Iterated local search with neighborhood space reduction for two-echelon distribution network for perishable products
Résumé
This article presents a planning problem in a distribution network incorporating two levels of inventory management. Perishable products are routed with lot-sizing, multi-sourcing and limited transport capacity using a homogeneous fleet of vehicles. A mixed integer linear programming (MILP) and a greedy heuristic have been developed to solve this real planning problem. There are some instances for which the solver cannot give a good lower bound within the limited time and for other instances it takes a lot of time to solve MILP. The greedy heuristic is an alternative to the mixed integer linear program, used to quickly solve some large instances taking into account original and difficult constraints. For some instances the gap between the solution provided by the solver (MILP) and the heuristic becomes quite significant. An iterated local search (ILS) using the variable neighborhood descent (VND) method has been implemented to improve the quality of heuristic solutions. We have included the ILS method in an APS (Advanced Planning System) and have compared it with an exact resolution of the MILP. Two types of instances are tested: models derived from actual data and models built using a random generator of instances that have wider diversity for computational evaluation. The ILS procedure significantly improves the quality of solutions and average computational time is much shorter than MILP resolution.