The Share-a-Ride problem with stochastic travel times and stochastic delivery locations |
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Affiliation: | 1. Laboratory of Modeling and Optimization for Decisional, Industrial and Logistic Systems (MODILS), Faculty of Economics and Management Sciences, University of Sfax, Airport Street, km 4, Post Office Box 1088, 3018 Sfax, Tunisia;2. Research Group Logistics, Hasselt University, Campus Diepenbeek, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium;3. Research Foundation Flanders (FWO), Egmontstraat 5, 1000 Brussels, Belgium;4. Université de Lyon, F-42023 Saint Etienne, France;5. Université de Saint Etienne, Jean Monnet, F-42000 Saint-Etienne, France;6. LASPI, F-42334, IUT de Roanne |
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Abstract: | We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions. |
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Keywords: | Share-a-Ride problems Adaptive large neighborhood search Stochastic travel times Stochastic delivery locations Sampling strategies |
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