Metaheuristic-based optimization of treated water distribution in a Mexican City

Abstract

Nowadays, urban areas are composed of logistic networks that form a compact and complex entity whose integration has a meaningful impact on the sustainability of the urban system. Currently, there are several models for the optimization of these networks mainly focused on the transport of products and people. One of these models is the Vehicle Routing Problem (VRP). The scientific literature provides evidence that Genetic Algorithms (GAs) find acceptable solutions to VRP. Besides, local searches optimize the GA solutions and hence reduce computing runtime. In this study, three algorithms for local search are compared: (1) Tabu search, (2) a threshold accepting approach, and (3) a multi-start local search based on cross exchanges. Instances taken from a real-world case study of water distribution from the “Junta Municipal de Agua y Saneamiento de Ciudad Ju´arez” are considered for this analysis. Further results show that implemented local search algorithms reduce the traveled distance of the initial solutions