A biobjective capacitated vehicle routing problem using metaheuristic ILS and decomposition

Abstract

Vehicle routing problems (VRPs) have usually been studied with a single objective function defined by the distances associated with the routing of vehicles. The central problem is to design a set of routes to meet the demands of customers at minimum cost. However, in real life, it is necessary to take into account other objective functions, such as social functions, which consider, for example, the drivers' workload balance. This has led to growth in both the formulation of multiobjective models and exact and approximate solution techniques. In this article, to verify the quality of the results, first, a mathematical model is proposed that takes into account both economic and work balance objectives simultaneously and is solved using an exact method based on the decomposition approach. This method is used to compare the accuracy of the proposed approximate method in test cases of medium mathematical complexity. Second, an approximate method based on the Iterated Local Search (ILS) metaheuristic and Decomposition (ILS/D) is proposed to solve the biobjective Capacitated VRP (bi-CVRP) using test cases of medium and high mathematical complexity. Finally, the nondominated sorting genetic algorithm (NSGA-II) approximate method is implemented to compare both medium- and high-complexity test cases with a benchmark. The obtained results show that ILS/D is a promising technique for solving VRPs with a multiobjective approach.

Hybrid Heuristic for Vehicle Routing Problem with Time Windows and Compatibility Constraints in Home Healthcare System

“…Insertion and saving heuristic [24] Adaptive large neighborhood search with removal and insertion operator [25] Adaptive genetic algorithm with 2-opt procedure [26] Particle swarm optimization with simulated annealing [27] Total distance Probabilistic tabu search [18] Branch and bound algorithm with greedy algorithm and large neighborhood search [28] Large neighborhood search with ruin and recreate procedure [29] Parallel cutting plane of branch and bound [17] Parallel strategy of branch and bound [15] Simulated annealing with ruin and recreate procedure [30] Lagrangian relaxation and Column generation [16] Genetic algorithm [23] Variable neighborhood search with pruning and propagation techniques of constraint [31] Simulated annealing with K-restart [32] Parallel hybrid genetic algorithm with evolution function [33] Two-phase set partitioning and genetic algorithm [20] Multi-parametric mutation procedure with 1 + 1 evolution strategies algorithm [34] Adaptive large neighborhood search [35] Parallel simulated annealing [21] Particle swarm optimization with adaptive strategies [36] Bi-level Minimizing the number of vehicles and total distance Two evolutionary strategies algorithm with representation and mutation operators [37] Simulated annealing (for solving primary) and large neighborhood search (for solving secondary) [38] Genetic algorithm with Pareto ranking technique [19] Arc-guided evolution strategies [39] Bi-level Minimizing cost and maximizing path length Iterated local search and decomposition [40] Multiple problems:…”

A greedy heuristic algorithm to solve a VRP-based model for planning and coordinating multiple resources in emergency response to bushfires

“…In terms of methodologies used to solve VRP models, which are in the NP-hard problem categories, we investigated following papers, all of which have presented (meta)heuristic methods. Galindres-Guancha et al [18] presented a multi-objective capacitated VRP model considering drivers' workload balance as well as cost of routs as two practical objectives in the real world. To solve the model, they decomposed it into single sub-problems, which were solved using Iterated Local Search method.…”