Automated Discovery and Utilization of Tacit Knowledge in Facility Layout Planning and Optimization

Abstract

This paper puts forth a novel methodology for facilities layout planning and optimization, where the fitness evaluation of layout alternatives is automatically performed by employing an artificial neural network trained to preferences of the domain experts. The inherently uncertain, unstructured, and often tacit nature of facilities layout design preferences, constraints, and fitness objectives demands the use of domain experts for the fitness evaluation of layout alternatives, which is a resource-intensive process. Indeed, the usual unavailability of domain experts in a timely or economical manner highlights the need for resorting to the use of some intelligent and effective automation technique in this important domain. In order to test the key novel ingredient of the proposed approach, a variety of artificial neural networks are trained on a large data set containing both qualitative and quantitative fitness values of layout alternatives, as well as subjective rankings by a seasoned domain expert utilizing the knowledge of the application domain. Simulation results strongly support the viability of the proposed idea. Such an automated approach to fitness evaluations of layout alternatives is expected to significantly increase the efficacy and efficiency of the overall facilities layout planning process. Moreover, such an approach would spur the much sought for research in decision support and expert systems in layout planning. As such, the paper also provides some very interesting and promising, albeit challenging, future research directions.

Learning from Prior Designs for Facility Layout Optimization

“…The method does not consider any explicit objectives or constraints on the generated layouts. Ahmad et al [11] train a neural network to reproduce expert evaluations of given layouts, and propose that the resulting model would be useful for optimization; however the network was limited to four quantitative measures of the layout as inputs, and as many as 500 manually evaluated layouts were used for training data.…”