Cleaning debris in urban areas after major disasters is very relevant to inhabitants to recover from their effects. In natural disasters, an unexpected and large area can be affected. Moreover, the time and the costs to perform the cleaning operations can be very high. In this work, the integrated multi-period scheduling routing problem to clean debris (SRP-CD) after major disasters is investigated. The problem includes strategical (scheduling) and operational (routing) decisions and, considering complex issues such as two levels of synchronization between work-troops and dump trucks. The goal of SRP-CD is twofold: minimizing the number of days for the overall cleaning, in the strategical level; and minimizing the total costs of vehicles routes in the operational level. A new mathematical model based in a dynamic multi-flow formulation, constructive heuristics and Large Neighborhood Search (LNS)-based metaheuristics are proposed. Comparison experiments for the model and the approaches are carried out, to measure performance and robustness of the proposed methods. To the best of our knowledge, these are the first contributions in the literature for SRP-CD, including all aspects addressed here.

重大灾难发生后清理城市地区的垃圾对于居民从灾难中恢复过来非常重要。在自然灾害中，可能会影响意想不到的大面积区域。此外，执行清洁操作的时间和成本可能非常高。在这项工作中，研究了重大灾难后清除碎片的集成多周期调度路由问题（SRP-CD）。该问题包括战略（调度）和运营（路线）决策，并考虑复杂的问题，例如工作部队和自卸卡车之间的两级同步。SRP-CD 的目标有两个：在战略层面上最大限度地减少整体清洁的天数；并在运营层面最小化车辆路线的总成本。基于动态多流公式的新数学模型，提出了建设性启发式和基于大邻域搜索（LNS）的元启发式。对模型和方法进行了比较实验，以衡量所提出方法的性能和鲁棒性。据我们所知，这些是 SRP-CD 文献中的第一批贡献，包括此处讨论的所有方面。