Optimization of fuzzy inventory lot-size with scrap and defective items under inspection policy

Garg, Harish; Sugapriya, C.; Kuppulakshmi, V.; Nagarajan, D.

doi:10.1007/s00500-022-07804-3

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We solved Equation (30), and the calculation has been given in Appendix A part ((A4)-(A6)); we obtain the value of…”

Section: Solution Methods For Casementioning

confidence: 99%

“…Wright [17] Salameh and Jaber [6] Jaber et al [9] Sebatjane and Adetunji [13] Mittal and Sharma [24] Alamri et al [21] Jayaswal et al [22] Rezaei [23] Mittal and Sharma [24] Pattnaik [25] Rajeswari et al [26] Mahapatra et al [27] Taheri and Mirzazadeh [28] Dinagar and Manvizhi [29] Garg et al [30] Kuppulakshmi et al [31] Jayaswal and Mittal [32] Chung and Huang [33] Sulak [34] Shekarian et al [35] Kazemi et al [36] Present study…”

Section: Fuzzy Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Supply Chain Model for Defective Growing Items (Fishery) with Trade Credit Policy and Fuzzy Learning Effect

Alamri

2023

Axioms

View full text Add to dashboard Cite

Fundamentally, newborn items that are used commercially, such as chicken, fish, and small camel, grow day by day in size and also increase their weight. The seller offers a credit policy to the buyer to increase sales for a particular growing item (fish), and in this paper, it is assumed that the buyer accepts the policy of the trade credit. In this paper, the buyer acquires the newborn items (fish) from the seller and then sells them when the newborn items have increased their size and weight. From this point of view, the present paper reveals a fuzzy-based supply chain model that includes carbon emissions and a permissible delay in payment for defective growing items (fish) under the effect of learning where the demand rate is imprecise in nature and is treated as a triangular fuzzy number. Finally, the buyer’s total profit is optimized with respect to the number of newborn items. A numerical example has been presented for the justification of the model. The findings clearly suggest that the presence of trade credit, learning, and a fuzzy environment have an affirmative effect on the ordering policy. The buyer should order more to avoid higher interest charges after the grace period, which eventually increases their profit, while at the same time, it is also beneficial for the buyer to order less to gain the benefit of the trade credit period. The fuzziness theory controls the uncertainty situation of inventory parameters with the help of a de-fuzzified method. The lower and upper deviation of demand affects the total fuzzy profit. The effect of learning gives a positive response concerning the size of the order and the buyer’s total fuzzy profit. This means that the decision-maker should be aware of the size of the newborn items, rate of learning, and trade credit period during the supply chain because these directly affect the buyer’s total fuzzy profit. The impact of the inventory parameter of this model is presented with the help of sensitivity analysis.

show abstract

“…We solved Equation (30), and the calculation has been given in Appendix A part ((A4)-(A6)); we obtain the value of…”

Section: Solution Methods For Casementioning

confidence: 99%

Section: Fuzzy Environmentmentioning

confidence: 99%

Sustainable Supply Chain Model for Defective Growing Items (Fishery) with Trade Credit Policy and Fuzzy Learning Effect

Alamri

2023

Axioms

View full text Add to dashboard Cite

show abstract

An approach for solving fully generalized intuitionistic fuzzy transportation problems

Beg,

Bisht,

Rawat

2023

Comp. Appl. Math.

View full text Add to dashboard Cite

A Supply Chain Model with Learning Effect and Credit Financing Policy for Imperfect Quality Items under Fuzzy Environment

Alamri

Jayaswal

Mittal

2023

Axioms

View full text Add to dashboard Cite

In this paper, the seller offers a credit period to his buyer for more sales and the buyer accepts the seller’s policy to gain more profit, and it is assumed that the seller has defective and non-defective items. When the seller provides lots for sale to his buyer then, the buyer separates the whole lots with the help of inspection process into defective and perfect quality items. Further, in this scenario, the percentage of defective items present in the lot follows the S-shape learning curve and it is also considered that the demand rate is imprecise in nature. Here, the demand rate assumes a triangular fuzzy number due to the imprecise nature and it is the model assumption. Based on this assumption, we developed an inventory model with the effect of learning and trade credit strategy under a fuzzy environment for the buyer. The buyer’s total profit has been optimized concerning the order quantity in the fuzzy environment where order quantity has been assumed as a decision variable. The results of this model were verified with the help of numerical examples and sensitivity analysis. We compared the buyer’s total profit in a crisp and fuzzy environment and the buyer gained more profit in a fuzzy environment compared to the crisp environment. Moreover, we compared the results with and without the effect of learning and trade credit on the buyer’s ordering policy and obtained a positive effect on the ordering policy in the numerical section. We determined positive results from the sensitivity analysis, which proved that the trade credit policy will be beneficial for both partners of the supply chain.

show abstract

Optimization of fuzzy inventory lot-size with scrap and defective items under inspection policy

Cited by 5 publications

References 32 publications

Sustainable Supply Chain Model for Defective Growing Items (Fishery) with Trade Credit Policy and Fuzzy Learning Effect

Sustainable Supply Chain Model for Defective Growing Items (Fishery) with Trade Credit Policy and Fuzzy Learning Effect

An approach for solving fully generalized intuitionistic fuzzy transportation problems

A Supply Chain Model with Learning Effect and Credit Financing Policy for Imperfect Quality Items under Fuzzy Environment

Contact Info

Product

Resources

About