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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662799
• 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.**

调度是确定应执行操作，任务或活动的顺序的过程，以使执行所需的资源不会超载。这些任务通常受到各种约束的约束，问题符合优化标准。调度问题通常是NP固定的，需要有效解决特殊技术。约束编程（CP）是一种由人工智能发行的技术，证明自己非常有效地解决调度问题。由于计算时间很长，在找到最佳解决方案之前停止求解器，因此返回可能在装配线上导致机场或闲置时间延迟的次优计划。如果开发更快的求解器，将避免这些不便。******约束编程在解决调度问题上的成功来自其过滤算法，这些算法建议在调度约束上进行修剪搜索空间。这些算法根据调度问题的放松应用了几种过滤规则。如果调度问题的放松版本禁止在给定时间开始任务，则求解器可以安全地丢弃这些解决方案，并花时间探索搜索空间的另一部分。通过改善过滤规则所使用的放松，可以过滤搜索空间的较大部分，从而加快求解过程。 *** 1）基于更强的放松算法设计过滤算法来实现这一点。学生，3个新硕士学生，并允许一位实际的博士学位。学生完成他的论文。此外，该计划为本科生提供3个实习。一项新博士还有一位博士学位学生将研究适合客观标准的过滤规则。最后，两名硕士学生将使用实施现有过滤规则的更快算法。**