Modeling and Solution of Realistic Facility Layout Problems Involving Random Elements

在这个项目中，我们提出了一个明确考虑生产效率的设施布局问题的新框架。在设施布局问题的经典框架中，布局只通过物料搬运成本来评估，而没有考虑布局如何影响生产效率的任何方面。然而，在实际系统中，设施的布局确实会影响生产效率，如吞吐量、提前期等。因此，一般而言，应该存在“最佳”布局，它吸收了系统中涉及的变异性，并达到最高的生产效率。简而言之，我们的问题是找到能产生最大生产效率的设施布局。我们将这种类型的布局问题称为随机设施布局问题(SFLP)。例如，在缓冲区空间的物理大小与设施本身相比不可忽略的情况下，将缓冲区空间分配给设施会影响距离…设施之间有更多。在这种情况下，生产效率取决于缓冲空间分配和设施布局。我们的问题针对这种情况给出了合理的解决方案。在第一个模型中，我们选择制造提前期作为绩效指标，建立了作业车间型生产系统的最优设施布局和缓冲空间分配问题，其中相对较大尺寸的零件由AGV转移并以FCFS方式通过可变路径进行加工。我们的方法基于近似马尔可夫分析，使用分解技术来评估提前期，并使用遗传算法来搜索缓冲区空间分配和布局的最优组合。在第二个模型中，我们考虑具有前馈配置和可变处理时间的生产系统。我们的目标是有效地找到帕累托最优的生产量和物料搬运成本解决方案。我们假设工作转移是由皮带传送带或无限车辆完成的。由于在这些假设下，设施布局不影响吞吐量，因此我们首先使用马尔科夫分析来计算每个缓冲区空间分配的吞吐量。然后利用遗传算法搜索帕累托最优设施布局和缓冲区空间分配集。较少

In this project, we propose a new framework of facility layout problems which takes the production efficiency into account explicitly. In the classical framework of facility layout problems, layouts are evaluated only by the material handling cost, and any aspects as to how layouts affect the production efficiencies are not considered. In real systems, however, the layout of facilities do affect the production efficiencies such as throughput, lead time and so on. Thus, in general, there should exist the "best" layout which absorbs the variability involved in the system and attains the highest production efficiency. In short, our problem is to find the layout of facilities which results in the maximal production efficiency. We refer this type of layout problems as the "Stochastic Facility Layout Problem (SFLP)". For examples, in case when the physical size of a buffer space can not be ignored compared with the facility itself, allocating buffer spaces to a facility affects the distances … More between facilities. In such a case, the production efficiency depends on both buffer space allocation and facility layout in a complex way. Our problem gives rational solutions for such a situation.In the first model, we choose the manufacturing lead time as the performance measure, formulate an optimal facility layout and buffer space allocation problem for job-shop type production systems in which relatively large size parts are transfered by AGVs and processed via variable routes in FCFS manner. Our approach is based on an approximate Markov analysis using a decomposition technique for evaluating the lead time and the genetic algorithm for searching the optimal combination of buffer space allocation and layout. In the second model, we consider production systems with a feed-forward configuration and variable processing times. Our objective is to efficiently find Pareto optimal solutions for both throughput and material handling cost. We assume that work transfer is performed by belt-conveyors or unlimited vehicles. Since the facility layout does not affect the throughput under these assumptions, we first calculate the throughput for every buffer space allocation using Markov analysis. Then the set of Pareto optimal facility layouts and buffer space allocations is searched using a Genetic Algorithm. Less