Modeling and solving an integrated periodic vehicle routing and capacitated facility location problem in the context of solid waste collection

• URL: http://arxiv.org/abs/2504.10648v1
• Date: Mon, 14 Apr 2025 19:01:12 GMT
• Title: Modeling and solving an integrated periodic vehicle routing and capacitated facility location problem in the context of solid waste collection
• Authors: Begoña González, Diego Rossit, Mariano Frutos, Máximo Méndez,
• Abstract summary: This article proposes a unified optimization model to address two common waste management system optimization problems.<n>The integration of these two problems is not usual in the literature since each of them separately is already a major computational challenge.<n>Two improved exact formulations based on mathematical programming and a genetic algorithm (GA) are provided to solve this proposed unified optimization model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Few activities are as crucial in urban environments as waste management. Mismanagement of waste can cause significant economic, social, and environmental damage. However, waste management is often a complex system to manage and therefore where computational decision-support tools can play a pivotal role in assisting managers to make faster and better decisions. In this sense, this article proposes, on the one hand, a unified optimization model to address two common waste management system optimization problem: the determination of the capacity of waste bins in the collection network and the design and scheduling of collection routes. The integration of these two problems is not usual in the literature since each of them separately is already a major computational challenge. On the other hand, two improved exact formulations based on mathematical programming and a genetic algorithm (GA) are provided to solve this proposed unified optimization model. It should be noted that the GA considers a mixed chromosome representation of the solutions combining binary and integer alleles, in order to solve realistic instances of this complex problem. Also, different genetic operators have been tested to study which combination of them obtained better results in execution times on the order of that of the exact solvers. The obtained results show that the proposed GA is able to match the results of exact solvers on small instances and, in addition, can obtain feasible solutions on large instances, where exact formulations are not applicable, in reasonable computation times.

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