A Fuzzy Inventory Model with Imperfect Items and Backorder with Allowable Proportionate Discount

A Fuzzy Inventory Model with Imperfect Items and Backorder with Allowable Proportionate Discount

Rojalini Patro Sujit Acharya Mitali Madhusmita NayakMilu Acharya 

Department of Mathematics, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar-751030, Odisha, India

Department of Management Studies, DDCE, Utkal University, Bhubaneswar – 751007, Odisha, India

Corresponding Author Email: 
mitalinayak7@gmail.com
Page: 
39-46
|
DOI: 
https://doi.org/10.18280/mmc_d.390106
Received: 
23 January 2018
| |
Accepted: 
20 December 2018
| | Citation

OPEN ACCESS

Abstract: 

This paper presents both crisp and fuzzy EOQ models for defective items present in each lot when shortages are allowed and backorder takes place. The aim of the work is to first construct an optimal order quantity for the crisp case and then to develop the corresponding fuzzy model. In contrast to the previous inventory models, an allowable proportionate discount is incorporated for the defective items present in each lot to provide a general framework to the model. The aim of the present paper is to find the optimal order size and the expected shortage level so as to obtain the optimum total profit for both the models. The necessary and sufficient conditions for the existence and uniqueness of the optimal solutions are derived and it is also shown that under certain conditions the crisp model boils down the traditional EOQ backorder formula. For the fuzzy case, triangular fuzzy numbers are used for the defective rates and for defuzzification signed distance method is used. Finally, numerical example is provided to illustrate the solution procedure and sensitivity analysis is performed on the results to analyze the effect of the variations taken place for the parameters involved in the model.

Keywords: 

inventory, imperfect quality, proportionate discount, backorder, EOQ

1. Introduction
2. The Mathematical Model
3. Numerical Results
4. Observations
5. Conclusion
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