Multi-objective inventory models of deteriorating items with some constraints in a fuzzy environment
Computers and Operations Research
A fuzzy approach to the multiobjective transportation problem
Computers and Operations Research
Nearest interval approximation of a fuzzy number
Fuzzy Sets and Systems - Fuzzy intervals
A diffusion inventory model for deteriorating items
Applied Mathematics and Computation
Computers and Operations Research
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Necessity constraint in two plant optimal production problem with imprecise parameters
Information Sciences: an International Journal
Computers & Mathematics with Applications
Computers and Industrial Engineering
Optimal inventory policies for deteriorating complementary and substitute items
International Journal of Systems Science
A two warehouse supply-chain model under possibility/ necessity/credibility measures
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
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In this paper, realistic production-inventory models without shortages for deteriorating items with imprecise holding and production costs for optimal production have been formulated. Here, the rate of production is assumed to be a function of time and considered as a control variable. Also the demand is time dependent and known. The imprecise holding and production costs are assumed to be represented by fuzzy numbers which are transformed to corresponding interval numbers. Following interval mathematics, the objective function is changed to respective multi-objective functions and thus the single-objective problem is reduced to a multi-objective decision making(MODM) problem. The MODM problem is then again transformed to a single objective function with the help of weighted sum method and then solved using global criteria method, calculus method, the Kuhn---Tucker conditions and generalized reduced gradient(GRG) technique. The models have been illustrated by numerical data. The optimum results for different objectives are obtained for different types of production function. Numerical values of demand, production function and stock level are presented in both tabular and graphical forms