论文标题
在线打印商店调度问题的混合整数线性编程和约束编程模型
Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem
论文作者
论文摘要
在这项工作中,考虑了在线打印商店的调度问题。当今印刷行业中出现的这个具有挑战性的实际问题可以看作是一个灵活的车间调度问题,具有序列灵活性,其中工作的操作之间的优先限制由任意定向的无循环图提供。此外,几种复杂的特殊性,例如机器的不可用时期,可重新操作,依赖序列的设置时间,具有优先级约束的操作之间的部分重叠,释放时间和固定操作中也存在。在目前的工作中,提出了最小化MakePAN的混合整数线性编程和约束编程模型。对问题进行建模是双重的。一方面,问题是精确定义的。另一方面,分析了商业软件解决模型的功能和局限性。提出了具有小型,中和大型实例的广泛数值实验。数值实验表明,在考虑混合整数线性编程模型时,商业求解器能够最佳地求解小型实例的一小部分。尽管在考虑问题的约束编程公式时,所有小型和一部分中型实例都可以最佳地解决。此外,商业求解器能够为实践中出现的实例大小的大型实例提供可行的解决方案。
In this work, the online printing shop scheduling problem is considered. This challenging real problem, that appears in the nowadays printing industry, can be seen as a flexible job shop scheduling problem with sequence flexibility in which precedence constraints among operations of a job are given by an arbitrary directed acyclic graph. In addition, several complicating particularities such as periods of unavailability of the machines, resumable operations, sequence-dependent setup times, partial overlapping among operations with precedence constraints, release times, and fixed operations are also present in the addressed problem. In the present work, mixed integer linear programming and constraint programming models for the minimization of the makespan are presented. Modeling the problem is twofold. On the one hand, the problem is precisely defined. On the other hand, the capabilities and limitations of a commercial software for solving the models are analyzed. Extensive numerical experiments with small-, medium-, and large-sized instances are presented. Numerical experiments show that the commercial solver is able to optimally solve only a fraction of the small-sized instances when considering the mixed integer linear programming model; while all small-sized and a fraction of the medium-sized instances are optimally solved when considering the constraint programming formulation of the problem. Moreover, the commercial solver is able to deliver feasible solutions for the large-sized instances that are of the size of the instances that appear in practice.