15h30 - 15h55
Stochastic Uncapacitated Hub Location
We present stochastic uncapacitated hub location problems in which uncertainty is associated with demands and transportation costs. To solve the case of uncertain independent transportation costs, we propose a Monte-Carlo simulation-based algorithm that integrates a sample average approximation scheme with a Benders decomposition algorithm.
15h55 - 16h20
Facility Location with Economies of Scale and Congestion: Models and Column Generation Heuristics
We study location problems with an inverse S-shaped cost function that is initially concave and then turns convex. We introduce a nonlinear mixed integer programming formulation that is decomposable by environment type: economies of scale or congestion. We propose solution methods based on Lagrangean relaxation, column generation, and branch-and-bound.
16h20 - 16h45
Cannibalisation Effects in Franchise Chains: A Variable Neighbourhood Search Method
We present a Variable Neighbourhood Search heuristic to solve the classical Max-Min p-dispersion problem. It will serve as the core to build more practical franchise location models in the future. We intend to minimize cannibalization as much as possible by maximizing the dispersion between either the newly added units, or between the existing units and the new ones.
16h45 - 17h10
Dividing a Territory Among Several Facilities
We consider the problem of dividing a geographic region into sub-regions so as to minimize the maximum workload of a collection of facilities over that region. We consider two measures for the "workload" of a facility: one is the length of a TSP tour of all demand points in the assigned sub-region, and the other is a monomial function of the distance from a demand point to its assigned facility.