Towards Automatic Facility Layout Design Using Reinforcement Learning

Abstract

The accuracy and perfection of layout designing significantly depend on the designer's ability. Quick and nearoptimal designs are very difficult to create. In this study, we propose an automatic design mechanism that can more easily design layouts for various unit groups and sites using reinforcement learning. Specifically, we devised a mechanism to deploy units to be able to fill the largest rectangular space in the current site. We aim to successfully deploy given units within a given site by filling a part of the site. We apply the mechanism to the three sets of units in benchmark problems. The performance was evaluated by changing the learning parameters and iteration count. Consequently, it was possible to produce a layout that successfully deployed units within a given one-floor site.

Automated Code Generation from Use cases and the Domain Model

“…In order to build the USLSCG generator, we implement two modules, Module (1) and Module (2) as shown Within this context, source code classes are often manually built from design class diagrams of use cases. In order to automatically generate source code classes from a use case model to reduce cost when software requirements are changed, several approaches [10], [11], [12], [13] have been proposed. Sunitha el al.…”