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Simulated Annealing-Based Embedded Meta-Heuristic Approach to Solve Bi-objective Robust Stochastic Sustainable Cellular Layout

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  • Ravi Kumar

    (Indian Institute of Technology Delhi)

  • Surya Prakash Singh

    (Indian Institute of Technology Delhi)

Abstract

The increase in competitiveness and environmental challenges drive manufacturing industry to change in layout frequently. Moreover, due to environmental challenges, use of scarce resources like electrical energy consumption (EEC) is a major issue. To tackle these rapidly changing conditions, a layout must be efficient and effective. This work proposes a bi-objective robust stochastic sustainable cellular facility layout mathematical model and embedded meta-heuristic to solve it. The proposed model simultaneously minimizes material handling cost for both inter/intra-cellular movement and EEC in cellular manufacturing systems. In today’s sensitive market, demand fluctuates very frequently, so a layout must be robust so that it can tackle all these market fluctuations. To cure the problem, robust facility layout has been designed and solved using simulated annealing (SA)-based embedded meta-heuristic. To show the validity of proposed SA-based embedded meta-heuristic, twenty-five data instances have been used and solved.

Suggested Citation

  • Ravi Kumar & Surya Prakash Singh, 2018. "Simulated Annealing-Based Embedded Meta-Heuristic Approach to Solve Bi-objective Robust Stochastic Sustainable Cellular Layout," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 69-93, March.
    • Handle: RePEc:spr:gjofsm:v:19:y:2018:i:1:d:10.1007_s40171-017-0174-4
    DOI: 10.1007/s40171-017-0174-4
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    References listed on IDEAS

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    Cited by:

    1. Kuldeep Lamba & Ravi Kumar & Shraddha Mishra & Shubhangini Rajput, 2020. "Sustainable dynamic cellular facility layout: a solution approach using simulated annealing-based meta-heuristic," Annals of Operations Research, Springer, vol. 290(1), pages 5-26, July.

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