Integrated Location-Vehicle Routing for the Distribution of Products Through the Supply Chain for the Sustainable Development of Cities

In research related to location and vehicle routing, as well as its applications in supply chain management, the academic community is interested in contributing to the solution of social problems. To this end, a bi-objective optimization model and two metaheuristics based on local search and genetic algorithms were proposed and applied to the supply chain for the distribution of products, with the aim of promoting sustainable development in cities. The logistics network for this process consists of two levels: supplier-facility and facility-customer. A route plan is obtained through proposal optimization of costs and distances. Additionally, vehicle fuel consumption and CO2 emissions are measured during the journey. The proposals were implemented using GLPK and Python, and while the model generates solutions quickly in small scenarios, it does not find results within the time limit of 7200 s for large scenarios. The metaheuristics efficiently produces results for large scenarios within the established limit, demonstrating its significant potential.