Developing tools for the team orienteering problem: A simple genetic algorithm

Presently, the large-scale collection process of selective waste is typically expensive, with low efficiency and moderate effectiveness. Despite the abundance of commercially available software for fleet management, real life managers are only minimally helped by it when dealing with resource and budgetary requirements, scheduling activities, and acquiring resources for their accomplishment within the constraints imposed on them. To overcome these issues, we intend to develop a solution that optimizes the waste collection process by modelling this problem as a vehicle routing problem, in particular as a Team Orienteering Problem (TOP). In the TOP, a vehicle fleet is assigned to visit a set customers, while executing optimized routes that maximize total profit and minimize resources needed. In this work, we propose to solve the TOP using a genetic algorithm, in order to achieve challenging results in comparison to previous work around this subject of study. Our objective is to develop and evaluate a software application that implements a genetic algorithm to solve the TOP. We were able to accomplish the proposed task and achieved interesting results with the computational tests by attaining the best known results in half of the tested instances.