Fuzzy production inventory model for deteriorating items with shortages under the effect of time dependent learning and forgetting: a possibility / necessity approach

Pathak, Savita; Kar, Samarjit; Sarkar, Sandipan

doi:10.1007/s12597-012-0102-5

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…In recent years, a significant number of models have been created, each of which takes into account the deterioration rate and defective rate. Several researchers ( [1], [2], [3], [4], [5], [6], [7]) has considered deterioration and defective items ( [8], [9], [10]) for the study of different inventory models. Demand plays an important role in inventory management, the study of inventory systems can not be anticipated without addressing demand rates.…”

Section: Introductionmentioning

confidence: 99%

Inventory system with generalized triangular neutrosophic cost pattern incorporating maximum life-time-based deterioration and novel demand through PSO

Bhavani¹,

Mahapatra²

2022

Soft Comput

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At the manufacturing plant or while the products are being transferred from one supply layer to another, there is a considerable possibility of receiving damaged or faulty items mixed in with non-defective commodities. This research focuses on the non-defective and defective products that are shipped to retailers by their suppliers. The retailer reworks faulty items to make them non-defective, and the retailer receives a discount on the cost of purchasing defective items. The presented inventory system addresses the uncertainty in inventory costs and also considers the deterioration of items with prioritized maximum product life. In this study, our aim is to minimize the total inventory cost when demand rate as a function of quality and power pattern of time under crisp and generalized triangular neutrosophic environments.Based on the payment deal, interest charges are imposed only when the payment delay has passed a particular allowable time limit. The neutrosophic number, which provides three different types of membership functions representing truth, hesitation, and falseness, is used in the inventory model to handle the cost pattern's uncertainty. A particle swarm optimization approach is used to analyze the proposed inventory model, and the results are validated using a numerical example and sensitivity analysis for various parameters.

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