A biased random-key genetic algorithm with variable mutants to solve a vehicle routing problem

TL;DR

This paper introduces a novel biased random-key genetic algorithm with variable mutants (BRKGA-VM) for vehicle routing problems, demonstrating improved efficiency and solution quality through hybridization with local search techniques.

Contribution

The paper presents a new BRKGA variant with a variable mutant population and an innovative decoder, enhancing solution exploration and performance in vehicle routing problems.

Findings

BRKGA-VM outperforms previous BRKGA versions in effectiveness.

Variable mutants enable faster convergence to solutions.

Hybridization with VND improves solution quality.

Abstract

The paper explores the Biased Random-Key Genetic Algorithm (BRKGA) in the domain of logistics and vehicle routing. Specifically, the application of the algorithm is contextualized within the framework of the Vehicle Routing Problem with Occasional Drivers and Time Window (VRPODTW) that represents a critical challenge in contemporary delivery systems. Within this context, BRKGA emerges as an innovative solution approach to optimize routing plans, balancing cost-efficiency with operational constraints. This research introduces a new BRKGA, characterized by a variable mutant population which can vary from generation to generation, named BRKGA-VM. This novel variant was tested to solve a VRPODTW. For this purpose, an innovative specific decoder procedure was proposed and implemented. Furthermore, a hybridization of the algorithm with a Variable Neighborhood Descent (VND) algorithm has also been considered, showing an improvement of problem-solving capabilities. Computational results show a better performances in term of effectiveness over a previous version of BRKGA, denoted as MP. The improved performance of BRKGA-VM is evident from its ability to optimize solutions across a wide range of scenarios, with significant improvements observed for each type of instance considered. The analysis also reveals that VM achieves preset goals more quickly compared to MP, thanks to the increased variability induced in the mutant population which facilitates the exploration of new regions of the solution space. Furthermore, the integration of VND has shown an additional positive impact on the quality of the solutions found.