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Towards automatic facility layout design using reinforcement learning

Ikeda Hikaru, Nakagawa Hiroyuki, Tsuchija Tatsuhiro

Annals of Computer Science and Information Systems

2022

Vol. 32

11--20

The accuracy and perfection of layout designing significantly depend on the designer's ability. Quick and near-optimal designs are very difficult to create. In this study, we proposed an automatic design mechanism that can more easily design layouts for various unit groups and sites using reinforcement learning. Accordingly, 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.

Finding Minimum Locating Arrays Using a CSP Solver

Konishi Tatsuya, Kojima Hideharu, Nakagawa Hiroyuki, Tsuchiya Tatsuhiro

Fundamenta Informaticae

2020

Vol. 174, nr 1

27--42

Combinatorial interaction testing is an efficient software testing strategy. If all interactions among test parameters or factors needed to be covered, the size of a required test suite would be prohibitively large. In contrast, this strategy only requires covering t-wise interactions where t is typically very small. As a result, it becomes possible to significantly reduce test suite size. Locating arrays aim to enhance the ability of combinatorial interaction testing. In particular, (1̅ , t) -locating arrays can not only execute all t-way interactions but also identify, if any, which of the interactions causes a failure. In spite of this useful property, there is only limited research either on how to generate locating arrays or on their minimum sizes. In this paper, we propose an approach to generating minimum locating arrays. In the approach, the problem of finding a locating array consisting of N tests is represented as a Constraint Satisfaction Problem (CSP) instance, which is in turn solved by a modern CSP solver. The results of using the proposed approach reveal many (1̅ , t) -locating arrays that are smallest known so far. In addition, some of these arrays are proved to be minimum.