The Performance of Genetic Algorithms on Solving the Pickup and Delivery Vehicle Routing Problem

Abstract

This study examines the performance of genetic algorithms (GAs) on solving a pickup and delivery vehicle routing problem, typically faced by home delivery service providers. A number of combinations of GA's three main algorithmic operators, namely, selection, crossover, and mutation is implemented, and the data envelopment analysis (DEA) is adopted to evaluate and rank these various combinations of GA operators. The numerical results demonstrate that DEA is appropriate in determining the efficient combinations of GA operators. Among the combinations under consideration, the combination using tournament selection and simple crossover is generally more efficient. The findings also contribute to algorithm development and evaluation of vehicle routing problems from the operations research perspective.