Handling qualitative aspects in Unequal Area Facility Layout Problem: An Interactive Genetic Algorithm

The Unequal Area Facility Layout Problem (UA-FLP) has been addressed using several methods. However, the UA-FLP has only been solved for criteria that can be quantified. Our approach includes subjective features in the UA-FLP, which are difficult to take into account with a more classical heuristic optimization. In this respect, we propose an Interactive Genetic Algorithm (IGA) that allows an interaction between the algorithm and the Decision Maker (DM). Involving the DM's knowledge in the approach guides the search process, adjusting it to his/her preferences at each generation of the algorithm. In this paper, we are concerned with assisting the DM in finding a good solution according with criteria that can be: subjective, unknown at the beginning or changed during the process, so that, the problem addressed differs from a classic optimization problem. In order to avoid overloading the DM, the whole population is classified into clusters by the fuzzy c-means clustering algorithm and only one representative element of each cluster is directly evaluated by the DM. A memory of the best solutions chosen by the DM is kept as a reference. The tests carried out show that the proposed IGA is capable of capturing DM preferences.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► We are concerned with an Unequal Area Facility Layout Problem. ► Existing approaches are based on optimization, which cannot be relevant in certain cases. ► Our Interactive Genetic Algorithm involves the Decision Maker in the search for a suited solution. ► In the literature Interactive Meta-heuristics seem not discussed for this type of problem. ► A prototype implemented as a web application is illustrated with an example.