NEXT SEMINAR 2023/2024
In this section, you will find the information (Speaker/Guest, abstract, date & time slot , location, online registration link..) about the upcoming seminar during the year. The seminar registrations will be on Eventbrite and everyone is welcome, from within ESSEC as well as from outside.
Layla MARTIN - Eindhoven University of Technology - Netherland
October 25th, 12pm - 1pm / Room C5
Abstract: Next-day delivery logistics services are redefining the industry by increasingly focusing on customer service. A challenge each logistics service provider faces is to jointly optimize time window assignment and vehicle routing for such next-day delivery services. To do so in a cost-efficient and customer-centric fashion, real life uncertainty such as stochastic travel times need to be incorporated in the optimization process. This paper focuses on the canonical optimization problem within this context; the Time Window Assignment Traveling Salesperson Problem with Stochastic Travel Times (TWATSP-ST). It belongs to the class of two-stage stochastic mixed-integer programming problems with continuous recourse. We introduce Two-Step Benders Decomposition with Scenario Clustering (TBDS) as a general exact solution methodology for solving such stochastic programs to optimality. The method combines and generalizes Benders dual decomposition, partial Benders decomposition, and Scenario Clustering techniques and does so within a novel two-step decomposition along the binary and continuous first-stage decisions. Extensive experiments show that TBDS is superior to state-of-the-art approaches in the literature. It solves TWATSP-ST instances with up to 25 customers to optimality. It provides better lower and upper bounds that lead to faster convergence than related methods. For example, Benders dual decomposition cannot optimally solve instances of 10 customers. We use TBDS to analyze the structure of the optimal solutions. By increasing routing costs only slightly, customer service can be improved tremendously, driven by smartly alternating between high- and low-variance travel arcs to reduce the impact of delay propagation throughout the executed vehicle route. Joint work with Sifa Celik, Layla Martin, Albert H. Schrotenboer, Tom Van Woensel
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