A New Approach for Vehicle Routing with Stochastic Demand: Combining Route Assignment with Process Flexibility
Hanzhang QinLaboratory for Information and Decision Systems MIT
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 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 non-asymptotic regimes is further verified through numerical analysis.
Hanzhang Qin is a Ph.D. candidate in Computational Science and Engineering under supervision of Professor David Simchi-Levi. Affiliated with Laboratory for Information & Decision Systems and Center for Computational Science & Engineering at MIT, Hanzhang has research interests that span stochastic control, applied probability and statistical learning, with applications in supply chain analytics and transportation systems. He holds two master's, one in EECS and one in Transportation both from MIT. Prior to attending MIT, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University, where he was advised by Professor Hai Jiang and Professor Liping Zhang for his undergraduate theses.