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

IEEE Transactions on Fuzzy Systems (Impact Factor: 6.31). 07/2011; 19(3):465 - 477. 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.

  • [Show abstract] [Hide abstract]
    ABSTRACT: This work develops a possibilistic programming method to solve multi-product and multi-time period production/distribution planning decision PDPD problems involving imprecise goals, forecast demand and cost/time coefficients in uncertain environments. The proposed method attempts to simultaneously minimize total production and distribution costs and total delivery time in relation to inventory levels, available machine capacity and labor levels at each source, as well as forecast demand and available warehouse space at each destination, and considering the constraint of available budget. An industrial case is used to demonstrate the feasibility of applying the proposed method to real-world PDPD problems. Moreover, several significant management implications regarding the practical application of the proposed method are presented.
    Journal of Intelligent and Fuzzy Systems 01/2013; 25(1):219-230. DOI:10.3233/IFS-2012-0629 · 0.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we consider a hybrid 'Make-to-Stock-Make-to-Order' environment to develop a novel optimisation model for medium-term production planning of a typical multi-product firm based on the competencies of the robust optimisation methodology. Three types of uncertainties: suppliers, processes and customers, are incorporated into the model to construct a robust practical model in an uncertain business environment. The modelling procedure is started with applying deterministic linear programming to develop a new multi-objective approach for the combination of multi-product multi-period production planning and aggregate production planning problems. Then, the proposed deterministic model is transformed into a robust optimisation framework and the solution procedure is designed according to the Lp-Metric methodology. Next, using the IBM ILOG CPLEX optimisation software, the proposed model is evaluated by applying the data collected from an industrial case study. Final results illustrate the applicability of the proposed model.
    International Journal of Production Research 12/2014; 53(5):1-29. DOI:10.1080/00207543.2014.935828 · 1.32 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We combine the concepts of gradual numbers and Kaucher arithmetic on extended intervals to define extended gradual interval (EGI) arithmetic in which subtraction and division operators are the inverse operators of addition and multiplication, respectively. Use of the proposed EGI operators can lead to non-monotonic gradual intervals that are not fuzzy subsets and cannot be represented by fuzzy intervals. In this context and when fuzzy representation results are desired, an approximation strategy is proposed to determine the nearest fuzzy interval of the non-monotonic gradual interval obtained. This approximation is viewed as an interval regression problem according to an optimization procedure. The EGI operators are applied to the common fuzzy weighted average (FWA) leading to a gradual weighted average (GWA).
    Fuzzy Sets and Systems 12/2014; 257:67–84. DOI:10.1016/j.fss.2013.08.003 · 1.88 Impact Factor