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Analyzing an inventory model with two-level trade credit period including the effect of customers’ credit on the demand function using q-fuzzy number

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In this paper, an EOQ model for deteriorating items has been developed in infinite time horizon including two-level delay in payment in which one delay in payment (M) is offered… Click to show full abstract

In this paper, an EOQ model for deteriorating items has been developed in infinite time horizon including two-level delay in payment in which one delay in payment (M) is offered to the retailer by the supplier and the another delay in payment (N) is offered by the retailer to all customers. Since the real business world is full of uncertainties and the supplier has to face different problems with the retailer, hence there may exist uncertainties in the credit period which is offered by the supplier to the retailer. Again, uncertainties may be linear or non-linear type. Till now, there is no standard fuzzy number by which linearity and non-linearity can be explored simultaneously. In this respect, a new type of fuzzy number known as q-fuzzy number has been introduced to consider linearity and non-linearity together and this is the novelty of the paper. On the other hand, the retailer intends to offer a credit period to all customers to give rise the demand of the items. So, here demand function depends on credit period and duration of offering the credit period. The aim of the retailer is that how much credit period be benefited to get maximum profit. Therefore the purpose of this model is to determine the optimal credit length for the customers and optimal replenishment cycle length. Also, the model has been discussed considering the situation when the retailer offers no credit to the customers. Then some theoretical results and an algorithm for defuzzification have been developed. Finally, some numerical examples have been carried out to interpret the model and a sensitivity analysis of the optimal solution has been provided with respect to some parameters.

Keywords: fuzzy number; credit period; retailer; credit

Journal Title: Operational Research
Year Published: 2020

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