Journées de l'optimisation 2017
HEC Montréal, 810 mai 2017
1^{er} Atelier Canadien sur l'optimisation des soins de santé (CHOW)
HEC Montréal, 1011 mai 2017
JOPT2017
HEC Montréal, 8 — 11 mai 2017
MB6 Problèmes de transport multiattributs / Multiattribute vehicle routing problems
8 mai 2017 10h30 – 12h10
Salle: CPA du Québec
Présidée par Luis Gouveia
4 présentations

10h30  10h55
A Matheuristic for Assembly, Production and Inventory Routing Problems
We introduce and formulate the assembly routing problem (ARP). We propose a threephase matheuristic to solve not only the the ARP, but also the PRP and the IRP. We obtained 818 new best known solutions out of 2,628 standard IRP and PRP test instances.

10h55  11h20
A variable MIP neighborhood descent algorithm for an ATM inventory routing problem
We introduce the variable MIP neighborhood descent algorithm, a generalpurpose algorithm which takes advantage of the MIP formulation of a problem to speed up the solution of a branchandcut algorithm. We test this algorithm on an inventory routing problem arising when replenishing a network of ATMs.

11h20  11h45
Multiple Depot Vehicle Scheduling Problem with controlled trip shifting
We are interested in improving the classical Vehicle Scheduling problem with multiple depots by allowing a slight modification of departure schedules. By shifting some trips one can indeed expect to cover all trips with fewer vehicles and/or less expensive transit connexions.
However, reducing operational costs this way should not be detrimental to the overall quality of departure schedules. Therefore our model controls these three criterions: the number of shifted trips, the interval between two sameline consecutive trips and the quality of passengers connexions.
In order to solve this problem we propose two column generation based algorithms: an exact one and a heuristic one. We also apply several graph reductions which allow solving larger instances.
Tests on real urban data show that slightly shifting some trips can yield to a significant reduction in the number of vehicles used. 
11h45  12h10
New path elimination constraints for multidepot routing problems
Multidepot routing problems arise in distribution logistics where a set of vehicles based at several depots are used to serve a number of clients. Most variants of this problem have the basic requirement that the route of each vehicle starts and ends at the same depot. This talk describes new inequalities, namely multicut constraints (MCC), for multidepot routing problems that enforce this requirement. The MCCs are exponential in size, and are equivalent to a compact threeindex formulation for the problem in terms of the associated linear programming relaxations. In the talk we describe how a generalization of the MCCs can be obtained, in a similar manner, by using a stronger version of the threeindex formulation. The connection between the compact and the exponential formulations implies a separation procedure based on maxflow/mincut computations, which has reduced complexity in comparison with a previously known set of constraints described for the same purpose. The new inequalities are used in a branchandcut algorithm. Computational results with instances with 200 clients and up to 20 depots indicate that the algorithm is able to optimally solve the instances generally within a few hundred seconds of computation time.