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A multi-compartment capacitated arc routing problem with intermediate facilities for solid waste collection using hybrid adaptive large neighborhood search and whale algorithm

机译:使用混合自适应大邻域搜索和鲸鱼算法的带有中间设施的多舱室容性电弧路由问题,用于收集固体废物

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Municipal solid waste collection is an increasingly difficult task and has the highest operation cost in the solid waste management process; thus, finding the optimal routes for the waste collection is a most tactically significant decision that should be focused on due to the population growth. This research investigated the multi-compartment capacitated arc routing problem with intermediate facilities (MCCARPIF) in the context of solid waste collection. This problem has been researched rarely in the past in the real world. The MCCARPIF develops the capacitated arc routing problem (CARP) by considering both the multi-compartment vehicles and intermediate facilities together. In case of waste separation, the fleet of vehicles should have multiple parts to avoid mixing waste together. In developing countries, the process of separating the wastes is not carried out comprehensively, so this subject is almost new and research about it can improve their waste collection process. Due to the complexity of this model, two algorithms are developed to solve it: an adaptive large neighborhood search algorithm (ALNS) and the hybrid ALNS with whale optimization algorithm. Results showed that hybrid ALNS with whale optimization algorithm got higher quality solutions in comparison to ALNS. A real case study in one of the districts of Tehran municipality has been considered and the results obtained show that the use of multi-compartment vehicles is more cost-effective than the use of single-compartment vehicles, reducing the total distance traveled.
机译:城市固体废物的收集是一项日益艰巨的任务,在固体废物管理过程中具有最高的运营成本。因此,寻找最佳的废物收集途径是最重要的战略决策,应因人口增长而集中关注。本研究在固体废物收集的背景下研究了带有中间设施的多隔室电容电弧路由问题(MCCARPIF)。过去在现实世界中很少研究此问题。 MCCARPIF通过同时考虑多厢车辆和中间设施来发展电容电弧路由问题(CARP)。如果进行废物分类,则车队应具有多个零件,以避免将废物混合在一起。在发展中国家,废物的分离过程尚未全面进行,因此该主题几乎是新话题,对其进行的研究可以改善废物的收集过程。由于该模型的复杂性,开发了两种算法来解决它:自适应大邻域搜索算法(ALNS)和带有鲸鱼优化算法的混合ALNS。结果表明,与ALNS相比,采用鲸鱼优化算法的混合ALNS获得了更高质量的解决方案。已考虑在德黑兰市的一个地区进行实际案例研究,所获得的结果表明,使用多厢车辆比使用单厢车辆更具成本效益,从而减少了总行驶距离。

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