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Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem

  • Big Data Analytics in Operations & Supply Chain Management
  • Published:
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Abstract

Manufacturing industries have become larger, diverse and the factors affecting a facility layout design have grown rapidly. Handling and evaluating these large set of criteria (factors) is difficult in designing and solving facility layout problems. These factors and uncertainties have a large impact on manufacturing time, manufacturing cost, product quality and delivery performance. In order to operate efficiently, these facilities should adapt to these variations over multiple time periods and this must be addressed while designing an optimal layout. This paper proposes a novel integrated framework by combining Big Data Analtics and Hybrid meta-heuristic approach to design an optimal facility layout under stochastic demand over multiple periods. Firstly, factors affecting a facility layout design are identified. The survey is conducted to generate data reflecting 3V’s of Big Data. Secondly, a reduced set of factors are obtained using Big Data Analytics. These reduced set of factors are considered to mathematically model a weighted aggregate objective for Multi-objective Stochastic Dynamic Facility Layout Problem (MO-SDFLP). Hybrid Meta-heuristic based on Firefly (FA) and Chaotic simulated annealing is used to solve the MO-SDFLP. To show the working methodology of proposed integrated framework an exemplary case is presented.

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Authors and Affiliations

  1. Department of Electronics and Communication, Indira Gandhi Delhi Technical University for Women, Delhi, India

    Akash Tayal

  2. Department of Management Studies, Indian Institute of Technology, Delhi, India

    Surya Prakash Singh

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  1. Akash Tayal
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  2. Surya Prakash Singh
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Correspondence to Surya Prakash Singh.

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Tayal, A., Singh, S.P. Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem. Ann Oper Res 270, 489–514 (2018). https://doi.org/10.1007/s10479-016-2237-x

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  • DOI: https://doi.org/10.1007/s10479-016-2237-x

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