Application of Fuzzy Sets to Aggregate Production Planning With Multiproducts and Multitime Periods

ArticleinIEEE Transactions on Fuzzy Systems 19(3):465 - 477 · July 2011with5 Reads
Impact Factor: 8.75 · DOI: 10.1109/TFUZZ.2011.2114668 · Source: IEEE Xplore
Abstract

The objective of this study is to develop a fuzzy mathematical programming method to solve aggregate production planning (APP) decision problems that involve multiproducts and multitime periods in a fuzzy environment. The fuzzy APP model that is developed here attempts to minimize total cost with respect to inventory carrying levels, available labor levels, machine capacity and warehouse space, and the constraint of available budget. The proposed APP method evaluates monetary interest of related operating cost categories and provides greater computational efficiency and flexibility by adopting triangular fuzzy numbers and piecewise linear membership functions to represent both imprecise data and fuzzy goals. The actual performance of an industrial company was used to demonstrate the feasibility of applying the proposed method to real-world APP decisions. The proposed method yields an efficient solution and presents overall decision-maker satisfaction with the given goal values. This paper also presents several significant management implications that are related to the practical application of the proposed method.

    • "In this study, fuzzy model for production planning is considered, which is also extensively used in the literature. Liang et al. (2011), for instance, developed a fuzzy mathematical programming method to solve aggregate production planning decision problems that involve multi products and multi time periods in a fuzzy environment. The study evaluates monetary interest of related operating cost categories and provides greater computational efficiency and flexibility by adopting triangular fuzzy numbers and piecewise linear membership functions to represent both imprecise data and fuzzy goals. "
    Full-text · Article · Jan 2016
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    • "More complicated and realistic cases must be further tested. For example, the proposed operators may be used in the context of sensitivity analysis [36], solving of uncertain linear and nonlinear systems [69], uncertain aggregation operators [7], solving of optimization problems [39], solving of multiple-criteria decision-making problems [13,35,47,48], inverse control design [5,6,10], determination of cluster centers for linguistic fuzzy C-means [2], and aggregation of Sugeno-like rule consequents [70]. Table A.1 Interpretation of the indicator Ξ (λ). "
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    Full-text · Article · Mar 2012 · IEEE Transactions on Fuzzy Systems
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