Solving the last mile delivery problem using iterated local search approach

This paper deals with a real-life vehicle routing problem (VRP) called the last mile delivery problem (LMDP). We formulate this problem into a mixed integer linear programming model, which can be viewed as a combination of the capacitated VRP, the multi-depot VRP, the open VRP and the pickup-and-delivery problem with time windows (PDPTW). To solve the LMDP, we develop a meta-heuristic algorithm based on iterated local search, in which an adaptive large neighborhood search is applied in the perturbation phase to enlarge the search scope. Results from the computational experiments show that the proposed approach performs well when applied to the benchmark instances. Furthermore, we demonstrate the effectiveness of our algorithm by applying it to some real cases of the LMDP.