Expected number analysis of a two-server queueing network subject to inter-stage inspection and rework

Gopalan, M.N.; Kannan, S.

doi:10.1016/0305-0548(94)00080-r

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…Case II: Learning in production and in reworks Most of manufacturing processes are not defect free and result in items that require rework (Gopalan and Kannan, 1995;Agnihothri and Kenett, 1995;Buzacott, 1999;Flapper et al, 2002). Rework occurs when a product or a service does not meet the internal or external quality requirements, and could be defined as doing something at least one extra time due to non-conformance to these requirements (e.g., Love et al, 1999;Bohn and Terwiesch, 1999).…”

Section: Mathematical Modellingmentioning

confidence: 98%

Learning curves for imperfect production processes with reworks and process restoration interruptions

Jaber

Guiffrida

2008

European Journal of Operational Research

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Section: Mathematical Modellingmentioning

confidence: 98%

Learning curves for imperfect production processes with reworks and process restoration interruptions

Jaber

Guiffrida

2008

European Journal of Operational Research

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“…The optimization of choice by the application of fuzzy logic in practice has been applied many times [19,20,21,22] In practice, as the production of defective items is inevitable, reworking process is often done. Gopalan and Kannan wrote [23]: ''All over the world, industries are concentrating in making quality an inherent in their products. In spite of these efforts, rework is becoming an unavoidable factor in many production systems as in glass manufacturing, food processing, etc."…”

Section: T Field: Mathematics (Operations Research) Article Type: Orimentioning

confidence: 99%

Optimizing models for production and inventory control using a genetic algorithm

Pamučar¹,

Đorović²

2012

Vojnotehnički glasnik

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In order to make the Economic Production Quantity (EPQ) model more applicable to real-world production and inventory control problems, in this paper we expand this model by assuming that some imperfect items of different product types being produced such as reworks are allowed. In addition, we may have more than one product and supplier along with warehouse space and budget limitation. We show that the model of the problem is a constrained non-linear integer program and propose a genetic algorithm to solve it. Moreover, a design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. In the end, a numerical example is presented to demonstrate the application of the proposed methodology.

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“…One of these assumptions is that production processes are perfect and defect free and that all items ordered/produced conform to quality. This assumption is not realistic (e.g., Gopalan & Kannan, 1995;Buzacott, 1999). Production and remanufacturing processes are not perfect and result in defective products that require reworking, that result in additional efforts and costs which should be eliminated (e.g., Wacker, 1987;Lee, 1992;Tang & Lo, 1993;Agnihothri & Kenett, 1995).…”

Section: Introductionmentioning

confidence: 99%

Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations

Saadany¹

2021

Preprint

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Reverse Logistics is the flow and management of products, packaging, components, and information from the point of consumption (i.e., the market) to the point of origin (i.e., manufacturers and suppliers). It is a collection of practices similar to those of supply chain management, but in the opposite direction, from downstream to upstream. Reverse logistics is a valuable solution to the hazards jeopardizing the environment, and it involves activities such as reuse, repair, remanufacture, refurbish, reclaim and recycle.Reverse logistics became an established line of research, covering several areas, including inventory control; though, several research gaps still exist, such as: ignoring switching costs between production and remanufacturing processes and learning effects, the assumption that production and remanufacturing processes are of perfect quality, remanufactured products are assumed to be as-good-as new, the assumption that returned products are treated as whole products while ignoring disassembly, collection rate of used items is independent of price and quality, and the assumption that pure remanufacturing and production policies are optimal. These research gaps are addressed in mathematical models to bring reverse logistics optimization closer to reality. Deterministic and stochastic components are considered here with numerical examples and results discussed. The key conclusions are as follows:The inclusion of the first time interval where no remanufacturing/repair exists, results in preventing the overestimation of inventory holding costs in the repairable stock. Assuming production and remanufacturing processes to be perfect, or ignoring learning effects in these processes, might not capture the benefits that product recovery programs are supposed to bring. Although works in the literature assumed pure remanufacturing is mathematically attainable but not feasible, this study shows that the pure remanufacturing case is not valid mathematically, which proves it to be infeasible. It is favourable to compensate customers to settle for remanufactured products instead of new ones. Considering disassembly of returns in the modelling of reverse logistics is proven beneficial. Finally, mixed production and remanufacturing policies are optimal rather than pure ones; and the inclusion of price and quality to determine return and collection rates is crucial.

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Expected number analysis of a two-server queueing network subject to inter-stage inspection and rework

Cited by 12 publications

References 18 publications

Learning curves for imperfect production processes with reworks and process restoration interruptions

Learning curves for imperfect production processes with reworks and process restoration interruptions

Optimizing models for production and inventory control using a genetic algorithm

Inventory management in reverse logistics with imperfect production, learning, lost sales, subassemblies, and price/quality considerations

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