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

Mathematics

Mahesh Kumar Jayaswal¹  Mandeep Mittal²  Faizan Ahmad Khan³  Osama Abdulaziz Alamri⁴ 

[1] Department of Mathematics and Statistics, Banasthali Vidyapith, Banasthali 3040222, Rajasthan, India;Department of Mathematics, Amity Institute of Applied Sciences, Amity University Uttar Pradesh, Noida 201313, Uttar Pradesh, India;Department of Mathematics, University of Tabuk, Tabuk 71491, Saudi Arabia;Department of Statistics, University of Tabuk, Tabuk 71491, Saudi Arabia;

关键词: EOQ;    defective items;    learning effects;    trade credit;    cloudy fuzzy number;   

DOI  :  10.3390/math10020246

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown