A Hybrid Heuristic based on Iterated Local Search for Multivehicle Inventory Routing Problem

We study a multivehicle inventory routing problem (MIRP) in which supplier delivers one type of product along a finite planning horizon, using a homogeneous fleet of vehicles. The main objective is to minimize the total cost of storage and transportation. In order to solve MIRP, we propose an algorithm based on iterated local search (ILS) metaheuristic, using a variable neighborhood descent with random neighborhood ordering (RVND) in the local search phase. Moreover, we combined this algorithm with an exact procedure based on mathematical programming to solve specifically the inventory management as a subproblem. To validate our approach, computational tests were performed on 560 benchmark instances, achieving very competitive results in comparison to the best known algorithms.