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

D.K. Jana, K. Maity and T.K. Roy

International Journal of Operational Research, 2013, vol. 18, issue 4, 365-385

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.

Keywords: inventory modelling; multi-objective genetic algorithms; MOGAs; fuzzy sets; rough sets; imperfect production; reworking; fuzzy logic; decision making; multi-objective programming. (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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