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Constraint-based assembly line sequencing.

机译:基于约束的组装线排序。

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A wide variety of combinatorial optimization problems have been studied in recent years. Of particular interest are a class of optimization problems arising from the manufacturing of vehicles on assembly lines. These problems consist of sequencing the vehicles that are going to be produced such that their production is done in an efficient cost effective manner. In this thesis we introduce a real-world vehicle sequencing problem that was provided by TigrSoft, who solved the problem for one of their clients using a greedy search approach. We began by modeling this problem as a constraint satisfaction problem and from there we devised three different solution techniques for solving it. These solution techniques include a simple hill-climbing algorithm, a backtracking algorithm with parameterized soft constraints, and a branch and bound algorithm that is capable of finding optimal solutions. We were able to improve results, compared to TigrSoft's algorithm, using any of these three solution techniques. For our best method, a branch and bound technique with a decomposition into smaller sub-problems, we obtained improvements ranging between 3% and 13% for six real-world problem instances.
机译:近年来,已经研究了各种各样的组合优化问题。特别令人关注的是在装配线上制造车辆引起的一类优化问题。这些问题包括对将要生产的车辆进行排序,以便以有效的成本效益的方式完成其生产。在本文中,我们介绍了由TigrSoft提供的现实世界中的车辆排序问题,后者使用贪婪的搜索方法为其客户之一解决了该问题。我们首先将此问题建模为约束满足问题,然后设计了三种不同的解决方案来解决该问题。这些解决方案技术包括简单的爬山算法,具有参数化软约束的回溯算法以及能够找到最佳解决方案的分支定界算法。与TigrSoft的算法相比,使用这三种解决方案技术中的任何一种,我们都能改善结果。对于我们最好的方法,一种将分支分解为较小的子问题的分支定界技术,对于六个实际问题实例,我们获得了介于3%和13%之间的改进。

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