Before each flight departs, baggage has to be loaded into containers, which are then forwarded to the airplane. Planning the loading process consists of setting the start times for the loading process and depletion of the baggage storage as well as assigning handling facilities and workers. Flight delays and uncertain arrival times of passengers at the check-in counters require plans that are adjusted dynamically every few minutes and, hence, an efficient planning procedure. We propose a model formulation and a solution procedure that utilize historical flight data to generate reliable plans in a rolling planning fashion, allowing problem parameters to be updated in each reoptimization. To increase the tractability of the problem, we employ a column generation–based heuristic in which new schedules and work profiles are generated in subproblems, which are solved as dynamic programs. In a computational study, we demonstrate the robust performance of the proposed procedure based on real-world data from a major European airport. The results show that (i) the procedure outperforms both a constructive heuristic that mimics human decision making and a meta heuristic (tabu search) and (ii) being able to dynamically (re)allocate baggage handlers leads to improved solutions with considerably fewer left bags.

中文翻译：

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机场行李处理操作的数据驱动方法

每次航班起飞前，行李都必须装入集装箱，然后再送到飞机上。规划装载过程包括设置装载过程的开始时间和行李存储的耗尽以及分配处理设施和工人。航班延误和乘客在值机柜台的不确定到达时间需要每隔几分钟动态调整一次计划，因此需要一个有效的计划程序。我们提出了一个模型制定和一个解决程序，利用历史飞行数据以滚动计划方式生成可靠的计划，允许在每次重新优化中更新问题参数。为了增加问题的可处理性，我们采用基于列生成的启发式方法，其中在子问题中生成新的时间表和工作配置文件，作为动态程序求解。在一项计算研究中，我们根据来自欧洲主要机场的真实数据证明了所提出程序的稳健性能。结果表明（i）该程序优于模拟人类决策的建设性启发式和元启发式（禁忌搜索），并且（ii）能够动态（重新）分配行李处理程序导致改进的解决方案显着减少遗留行李.