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An Integrated Decision-Support Workflow for Facility Layout Planning
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DOI：10.3390/asi9070156.png)
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Abstract
En 中文
Facility Layout Planning (FLP) remains a complex task for manufacturers seeking to improve productivity, reduce daily operating costs, and stay competitive in fast-changing markets. Traditional methods such as Systematic Layout Planning (SLP) offer useful guidelines for designing department layouts but still rely heavily on judgment and experience. At the same time, modern optimization and simulation techniques provide valuable quantitative insights. These techniques are often used separately rather than as part of an integrated process. In this work, a hybrid layout-planning approach that combines these techniques is developed and validated through an industrial case study, providing a practical decision-support process for facility layout planning. The process starts with SLP, which develops an initial layout using activity relationship charts, material-flow analysis, and handling-cost estimates. A simulation model then evaluates throughput, machine utilization, and work-in-progress, providing early indications of the layout’s real-world performance. A Genetic Algorithm (GA) is used to find improved configurations that reduce distances and costs. The optimized layouts are further tested through simulation. To demonstrate practical use, the framework was applied at a transformer manufacturing plant. It resulted in an approximately 35% reduction in material-handling costs. The results show that the optimized layout reduced material-handling costs from 7062.5 to approximately 4560 L.E. per transformer while increasing monthly throughput by 2.46% (approximately 11 transformers per month). Additionally, a what-if analysis was performed to identify opportunities for improvement, such as increasing production by using an automatic laser-cutting machine. The findings support data-driven decisions in facility layout design and long-term operational planning.
Keywords:
discrete event simulation
facility planning
genetic algorithm
optimization
SLP
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