A profit jump inventory model for imperfect quality items with receiving reparative batch and order overlapping in dense fuzzy environment
RAIRO. Operations Research, Tome 55 (2021) no. 2, pp. 723-744

This paper presents an economic order quantity (EOQ) inventory model for imperfect quality items with receiving a reparative batch and order overlapping in a dense fuzzy environment Here, the imperfect items are identified by screening and are divided into either scrap or reworkable items. The reworkable items are kept in store until the next items are received. Afterwards, the items are returned to the supplier to be reworked. Also, discount on the purchasing cost is employed as an offer of cooperation from a supplier to a buyer to compensate for all additional holding costs incurred to the buyer. The rework process is error free. An order overlapping scheme is employed so that the vendor is allowed to use the previous shipment to meet the demand by the inspection period. However, we assume the total monthly demand quantity as the dense fuzzy number because of learning effect. Moreover, first of all a profit maximization deterministic model is developed and solve by classical method. Fuzzifying the final optimized function via dense fuzzy demand quantity we have employed extended ranking index rule for its defuzzification. During the process of defuzzification we make an extensive study on the paradoxical unit square of the left and right deviations of dense fuzzy numbers. A comparative study is made after splitting the model into general fuzzy and dense fuzzy environment. Finally numerical and graphical illustrations and sensitivity analysis have been made for its global justifications.

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DOI : 10.1051/ro/2021020
Classification : 90B05
Keywords: Inventory control, imperfect quality, order overlapping, dense fuzzy number, ($$) – paradox
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De, Sujit Kumar; Mahata, Gour Chandra. A profit jump inventory model for imperfect quality items with receiving reparative batch and order overlapping in dense fuzzy environment. RAIRO. Operations Research, Tome 55 (2021) no. 2, pp. 723-744. doi: 10.1051/ro/2021020

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