Optimizing Routes for Medicine Distribution Using Team Ant Colony System

Abstract

Distributing medicine using multiple deliverymen in big hospitals is considered complex and can be viewed as a Multiple Traveling Salesman Problem (MTSP). MTSP problems aim to minimize the total displacement of the salesmen, in which all intermediate nodes should be visited only once. The Team Ant Colony Optimization (TACO) can be used to solve this sort of problem. The goal is to find multiple routes with similar lengths to make the delivery process more efficient. Thus, we can map this objective in two different fitness functions: minimizing the longest route, aiming to be fair in the allocation of the workload to all deliverymen; and decreasing the total cost of routes, seeking to reduce the overall workload of the deliverymen. However, these objectives are conflicting. This work proposes the use of swarm optimizers to improve the performance of the TACO concerning these two objectives. The results using global optimizers for the parameters far outperformed the original TACO for the case study.