HEC Montréal, Canada, 6 - 8 mai 2013

Journées de l'optimisation 2013

HEC Montréal, Canada, 6 — 8 mai 2013

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MB3 Tournées de véhicules II / Vehicle Routing Problem II

6 mai 2013 15h30 – 17h10

Salle: St-Hubert

Présidée par Luis Gouveia

3 présentations

  • 15h30 - 15h55

    Polynomial-Time Separation of Enhanced Reverse Multistar Inequalities

    • Luis Gouveia, prés., University of Lisbon
    • Juan Jose Salazar Gonzalez, Universidad de La Laguna

    The Vehicle Routing Problem with a minimum number of customers per route concerns the Capacitated Vehicle Routing Problem with unit-demand customers and a lower bound on the number of customers visited by each vehicle. This paper answers two open questions in a previous article, namely finding a compact formulation for the problem such that the corresponding linear programming relaxation implies the Enhanced Reverse Multistar inequalities, and finding a polynomial-time separation algorithm for this class of inequalities.

  • 15h55 - 16h20

    The Dynamic Dial-a-Chauffeur Problem

    • Johan Oppen, prés., Molde University College
    • Niels Agatz, Rotterdam School of Management
    • F Jordan Srour, Lebanese American University

    We present a real-world problem, where a company offers a service to transport customers from one location to another in the customer's own car. One of many uses for this type of service is people who need to get themselves and their car home safely after drinking too much to drive.

    Results from initial computational experiments will be presented.

  • 16h20 - 16h45

    Tactical Time Slot Management for Home Delivery

    • Vahid Famildardashti, prés., Université de Montréal
    • Michel Gendreau, Polytechnique Montréal
    • Jean-Yves Potvin, Université de Montréal

    We consider a problem found in home delivery applications where time slots for service must be assigned to different geographical zones based on customer demand. A mathematical programming model is first presented,
    which allows for split deliveries. Then, an adaptive large neighborhood search framework is proposed to solve the problem.