Department of Engineering, Universidad Nacional del Sur, Alem Av. 1253, Bahía Blanca 8000, Argentina.
INMABB UNS-CONICET, Alem Av. 1253, Bahía Blanca 8000, Argentina.
Math Biosci Eng. 2021 Nov 3;18(6):9579-9605. doi: 10.3934/mbe.2021470.
The management of the collection of Municipal Solid Waste is a complex task for local governments since it consumes a large portion of their budgets. Thus, the use of computer-aided tools to support decision-making can contribute to improve the efficiency of the system and reduce the associated costs, especially in developing countries, which usually suffer from a shortage of resources. In the present work, a simulated annealing algorithm is proposed to address the problem of designing the routes of waste collection vehicles. The proposed algorithm is compared to a commercial solver based on a mixed-integer programming formulation and two other metaheuristic algorithms, i.e., a state-of-the-art large neighborhood search and a genetic algorithm. The evaluation is carried out on both a well-known benchmark from the literature and real instances of the Argentinean city of BahȪa Blanca. The proposed algorithm was able to solve all the instances, having a performance similar to the large neighborhood procedure, while the genetic algorithm showed the worst results. The simulated annealing algorithm was also able to improve the solutions of the solver in many instances of the real dataset.
城市固体废物的管理对地方政府来说是一项复杂的任务，因为它消耗了他们预算的很大一部分。因此，使用计算机辅助工具来支持决策可以有助于提高系统的效率并降低相关成本，特别是在发展中国家，这些国家通常资源短缺。在目前的工作中，提出了一种模拟退火算法来解决设计废物收集车辆路线的问题。所提出的算法与基于混合整数规划公式的商业求解器以及另外两种元启发式算法（即最先进的大邻域搜索和遗传算法）进行了比较。评估是在文献中的一个著名基准和阿根廷巴伊亚布兰卡市的真实实例上进行的。所提出的算法能够解决所有实例，其性能与大邻域程序相似，而遗传算法的结果最差。模拟退火算法还能够在真实数据集的许多实例中改进求解器的解决方案。
