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Optimisation of Assembly Line Balancing Type-E with Resource Constraints using NSGA-II

机译:使用NSGA-II优化具有资源约束的E型装配线平衡

摘要

Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that anyudassembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.
机译:Type-E问题的流水线平衡(ALB-E)试图将任务分配给生产线上的各个工作站,以便满足优先级关系并优化某些性能指标。最近在ALB-E中进行的大多数研究都假定可以将任何 udassembly任务分配给任何工作站。这种假设导致组装线所需资源的更高利用率。本研究针对单个模型研究带资源约束的Type-E问题的流水线平衡(ALBE-RC)。在这项工作中,考虑了三个目标功能,即最小化工作站数量,循环时间和资源数量。本文提出了一种Elitist非支配排序遗传算法(NSGA-II)来优化该问题。使用六个基准问题来测试优化算法,并将结果与​​多目标遗传算法(MOGA)和混合遗传算法(HGA)进行比较。通过计算测试,发现NSGA-II具有探索搜索空间的能力,具有更好的求解精度,并且具有均匀间隔的求解。今后,为了提高算法的性能,提出了提高求解精度的研究。

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