10h30 - 10h55
Lagrangian Heuristics for Large-Scale Dynamic Facility Location with Generalized Modular Capacities
We consider a facility location problem with multiple time periods, modular capacities and multiple commodities that generalizes several existing location problems due to its general cost structure. We propose Lagrangian heuristics that provide stable results even for large-scale instances, for which general-purpose MIP solvers do not find feasible solutions.
10h55 - 11h20
Exact Solution Methodologies for the Capacitated p-Center Problem
The capacitated p-center problem requires locating p facilities with capacity restrictions on a given network so that the maximum distance between demand nodes and the facilities they are assigned to is minimized. We propose new mathematical formulations and exact algorithms based on these formulations for solving the problem.
11h20 - 11h45
Integrated Location – Service Network Design
We address the problem setting wherein a single unit of resource is required to operate a service, resources are assigned to terminals to which they must ultimately return, there are a finite number of resources assigned to each terminal, and the length of resource circuits is restricted. Moreover, a number of fleet utilization issues must be addressed at the beginning of the season: 1) repositioning resources among terminals to account for shifts in demand patterns; 2) acquire (buy or long-term rent) new resources and assign them to terminals; 3) outsource particular services. We present an integrated formulation combining these selection-location and scheduled service design decisions. The mixed-integer formulation is defined over a time-space network, the initial period modelling the location decisions on resource acquisition and positioning, while the decisions on service selection and scheduling, resource assignment and cycling routing, and demand satisfaction being modelled on the rest of the network. We also present a matheuristic solution method combining slope scaling and column generation, discuss its algorithmic performance, and explore the impact of combining the location and design decisions in the context of consolidation carrier service design.
11h45 - 12h10
Affinely Adjustable Robust Location Transportation Problem
We study the application of adjustable robust optimization to a location transportation problem with uncertain demand. Unlike previous approximations for this problem, the method we employ allow us to exploit the fact that while strategic decisions need to be immediately implemented, operational decisions can be delayed until the actual demand is observed.