A Benders decomposition method for locating stations in a one-way electric car sharing system under demand uncertainty
Abstract: We focus on a problem of locating recharging stations in one-way station based electric car sharing systems which operate under demand uncertainty. We model this problem as a mixed integer stochastic program and develop a Benders decomposition algorithm based on this formulation. We integrate a stabilization procedure to our algorithm and conduct a large-scale experimental study on our methods. To conduct the computational experiments, we developed a demand forecasting method allowing to generate many demand scenarios. The method was applied to real data from Manhattan taxi trips. We are able to solve problems with 100 to 500 scenarios, each scenario including 1000 to 5000 individual customer requests, under high and low cost values and 5 to 15 mins of accessibility restrictions, which is measured as the maximum walking time to the operating stations.
Keywords: Location, Urban Mobility, Electric Car Sharing, Benders Decomposition, Mixed Integer Stochastic Programming, Stochastic Demand
Category 1: Applications -- OR and Management Sciences (Transportation )
Category 2: Combinatorial Optimization (Branch and Cut Algorithms )
Category 3: Robust Optimization
Citation: Technical Report, Department of Computer Science, Université libre de Bruxelles, 02/2018.
Entry Submitted: 02/26/2018
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