Strong and efficient filtering algorithms for scheduling constraints

针对调度约束的强大且高效的过滤算法

• 批准号: RGPIN-2016-05953
• 负责人: Quimper, ClaudeGuy
• 金额: $ 2.26万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684083
• 关键词: Strong; efficient; filtering; algorithms; scheduling

Scheduling is the process of determining in what order a collection of operations, tasks, or activities should be executed so that the resources required for their executions are not overloaded. The tasks are generally subject to a variety of constraints and the problem comes with an optimization criteria. Scheduling problems are generally NP-Hard and require special techniques to be efficiently solved. Constraint Programming (CP) is a technique issued from artificial intelligence that proved itself very efficient to solve scheduling problems.******Despite recent advances, industrial problems remain hard to solve. Due to long computation times, solvers are halted before the optimal solution is found and therefore return sub-optimal schedules that can cause delays in airports or idle times on an assembly line. These inconveniences would be avoided if faster solvers were developed.******The success of constraint programming for solving scheduling problems comes from its filtering algorithms that reason over the scheduling constraints to prune the search space. These algorithms apply several filtering rules based on a relaxation of the scheduling problem. If the relaxed version of the scheduling problem forbids a task to start at a given time, the solver can safely discard these solutions and spend time exploring another part of the search space. By improving the relaxation used by the filtering rules, it is possible to filter larger portions of the search space and therefore to speed up the solving process.******The long-term objective of this program is to increase the speed of constraint-based schedulers to find large and complex optimal schedules in a reasonable time. This is achieved by fulfilling 3 sub-objectives.***1) Designing filtering algorithms based on stronger relaxations that achieve more filtering than existing ones;***2) Designing filtering algorithms based on relaxations that are aware of the objective criterion;***3) Designing faster filtering algorithms.******We propose a research program that will fully train 2 new Ph.D. students, 3 new master students, and allow one actual Ph.D. student to complete his thesis. Moreover, this program offers 3 internships for undergrad students.******One master and one doctoral student will develop stronger relaxations that will offer a better pruning of the search space. One new Ph.D. and one finishing Ph.D. student will work on filtering rules that are adapted to the objective criterion. Finally, two master students will work on faster algorithms that enforce existing filtering rules.**

调度是确定一组操作、任务或活动应该以什么顺序执行的过程，以便它们执行所需的资源不会超载。这些任务通常会受到各种约束，并且该问题带有一个优化标准。调度问题通常是NP难的，需要特殊的技术才能有效地解决。约束编程(CP)是人工智能提出的一种技术，它被证明是解决调度问题的非常有效的方法。尽管最近取得了进展，但工业问题仍然很难解决。由于计算时间较长，求解器在找到最优解之前停止，因此返回可能导致机场延误或装配线上空闲时间的次优调度。如果开发出更快的求解器，这些不便就可以避免。*约束编程解决调度问题的成功来自于其过滤算法，该算法通过对调度约束进行推理来修剪搜索空间。这些算法基于调度问题的松弛应用了几个过滤规则。如果调度问题的宽松版本禁止任务在给定时间开始，求解器可以安全地丢弃这些解，并花费时间探索搜索空间的另一部分。通过改进过滤规则所使用的松弛，可以过滤更大部分的搜索空间，从而加快求解过程。该程序的长期目标是提高基于约束的调度器的速度，以便在合理的时间内找到大型且复杂的最优调度。这是通过实现三个子目标来实现的。*1)设计基于更强的松弛的过滤算法，实现比现有的更多的过滤；*2)基于了解客观标准的松弛设计过滤算法；*3)设计更快的过滤算法。*我们提出了一项研究计划，将全面培养2名新的博士生和3名新的硕士研究生，并允许1名实际的博士生完成他的论文。此外，该项目还为本科生提供了3个实习机会。一名硕士和一名博士生将培养出更强的放松能力，从而更好地修剪搜索空间。一名新博士和一名即将毕业的博士生将致力于过滤规则，以适应客观标准。最后，两名硕士学生将研究更快的算法，以实施现有的过滤规则。**