Inventory model with learning effect and imprecise market demand under screening error

In this paper, an inventory model is developed to deal the impreciseness present in market demand. It is assumed that the received items are not of perfect quality and after screening; imperfect items are withdrawn from inventory and sold at discounted price. However, in practice, errors occur in screening test. So, the screening process fails to be perfect. Due to acquaintance with handling methodology and system, holding cost and ordering cost are gradually decreases from one shipment to another. So, learning effect is incorporated on holding cost, ordering cost and number of defective items present in each lot. This type of situation arises in many industries especially for that industry where productivity is influenced by human labour needed for final assembly such as: cars, ships, machines, aircrafts, electronics, and stabilizer. Due to impreciseness in market demand, profit expression is fuzzy in nature. To fuzzify the profit expression, Extension Principle is used and for defuzzification centroid method is applied. Mathematically, it is shown that profit expression is concave in nature. Finally, the feasibility of proposed model and the effect of learning on optimal solution are shown through numerical example.

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