Combiner l'intelligence artificielle et la recherche opérationnelle pour optimiser les horaires d'équipages aériens et de chauffeurs d'autobus

AD OPT和Giro解决的调度问题具有相似的结构。它们包括以最低的成本用员工的时间表来完成一系列任务。在航空运输业中，机组结对问题的目标是每个航班轮换一次机组人员，机组编组问题的目标是每个航班轮换一次机组人员，机组编组问题的目标是每个航班轮换一次机组人员，以保证每个航班都有所需的人员。在公共交通行业中，日常问题的目标是司机工作日一次覆盖每条公交路线。所有这些问题都可以用包含大量变量和约束的集合覆盖模型来表述。我们的团队开发的算法可以解决这些问题，而无需同时考虑所有变量，并且通过初始聚合可能在解决方案中一个接一个出现的任务来减少约束的数量。为了达到最优性，如果需要，可以在解析期间修改此聚合。这些算法被用于解决航空公司机组人员配对问题，其中每个任务必须涉及一次。该项目的目标如下：-开发机器学习方法，以便从前几个月的解决方案中提取有关解决方案中可能一个接一个出现的任务的信息。-将这些算法进行推广，扩大应用范围：以团队的形式处理公交司机问题和空乘人员的月度阻塞问题，同时优化结对和月度排班。-实现能够处理商业问题的算法。该项目将给AD OPT和Giro带来竞争优势，它们将拥有比竞争对手更好的软件。

The scheduling problems addressed by AD OPT and Giro have similar structures. They consist in covering at minimal cost a set of tasks with employee schedules. In the air transportation industry, the crew pairing problem aims at covering each flight with crew rotations once, and the crew rostering problem attempts to cover each crew rotation with monthly blocks many times in order to guarantee that each flight has the required staff. In the public transportation industry, the daily problem aims at covering each bus route with drivers' workdays once.All these problems can be formulated with set covering models that contain a very large number of variables and constraints. Our team developed algorithms that solve these problems without considering all the variables at the same time and that reduce the number of constraints by initially aggregating tasks that are likely to come one after the other in a solution. In order to reach optimality, this aggregation can be modified during the resolution if needed. These algorithms were put to use on airline crew pairing problems, in which each task has to be covered once. The objectives of this project are as follows:- Develop machine learning methods in order to extract from solutions of previous months information about the tasks that are likely to come one after the other in a solution.- Generalize these algorithms so as to widen their scope of application: dealing with bus drivers problems and with monthly blocks problems of flight attendants working as a team, and simultaneously optimizing pairing and monthly rostering.- Make implementations of the algorithms that are able to deal with commercial problems.This project will give a competitive advantage to AD OPT and Giro, which will have at their disposal better software than their competitors.