A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
The String-to-String Correction Problem
Journal of the ACM (JACM)
The vehicle routing problem
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows
Computational Optimization and Applications
Distance measures based on the edit distance for permutation-type representations
Journal of Heuristics
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
Computers and Operations Research
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The Vehicle Routing Problem can be seen as a fusion of two well known combinatorial problems, the Travelling Salesman Problem and Bin Packing Problem. It has several variants, the one with Time Windows being the case of study in this paper. Its main objective is to find the lowest-distance set of routes to deliver goods to customers, which have service time windows, using a fleet of identical vehicles with restricted capacity. We consider the simultaneous minimisation of the number of routes along with the total travel distance. Although previous research has considered evolutionary methods for solving this problem, none of them has concentrated on the similarity of solutions. We analyse here two methods to measure similarity, which are incorporated into an evolutionary algorithm to solve the multi-objective problem. We have applied this algorithm to a publicly available set of benchmark instances, and when these similarity measures are considered, our solutions are seen to be competitive or better than others previously published.