A Multi-Item Sustainable Manufacturing Model with Discrete Setup Cost and Carbon Emission Reduction Under Deterministic and Trapezoidal Fuzzy Demand

Karthick, B.; Uthayakumar, R.

doi:10.1007/s41660-021-00159-6

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…Primarily in this model, we assume that the production process is perfect, so it can be extended by turning this model into an imperfect production process. Another extension is possible by relaxing the equal-sized shipment and fixed demand rate in the proposed model (refer, Yu and Hsu [38] (2017), Ganguly et al [13] (2019) and Ganesh Kumar and Uthayakumar [11] (2019) for unequal-sized shipments, Sarkar et al [31] (2020) for price and advertisement-dependent demand, Karthick and Uthayakumar [20] (2021), [21] (2021) for fuzzy demand). Exploring the changes that occur in this model by combining concepts such as learning and forgetting can be considered an extension (refer, Giri and Masanta [14]).…”

Section: Discussionmentioning

confidence: 99%

“…Karthick and Uthayakumar [20] (2021) investigated the imperfect production inventory model with triangular fuzzy demand under the signed distance method. Karthick and Uthayakumar [21] (2021) developed a VMI-consignment stock policy model with multiple items and trapezoidal fuzzy number under the graded mean integration method.…”

Section: Delay In Shipmentmentioning

confidence: 99%

“…fuzzy or vague terms, to express their preferences. Thus, the solution to these sorts of challenges can be found by considering an uncertain parameter with fuzzy numbers (see for instance, [20][21][22]). Generally, fuzzy numbers are used to treat uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

“…h p rij2 =[10, 10, 10; 10, 10, 10; 10, 10, 10], ϕ h p rij1 =[14, 14, 14; 14, 14, 14; 14, 14, 14], ϕ h p mij4 =[5, 5, 5; 5, 5, 5; 5, 5, 5], ϕ h p mij3 =[2, 2, 2; 2, 2, 2; 2, 2, 2], ϕ h p mij2 =[3, 3, 3; 3, 3, 3; 3, 3, 3], ϕ h p mij1 =[6, 6, 6; 6, 6, 6; 6, 6, 6]. The optimum values, l j =[0.1151, 0.0959, 0.1342], B(l j ) =[1.4000, 7.0000, 5.6000], k ij = [4, 4, 4],m ij = [1, 1, 1], n ij =[5,5,5], the profit on three items for three consignee are [P11 3 , P 21 3 , P21 3 ] =…”

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A single-consignor multi-consignee multi-item model with permissible payment delay, delayed shipment and variable lead time under consignment stock policy

Karthick

Uthayakumar

2021

RAIRO-Oper. Res.

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This article proposes a two-level fuzzy supply chain inventory model, in which a single consignor delivers multiple items to the multiple consignees with the consignment stock agreement. The lead time is incorporated into the model and is considered a variable for obtaining optimal replenishment decisions. In addition, crashing cost is employed to reduce the lead time duration. This article investigates four different cases under controllable lead time to analyze the best strategy, focusing on two delays such as delay-in-payments and delay-in-shipment. In all four cases, all associated inventory costs are treated as a trapezoidal fuzzy number, and a signed distance method is employed to defuzzify the fuzzy inventory cost. An efficient optimization technique is adopted to find the optimal solution for the supply chain. Four numerical experiments are conducted to illustrate the four cases. Any one of these experimental results will provide the best solution for the ideal performance of the business under controllable lead time in the consignment stock policy. Finally, the managerial insights, conclusion and future direction of this model are provided.

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Section: Discussionmentioning

confidence: 99%

Section: Delay In Shipmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A single-consignor multi-consignee multi-item model with permissible payment delay, delayed shipment and variable lead time under consignment stock policy

Karthick

Uthayakumar

2021

RAIRO-Oper. Res.

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“…Very recently, Karthick and Uthayakumar (2021b) considered a single vendor-buyer imperfect inventory model and planned for continuous investment to improve the quality by utilizing triangular demand function. Karthick and Uthayakumar (2021a) also investigated two different scenarios, one for economic reason and the other for environmental reason in view of reducing set up cost, controlling carbon emissions during production.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Policy for a Green Production System with Quality Inspection and Warranty Cost Under Environmental Regulations

Rini

Kishore

2021

Process Integr Optim Sustain

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All manufacturing industries contribute to carbon emissions and their harmful effects on environment have always been a point of concern. So, there must be innovative ways devised to target this problem. One of the mandatory regulations imposed by the government in many countries is that of carbon cap-and-trade, which has been quite successful in limiting the amount of carbon emissions produced during various industrial activities. Apart from dealing with environmental policies, the manufacturers need to look after their brand image by constantly putting efforts towards quality of the product being sold in the market. Assuming production of imperfect items during the production process, the manufacturer implements product inspection policy on the produced lot. The lot which remains uninspected is sold with a warranty cost and the one inspected encounters inspection errors, namely, type I and type II. Type I error, mistakenly marking a non-defective unit as defective, is considered to be manufacturer's risk and leads to direct monetary loss to his firm. Type II error, mistakenly marking a defective item as non-defective, is considered to be customer's risk which hampers the brand image of the firm due to customer frustration. Also, it is assumed that the production process gradually shifts from in-control state to out of control state, where the latter produces more defective items. A comprehensive sensitivity analysis is performed to check for the robustness of the model parameters. Numerical analysis is conducted to validate the hypothesis by jointly optimising non-inspected fraction and the fraction of cycle time for production by minimising the expected total cost function.

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An EOQ Inventory Model with Shortage Backorders and Incorporating a Learning Function in Fuzzy Parameters

Ganesan¹,

Uthayakumar

2021

Springer Proceedings in Mathematics &Amp; Statistics

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A Multi-Item Sustainable Manufacturing Model with Discrete Setup Cost and Carbon Emission Reduction Under Deterministic and Trapezoidal Fuzzy Demand

Cited by 14 publications

References 25 publications

A single-consignor multi-consignee multi-item model with permissible payment delay, delayed shipment and variable lead time under consignment stock policy

A single-consignor multi-consignee multi-item model with permissible payment delay, delayed shipment and variable lead time under consignment stock policy

Optimal Policy for a Green Production System with Quality Inspection and Warranty Cost Under Environmental Regulations

An EOQ Inventory Model with Shortage Backorders and Incorporating a Learning Function in Fuzzy Parameters

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