Iterated local search and simulated annealing algorithms for the inventory routing problem

Abstract

This paper addresses the inventory routing problem (IRP), which consists in defining the customer visit schedule, the delivery quantities, and the vehicle routing plan to meet the demands of a set of customers over a given time horizon. We consider the variant with a single item, a single supplier, multiple vehicles, and a finite multiperiod planning horizon, minimizing the sum of inventory and travel costs. In addition, we address an alternative objective function that minimizes the logistic ratio, defined as the total travel cost divided by the total quantity delivered to customers. This second objective function, while more realistic in some logistics settings, poses a challenge for integer programming models and exact methods because of its nonlinearity. To our knowledge, no heuristic method has been proposed to address this objective in the IRP variant addressed in this paper. To solve this problem with each of these objective functions, we propose effective metaheuristic algorithms based on iterated local search and simulated annealing. Computational experiments show that these algorithms provide reasonably high-quality solutions in relatively short running times for both objective functions when compared to other methods for well-known instances from the literature. Moreover, the algorithms produce new best solutions for some of these instances.

A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

“…The local search embeds a heuristic procedure; the perturbation and the acceptance criterion allow to explore the search space as well as to escape from local optima. The ILS metaheuristic has been applied in many optimization problems, such as vehicle routing problems (Juan et al., ; Pollaris et al., ; Brandão, ; Estrada‐Moreno et al., ), scheduling problems (Zohali et al., ; Cunha et al., ), facility location problems (De Armas et al., ), or inventory routing problems (Alvarez et al., ), among others. In particular, Stützle () and Juan et al.…”

A kernel search heuristic for the multivehicle inventory routing problem

“…The algorithm is used to solve also other problems related to the ARP, including the MIRP. Metaheuristic algorithms have instead been proposed in Santos et al (2016) and Alvarez et al (2018). Santos et al (2016) propose a hybrid multistart algorithm based on an iterated local search metaheuristic for the MIRP.…”

Recent challenges in Routing and Inventory Routing: E‐commerce and last‐mile delivery

“…The multi‐product and multi‐vehicle IRP is studied in Coelho and Laporte [41,42] and Cordeau et al [44]. More recently, metaheuristics algorithms were proposed in Santos et al [91] and Alvarez et al [6]. The former proposed a hybrid multi‐start algorithm based on an iterated local search, while the latter developed an iterated local search and a simulated annealing algorithm.…”