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
MD3 Optimisation combinatoire 1 / Combinatorial Optimization 1
8 mai 2017 15h30 – 17h10
Salle: MarieHusny
Présidée par Ilyas Himmich
4 présentations

15h30  15h55
Optimal design of data centers.
We present our current research on data centers design. Our goal is to find a network on 64 vertices with a small average distance and a bounded maximum degree, and some robustness properties. First, we establish some mathematical properties and then we find some interesting candidates by optimization.

15h55  16h20
Integer column generation using decomposition
Integer column generation using decomposition (ICG) is a new primal method that aims to solve the popular set partitioning problem. This method finds a sequence of integer solutions, with nonincreasing cost, leading to optimal or nearoptimal solutions in reasonable time. Potential columns favoring integrality are generated using a suited dual vector. Some acceleration strategies improving the effectiveness of ICG will be discussed. Computational experiments on some largescale bus drivers scheduling and aircrew pairing problems will be presented. The results obtained demonstrate the efficiency of ICG.

16h20  16h45
ANNULÉ / Pickup and delivery with complex loading constraints: application to the gasoline distribution
In this work, we present a Branch & Price method to solve a realworld pickup and delivery problem arising in the sector of the distribution of gasoline. The underlying network consists of four distinct depots, a group of private carriers with heterogeneous tank trucks and five types of gasoline to replenish three groups of customers on a weekly basis. Complex loading and routing rules are handled in the subproblem, a very difficult shortest path problem with resource constraints. Acceleration strategies will be discussed. Numerical results on real data show the high potential of the proposed approach.

16h45  17h10
Primal Neighbourhood Search Algorithm for solving the Shortest Path Problem with Resource Constraints
We propose a new exact primal method for solving the shortest path problem with resource constraints. Our algorithm performs a search in the neighbourhood of a set of sourcetask paths. We first define the notion
of adjacency in the context of the SPPRC. Then, we develop some polyhedral properties that are useful in the definition and exploration of the neighbourhood. Computational results on the VCSP show that the proposed solution approach is more efficient than classical dynamic programming algorithm.