A Genetic Algorithm for Multi-Agent Pick-Up and Delivery Routing Problem

This research aims to solve the task assignment and route sequencing for multiple autonomous mobile robots, multi agent, in a vehicle routing problem with pick-up and delivery by applying the genetic algorithm. The proposed method, based on the genetic algorithm, is applied to solve the difference in the sizes of the number of tasks, 5, 10, and 15. The fitness value of the proposed genetic algorithm is calculated by the inversion of the total distance of the solution decoded from the chromosome. The proposed genetic algorithm's performance is compared to the greedy algorithm in terms of the deviation percentage of the fitness value. The result shows that the proposed genetic algorithm can find better solutions with an average deviation percentage of 5.61% than the greedy algorithm.