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Constraint Programming Model for Multi-Manned Assembly Line Balancing Problem
Computers & Operations Research ( IF 4.1 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.cor.2020.105069
Zeynel Abidin Çil , Damla Kizilay

Abstract In recent years, the multi-manned assembly line has become popular since the large-sized products allow more than one operator working simultaneously on the same product in a workstation. This line usually occurs in large-size products such as cars, buses, trucks, and so on. The multi-manned assembly line offers several advantages, such as fewer number of workers/workstation and less cycle time to improve the performance of the system. However, it has been analyzed by a few papers in literature due to being a relatively new and complex problem. The current study aims to develop an efficient exact solution approach, constraint programming, to solve from small to large-size problems by minimizing the cycle time as a primary objective and the total number of workers as a secondary objective. First, two mixed-integer linear programming (MILP) models are proposed based on previous studies to solve the small test cases of the problem optimally. However, the models are not capable of solving the large-size test instances. Therefore, a constraint programming (CP) model is formulated to address both small and large-size data sets. The results of the CP model are compared with two MILP models and two heuristic algorithms available in the literature. The computational results indicate that the CP model discovers optimal solutions, approximately 90% of all the instances, and small optimality gaps in the remaining instances. It is useful to highlight that the CP model is highly concise and solved by a black-box, commercial solver.

中文翻译:

多人装配线平衡问题的约束规划模型

摘要 近年来,由于大型产品允许多个操作员在一个工作站中同时处理同一产品,因此多人装配线变得流行起来。这条线通常出现在大尺寸产品中,如汽车、公共汽车、卡车等。多人装配线提供了几个优点,例如更少的工人/工作站和更少的循环时间,以提高系统的性能。然而,由于它是一个相对较新和复杂的问题,因此在文献中已经有几篇论文对其进行了分析。目前的研究旨在开发一种有效的精确求解方法,即约束规划,通过将周期时间最小化作为主要目标,将工人总数作为次要目标来解决从小到大的问题。第一的,在前人研究的基础上提出了两种混合整数线性规划(MILP)模型,以优化解决问题的小测试用例。但是,这些模型无法解决大型测试实例。因此,制定了约束规划 (CP) 模型来处理小型和大型数据集。CP 模型的结果与文献中可用的两个 MILP 模型和两个启发式算法进行了比较。计算结果表明,CP 模型发现了最优解,大约占所有实例的 90%,其余实例的最优性差距很小。强调 CP 模型高度简洁并且由黑盒商业求解器求解是很有用的。模型无法解决大型测试实例。因此,制定了约束规划 (CP) 模型来处理小型和大型数据集。CP 模型的结果与文献中可用的两个 MILP 模型和两个启发式算法进行了比较。计算结果表明,CP 模型发现了最优解,大约占所有实例的 90%,其余实例的最优性差距很小。强调 CP 模型高度简洁并且由黑盒商业求解器求解是很有用的。模型无法解决大型测试实例。因此,制定了约束规划 (CP) 模型来处理小型和大型数据集。CP 模型的结果与文献中可用的两个 MILP 模型和两个启发式算法进行了比较。计算结果表明,CP 模型发现了最优解,大约占所有实例的 90%,其余实例的最优性差距很小。强调 CP 模型高度简洁并且由黑盒商业求解器求解是很有用的。CP 模型的结果与文献中可用的两个 MILP 模型和两个启发式算法进行了比较。计算结果表明,CP 模型发现了最优解,大约占所有实例的 90%,其余实例的最优性差距很小。强调 CP 模型高度简洁并且由黑盒商业求解器求解是很有用的。CP 模型的结果与文献中可用的两个 MILP 模型和两个启发式算法进行了比较。计算结果表明,CP 模型发现了最优解,大约占所有实例的 90%,其余实例的最优性差距很小。强调 CP 模型高度简洁并且由黑盒商业求解器求解是很有用的。
更新日期:2020-12-01
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