A Fuzzy-stochastic Inventory Model without Backorder under Uncertainty in Customer Demand

@inproceedings{Dutta2013AFI,
  title={A Fuzzy-stochastic Inventory Model without Backorder under Uncertainty in Customer Demand},
  author={Pankaj Dutta and M. Nagare},
  booktitle={ICORES},
  year={2013}
}
In the current business scenario, a vital aspect of a realistic inventory model is to accurately estimate the customer demand especially in uncertain environment. Keeping this fact in mind recent trend of research includes uncertain demand, either random or fuzzy. In this paper, we amalgamate both random behavior and fuzzy perception into the optimization setting in modeling an inventory model without backorder. Treating customer demand as fuzzy random variable, we aim at providing an approach… Expand

References

SHOWING 1-10 OF 22 REFERENCES
Continuous review inventory model in mixed fuzzy and stochastic environment
TLDR
This paper develops a model in a mixed environment by incorporating fuzzy random variable as customer demand by fuzzifying the random lead-time demand and considering the annual average demand as a fuzzyrandom variable. Expand
On Solving Single-Period Inventory Model under Hybrid Uncertainty
TLDR
An unambiguous optimal order quantity from a set of n fuzzy observations in a newsvendor inventory setting in presence of fuzzy random variable demand capturing both fuzzy perception and randomness of customer demand is determined. Expand
An inventory model for single-period products with reordering opportunities under fuzzy demand
TLDR
This paper analyzes a single-period inventory model of profit maximization with a reordering strategy in an imprecise environment using ordering of fuzzy numbers with respect to their possibilistic mean values to determine the optimal order quantity. Expand
Incorporating one-way substitution policy into the newsboy problem with imprecise customer demand
TLDR
An efficient numerical search procedure is provided to identify the optimal order quantities, in which the utilization of imprecise demand and the use of one-way substitution policy increase the average expected profit. Expand
A single-period inventory model with fuzzy random variable demand
TLDR
To determine the optimal order quantity a new methodology is developed for this model in presence of fuzzy random variable demand where the optimum is achieved using a graded mean integration representation. Expand
Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand
TLDR
This article considers the mixture inventory model involving variable lead time with backorders and lost sales, and finds the optimal solution for order quantity and lead time in the fuzzy sense such that the total cost has a minimum value. Expand
Economic order quantity in fuzzy sense for inventory without backorder model
TLDR
It is found that, after defuzzification, the total cost is slightly higher than in the crisp model; however, it permits better use of the economic fuzzy quantities arising with changes in orders, deliveries, and sales. Expand
Backorder Fuzzy Inventory Model under Function Principle
In this paper, the fuzzy inventory model is discussed, especially in the case of permitting backorder under the fuzzy environment of fuzzy demand, fuzzy order cost, fuzzy inventory cost, and fuzzyExpand
Optimization of fuzzy production inventory models
  • C. Hsieh
  • Mathematics, Computer Science
  • Inf. Sci.
  • 2002
TLDR
Two fuzzy production inventory models with fuzzy parameters for crisp production quantity, or for fuzzy production quantity under the fuzzy arithmetical operations of Function Principle are introduced and optimal solutions are found. Expand
Fuzzy Inventory without Backorder
  • L. Lin, Huey-Ming Lee
  • Mathematics
  • 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)
  • 2009
For the total cost of the inventory without backorder model, if we fuzzify the order quantity, the total demand, the cost of storing and the cost of placing an order as fuzzy numbers then we canExpand
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