scholarly journals Development of a Fuzzy Economic Order Quantity Model of Deteriorating Items with Promotional Effort and Learning in Fuzziness with a Finite Time Horizon

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 36 ◽  
Author(s):  
Amalendu Singha Mahapatra ◽  
Biswajit Sarkar ◽  
Maheswar Singha Mahapatra ◽  
Hardik N Soni ◽  
Sanat Kumar Mazumder
Keyword(s):  
Finite Time ◽  
Time Horizon ◽  
Deteriorating Items ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Promotional Effort ◽  
Deterioration Rate ◽  
Fuzzy Environment ◽  
Finite Time Horizon

This study investigates an economic order quantity model of deteriorating items, where demand is fuzzy in nature and depends on promotional effort with full backorder for a given time horizon. The learning effect in the fuzzy environment is added in this model. A constant deterioration rate is assumed. Under these circumstances, a mathematical model is developed to curtail the total cost over a finite time horizon by determining the replenishment order quantity, number of replenishments, and the fraction of the replenishment cycle when inventory is positive. A solution algorithm is developed to find the optimal solutions. The applicability of the proposed model is illustrated through numerical examples. To get further insights, sensitivity analysis is carried out for the main parameters in crisp, fuzzy, and fuzzy-learning environments.

scholarly journals The Economic Order Quantity in a Fuzzy Environment for a Periodic Inventory Model with Variable Demand

2022 ◽  
pp. 102-107
Author(s):  
K. Kalaiarasi ◽  
MARY HENRIETTA H ◽  
M. Sumathi ◽  
A. Stanley Raj
Keyword(s):  
Sensitivity Analysis ◽  
Numerical Analysis ◽  
Fuzzy Numbers ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Critical Part ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment

The technique of limiting expenditure plays a critical part in an organization's ability to govern the smooth operation of its management system. The economic order quantity (EOQ) is calculated by solving a nonlinear problem, and the best solution is investigated in a fuzzy and intuitionistic fuzzy environment. The overall cost is made up of several factors, such as demand, holding, and ordering costs. The demand and stock-out characteristics were both fuzzified using fuzzy and intuitionistic fuzzy numbers. The numerical analysis shows the comparison between the two fuzzy numbers through sensitivity analysis.

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