
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| Institution: | 1. Rensselaer Polytechnic Institute, Troy, NY 12180, USA;2. School of Civil & Environmental engineering, Cornell University, Holister Hal, Ithaca, NY 14853, USA;3. Sandia National Laboratories, Albuquerque, NM 87185, USA;1. Department of Building, Civil and Environmental Engineering, Concordia University, H3G 1M8 Montreal, Quebec, Canada;2. Department of Building and Real Estate (BRE), Hong Kong Polytechnic University, Kowloon, Hong Kong;3. Deptartment of Architecture and Building Sciences, King Saud University, Riyadh, Saudi Arabia;4. College of Engineering, United Arab Emirates University, Alain, United Arab Emirates;1. TUM School of Management, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany;2. Faculty of Business and Economics, Universität Augsburg, Universitätsstr. 16, 86159 Augsburg, Germany;1. School of Aviation, The University of New South Wales, Sydney, Australia;2. Department of Transportation and Logistics Management, National Chiao Tung University, Hsinchu, Taiwan |
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| Abstract: | We create a formulation and a solution procedure for fleet sizing under uncertainty in future demands and operating conditions. The formulation focuses on robust optimization, using a partial moment measure of risk. This risk measure is incorporated into the expected recourse function of a two-stage stochastic programming formulation, and stochastic decomposition is used as a solution procedure. A numerical example illustrates the importance of including uncertainty in the fleet sizing problem formulation, and the nature of the fundamental tradeoff between acquiring more vehicles and accepting the risk of potentially high costs if insufficient resources are available. |
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