Inventory Models under m¿- measure
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The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibi...
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
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