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Heuristics for Robots-Humans Tasks Assignment in a Containers Loading Center

机译:集装箱装载中心中机器人-人的任务分配的启发式

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To improve working conditions at sorting centers and to reduce the burden, La Poste wants to automate a part of the container-handling process. This mainly concerns containers that belong to the destinations with highest traffic since they demand an important effort and time amount from the operators especially in critical times like truck-departure times, when the operators must quickly load the containers in carts to be transported in trucks so that no delay will occur to the delivery date of the mail items. In this paper, we give a mixed integer linear program to assign the tasks to the robots, in order to maximize the workload of the robots and minimize the effort made by the operators. In addition, a greedy heuristic is proposed. Experiments performed on realistic data confirm the performance of the greedy algorithm. In addition, a sensitivity analysis of the heuristic is given to test its robustness on noisy data.
机译:为了改善分拣中心的工作条件并减轻负担,La Poste希望使容器处理过程的一部分自动化。这主要涉及到属于最高流量目的地的集装箱,因为它们需要操作员付出大量的精力和时间,尤其是在诸如卡车出发时间等关键时刻,此时操作员必须将集装箱快速地装进推车中以便用卡车运输。不会延迟邮件项目的交付日期。在本文中,我们给出了一个混合整数线性程序来将任务分配给机器人,以最大程度地增加机器人的工作量并最大程度地减少操作员的工作量。另外,提出了贪婪启发式算法。对真实数据进行的实验证实了贪婪算法的性能。此外,还对启发式算法进行了敏感性分析,以测试其在嘈杂数据上的鲁棒性。

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