Inventory Models with Proportionate Discount under Learning Effect to Maximize Profit on Imperfect Quality Items

Authors

  • Milu Acharya, Rojalini Patro, Chapala Bohidar

Abstract

Two economic order quantity (EOQ) models for items with imperfect qualities were developed by takingallowable proportionate discount for scrap type defective items and a fixed selling price for both good and rework type items after the end of screening process under without and with learning effectfor different parameters: Inthe Type-I model(without learning):the incoming lot has fractions of both scrap and rework items and these fractions are considered random variables with known probability density functions. In theType-II model (with learning): learning is taken for both the holding cost and the ordering cost. Demands for both models are fulfilled from perfect and reworked items. These concepts are best fitted in automotive industries where due to learning, holding cost and ordering cost reduces from one shipment to another. The automotive industries can earn more profits by considering the effect of learning on holding cost and ordering cost in each lot. The objective is to obtain the maximum total profits for both the models. Numerical results are provided to illustrate the developed models and sensitivity analysis is conducted to understand the effect of scrap and reworked items on the total profit and lot size with and without learning effect on different parameters.
Keywords:Imperfect quality;Proportionate discount; Rework rate; Screening rate; Learning effect.

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Published

2020-05-18

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Section

Articles