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Multi-objective imperfect production inventory model in fuzzy rough environment via genetic algorithm

Author

Listed:
  • D.K. Jana
  • K. Maity
  • T.K. Roy

Abstract

In this paper, we concentrate on developing a fuzzy rough (Fu-Ro) multi-objective decision-making imperfect production inventory model via genetic algorithm. The imperfect items can be reworked in the same cycle. Next, we develop an effective algorithm to solve the fuzzy rough expected value multi-objective decision making model concerning the production inventory problem. Finally these are applied to practical production inventory problem in which all inventory costs, purchasing and holding cost in the objectives and constraints are assumed to be fuzzy rough in nature. In addition, the technique of fuzzy rough simulation is applied to deal with general fuzzy rough objective functions and fuzzy rough constraints which are usually difficult to convert into their equivalents. Furthermore, combined with the techniques of fuzzy rough expected value model, a multi-objective genetic algorithm (MOGA) using the compromise approach is designed for solving a fuzzy rough multi-objective programming problem. Finally, a model is applied to an imperfect production inventory problem to illustrate the usefulness of the proposed model.

Suggested Citation

  • D.K. Jana & K. Maity & T.K. Roy, 2013. "Multi-objective imperfect production inventory model in fuzzy rough environment via genetic algorithm," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 18(4), pages 365-385.
    • Handle: RePEc:ids:ijores:v:18:y:2013:i:4:p:365-385
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    Citations

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    Cited by:

    1. Ashoke Kumar Bera & Dipak Kumar Jana, 2017. "Multi-item imperfect production inventory model in Bi-fuzzy environments," OPSEARCH, Springer;Operational Research Society of India, vol. 54(2), pages 260-282, June.
    2. Subhendu Ruidas & Mijanur Rahaman Seikh & Prasun Kumar Nayak, 2022. "A production-repairing inventory model considering demand and the proportion of defective items as rough intervals," Operational Research, Springer, vol. 22(3), pages 2803-2829, July.

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