A New Approach for Vehicle Routing with Stochastic Demand: Combining Route Assignment with Process Flexibility

Kirby Ledvina (), Hanzhang Qin (), David Simchi-Levi () and Yehua Wei ()
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Kirby Ledvina: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Hanzhang Qin: Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
David Simchi-Levi: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Yehua Wei: Fuqua School of Business, Duke University, Durham, North Carolina 27708

Operations Research, 2022, vol. 70, issue 5, 2655-2673

Abstract: We propose a new approach for the vehicle routing problem with stochastic customer demands revealed before vehicles are dispatched. We combine ideas from vehicle routing and manufacturing process flexibility to propose overlapped routing strategies with customer sharing. We characterize the asymptotic performance of the overlapped routing strategies under probabilistic analysis while also providing an upper bound on the asymptotic performance that depends only on the mean and standard deviation of the customer demand distribution. Moreover, we show that the optimality gap of our approach decays exponentially as the size of overlapped routes increases. We demonstrate that our overlapped routing strategies perform close to the theoretical lower bound derived from the reoptimization strategy and significantly outperform the routing strategy without overlapped routes. The effectiveness of the proposed overlapped routing strategies in nonasymptotic regimes is further verified through numerical analysis.

Keywords: Transportation; stochastic vehicle routing; route design; process flexibility; probabilistic analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:70:y:2022:i:5:p:2655-2673

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