Pricing and lot sizing for an EPQ inventory model with rework and multiple shipments

Taleizadeh, Ata Allah; Kalantari, Solaleh Sadat; Cárdenas–Barrón, Leopoldo Eduardo

doi:10.1007/s11750-015-0377-9

Cited by 68 publications

(21 citation statements)

References 33 publications

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“…One can use discrete investment instead of continuous investment for automonated (Sarkar et al [63]) inspection policy, which may be more beneficial for some industry. One another limitation of this proposed model is the fixed setup cost, but a variable setup cost is more beneficial for some production system (Tiwari et al [64]) and variable setup cost can be reduced by some continuous or discrete investment (Sakar and Chung [65]) .…”

Section: Discussionmentioning

confidence: 99%

Autonomated Inspection Policy for Smart Factory—An Improved Approach

Sett¹,

Dey

Sarkar

2020

Mathematics

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The proposed model focuses on an imperfect production process (IPP) in which, during long-term production, the system may change to an “out-of-control” state from an “in-control” state and produce some imperfect products because of a long production run length. Brand image and industry reputation are affected by product defectiveness. To increase the profit of any industry and improve reputation and brand image, inspection of the production system is required. However, this inspection is subjected to human error, which negatively affects the assessment of production systems. Herein, an error-free inspection is performed with the help of an autonomation policy, in which each product is inspected via a machine instead of a human, facilitating an error-free inspection and converting the production system to a smart production system. Moreover, in reality, product demand cannot always be constant. Therefore, in this model, a selling-price-dependent demand is considered along with a variable production rate to enhance model applicability. Moreover, total system profit is optimized and optimal values for production run time, inspection scheduling, selling price, buffer inventory, and production rate are determined. Finally, for model validation, some numerical examples along with special cases are provided. The concavity of the optimal function is also proven through graphical illustration. The sensitivity of the key parameters of the presented model is explored and the significance is explained.

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Section: Discussionmentioning

confidence: 99%

Autonomated Inspection Policy for Smart Factory—An Improved Approach

Sett¹,

Dey

Sarkar

2020

Mathematics

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“…Recently, Avci (2019) and Yoon et al (2020) examined the importance of lateral transshipments and having information sharing between members of echelons to mitigate the impact of disruptions. Also some related research can be found in Taleizadeh et al (2013 , 2016a , b) and Lashgary et al (2016) .…”

Section: Literature Reviewmentioning

confidence: 95%

Resilience toward supply disruptions: A stochastic inventory control model with partial backordering under the base stock policy

Taleizadeh

Tafakkori

Thaichon

2021

Journal of Retailing and Consumer Services

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“…[17] optimized a stochastic lotsizing difficulty for a single faulty stage generation system with paramount rework, faulty preventive preservation, and minimal repair. [46] studied replenishment and pricing strategies for a defective process with paramount rework and various deliveries. [47] solved the multi-delivery lot-sizing dilemma with a partial rework.…”

Section: Reworkmentioning

confidence: 99%

An Optimal Two-level Supply Chain Model for Small and Medium-sized Enterprises with a rework for New Products and Price-Dependent Demand

2020

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Today, the advent of new technologies, globalization of markets, customers' varied needs, and increasingly fierce competition, are making SMEs seek to improve engagement with their suppliers, and cost management practices in order to survive. SMEs clearly need to focus on the interests of the entire supply chain by enacting win-win policies. In this study, we investigate a two-level inventory model featuring a manufacturer and a buyer in the competitive market with the policy of producing new products. Imperfect quality products and the capacity to rework are also considered in the model. In other words, because of the competitive nature of the market, any increase in price leads to a decrease in demand. The mathematical model is proposed over two scenarios: a scenario where shortage can occur, and one with no possibility of shortage. The objective function of the mathematical model revolves around the central goal of maximizing the total profit of the supply chain considering both independent and joint optimization by the supply chain members. A new algorithm is proposed to solve the mathematical model whose applicability is evaluated by giving a numerical example to the analysis software MATLAB. The results are analyzed and discussed using a sensitivity analysis approach.

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Pricing and lot sizing for an EPQ inventory model with rework and multiple shipments

Cited by 68 publications

References 33 publications

Autonomated Inspection Policy for Smart Factory—An Improved Approach

Autonomated Inspection Policy for Smart Factory—An Improved Approach

Resilience toward supply disruptions: A stochastic inventory control model with partial backordering under the base stock policy

An Optimal Two-level Supply Chain Model for Small and Medium-sized Enterprises with a rework for New Products and Price-Dependent Demand

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