2025

Heure:	10:30 - 11:30
Lieu:	Salle A303
Résumé:	A double ALNS metaheuristic for the multi-commodity location-network design problem with selection of heterogeneous vehicles
Description:	Francesco Contu This work investigates a decision support system for planning a consolidation-based distribution system in a city where inbound freight arrives in containers at an intermodal terminal. Since this facility lacks storage and transdock capabilities, containers must be transferred to satellite facilities to be unpacked and reloaded onto smaller vehicles. The problem is approached from the perspective of an urban mobility manager, who must select the satellite facilities and vehicles, define their routes, and determine the commodity flows to final destinations, while optimizing transportation resources. The problem is formulated as a Mixed-Integer Linear Programming model. To address realistically sized instances, a metaheuristic based on two Adaptive Large Neighborhood Searches (ALNSs) is proposed: the outer ALNS selects satellites and vehicles, and assigns containers to satellites; the inner ALNS handles routing and allocation decisions on the second echelon. These procedures are run iteratively. The metaheuristic is used to conduct an extensive experimental campaign using data from the city of Cagliari (Italy) to evaluate the distribution system.

Heure:	10:30 - 11:30
Lieu:	Salle B107
Résumé:	Deep Dual-Optimal Inequalities for Generalized Capacitated Fixed-Charge Network Design Problems
Description:	Alexis Schneider Capacitated fixed-charge network design problems and generalizations, such as service network design problems, have a wide range of applications but are known to be very difficult to solve. Many exact and heuristic algorithms to solve these problems rely on column-and-row generation (CRG), which frequently suffer from primal degeneracy. We present a set of dual inequalities, equivalent to a simple primal relaxation, that speed up CRG algorithms for generalized capacitated fixed charge network design problems. We investigate the impact of the dual inequalities theoretically as well as experimentally. For practical applications, the presented technique is simple to implement, has no additional computational cost and can accelerate CRG by orders of magnitude, depending on the problem size and structure.

Heure:	10:30 - 11:30
Lieu:	Salle B107
Résumé:	Parametric polyhedra in mixed-integer programming
Description:	Diego Morán Ramírez We present some old and new results on arbitrary families of parametric polyhedra. First, if the constraint matrix is fixed, in the literature there are structural results for the integer hull and the finiteness of cutting plane closures for varying r.h.s. For instance, recently, Becu et al. proved in "Approximating the Gomory Mixed-Integer Cut Closure Using Historical Data" that the GMI closure of this family is finitely generated, in the sense that there exists a finite list of aggregation weights defining the GMI cuts that give the GMI closure for any polyhedra in the family. We extend this result for other cutting plane closures. Second, if the family of parametric polyhedra is arbitrary but all polyhedra in the family have the same integer hull, they define the same MIP, and we can leverage this information to understand and solve MIPs better. These families have been used to understand theoretical properties of the rank of cutting planes and to obtain better formulations. We present an application of these same-integer-hull families to formulations for the Asymmetric Traveling Salesman Problem.