Received: 6 June 2019 Revised: 11 September 2019 Accepted: 9 October 2019 DOI: 10.1111/coin.12251 SPECIAL ISSUE ARTICLE Efficiency analysis for stochastic dynamic facility layout problem using meta-heuristic, data envelopment analysis and machine learning Akash Tayal 1 Utku Kose 2 Arun Solanki 3 Anand Nayyar 4 José Antonio Marmolejo Saucedo 5 1 Department of Electronic and Communication, Indira Gandhi Delhi Technical University for Women, Delhi, India 2 Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey 3 Department of CSE, School of ICT, Gautam Buddha University, Greater Noida, India 4 Graduate School, Duy Tan University, Da Nang, Vietnam 5 Faculty of Engineering, Universidad Panamericana, CDMX, Mexico Correspondence Utku Kose, Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey. Email: utkukose@sdu.edu.tr Abstract The facility layout problem (FLP) is a combinato- rial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedu- ral design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three-stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learn- ing (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simu- lated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta-heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (effi- ciency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA Computational Intelligence. 2019;1–31. wileyonlinelibrary.com/journal/coin © 2019 Wiley Periodicals, Inc. 1