Learning EOQ Model with Trade-Credit Financing Policy for Imperfect Quality Items under Cloudy Fuzzy Environment

Mahesh Kumar Jayaswal,Mandeep Mittal,Osama Abdulaziz Alamri,Faizan Ahmad Khan

doi:10.3390/math10020246

Mahesh Kumar Jayaswal, Mandeep Mittal + Show 2 more

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https://doi.org/10.3390/math10020246

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Abstract

An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model.

Highlights

In the economic order quantity (EOQ) model, all produced items are considered to be of good quality in nature
This paper investigates the impact of learning on retailer ordering policy for imperfect-quality items with trade-credit financing under in-process inspection, where the demand rate is taken as a cloudy fuzzy number
The demand rate was taken as the cloudy fuzzy triangular number

Summary

Introduction

In the economic order quantity (EOQ) model, all produced items are considered to be of good quality in nature. Because it is impossible to produce 100% good-quality items during manufacturing, many companies appoint an inspector to inspect the quality of items by separating defective and none defective items. In this paper, inspected items are classified into two categories, viz., non defective items and defective items. This paper investigates the impact of learning on retailer ordering policy for imperfect-quality items with trade-credit financing under in-process inspection, where the demand rate is taken as a cloudy fuzzy number. The numerical example reveals that the proposed model with the defuzzification method maximizes retailer profit. Sensitivity analysis is performed to show the novelty of the proposed model. The contribution of the authors is defined in the Table 1

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Conclusion