Use of the EOQ model for inventory analysis
Production and Inventory Management
Vertex method for computing functions of fuzzy variables
Fuzzy Sets and Systems
Fuzzy set theoretic interpretation of economic order quantity
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy arithmetic with requisite constraints
Fuzzy Sets and Systems - Special issue: fuzzy arithmetic
Fuzzy production inventory for fuzzy product quantity with triangular fuzzy number
Fuzzy Sets and Systems
Fuzzy economic production for production inventory
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Operations Management
Fuzzy EPQ models for an imperfect production system
International Journal of Systems Science
Applying fuzzy weighted average approach to evaluate office layouts with Feng-Shui consideration
Mathematical and Computer Modelling: An International Journal
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The purpose of this paper is to investigate and propose a fuzzy extended economic production quantity model based on an elaboratively modeled unit cost structure. This unit cost structure consists of the various lot-size correlative components such as on-line setups, off-line setups, initial production defectives, direct material, labor, and depreciation in addition to lot-size non-correlative items. Thus, the unit cost is correlatively modeled to the production quantity. Therefore, the modeling or the annual total cost function developed consists of not only annual inventory and setup costs but also production cost. Moreover, via the concept of fuzzy blurred optimal argument and the vertex method of the @a-cut fuzzy arithmetic (or fuzzy interval analysis), two solution approaches are proposed: (1) a fuzzy EPQ and (2) a compromised crisp EPQ in the fuzzy sense. An optimization procedure, which can simultaneously determine the @a-cut-vertex combination of fuzzy parameters and the optimizing decision variable value, is also proposed. The sensitivity model for the fuzzy total cost and thus EPQ to the various cost factors is provided. Finally, a numerical example with the original data collected from a firm demonstrates the usefulness of the new model.