Parallel Ant Colony Optimization for the Electric Vehicle Routing Problem

Abstract

Parallelizing metaheuristics has become a common practice considering the computation power and resources available nowadays. The aim of parallelizing a metaheuristic is either to increase the quality of the generated output, given a fixed computation time, or to reduce the required time in generating an output. In this work, we parallelize one of the best-performing ant colony optimization (ACO) algorithms and apply it to the electric vehicle routing problem (EVRP). EVRP is more challenging than the conventional vehicle routing problem, as with the consideration of electric vehicles additional hard constraints arise within the EVRP due to their limited driving range (e.g., the consideration whether electric vehicles need to visit a charging station during their daily operation). The proposed parallel ACO algorithm with several colonies also uses a migration policy to allow communication between the different colonies. From the simulation studies it is shown that parallelizing ACO algorithms, both with and without a migration policy, is highly effective.

Ant Colony optimization with Heuristic Repair for the Dynamic Vehicle Routing Problem

“…A non-negative value w ij ∈ R + is associated with each arc, representing the euclidean distance between nodes i and j. Node 0 denotes the central depot whereas the remaining nodes denote the customers. Each customer i ∈ N is assigned a positive value δ i indicating the customer's delivery demand 1 . Each vehicle 2 has a maximal cargo capacity C.…”

“…Ant colony optimization (ACO) algorithms have proved to be powerful problem-solving tools. They are able to provide the optimal (or near optimal) solution for difficult vehicle routing problems (VRPs) [1], [2]. Traditionally, researchers have focused their attention on static optimization problems, where the environment of the problem remains fixed during the optimization process of an algorithm.…”

A Benchmark Test Suite for the Electric Capacitated Vehicle Routing Problem

“…The MAX -MIN Ant System (MMAS) [19] variant is used which is one of the most-studied ACO variants and has already proven its good performance on relevant EVRPs [5], [20]. In this section we describe the application of MMAS to the E-CVRP, including the proposed method to construct feasible solutions.…”

Memory-Based Ant Colony System Approach for Multi-Source Data Associated Dynamic Electric Vehicle Dispatch Optimization

“…Real ants cooperate to find the shortest path from their nest to the food via pheromone. In recent years, ACO and its ant colony system (ACS) variant [33] have been successfully applied to many COPs, such as cloud resource scheduling [34], [35], scheduling problems [36], [37], personalized trip recommendation [38], disassembly planning problems [39], vehicle routing problem [40]- [42], and taxi dispatch problems [43]. Since the DEVD problem studied in this paper is a COP extended from taxi order dispatch, ACO can be a promising solver.…”