• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.29-41
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• 2006

As the quality and environmental problems become one of the most concerned issues of company, the rework policy needs to be efficiently designed and implemented to sustain the company's competitiveness. This paper examines three basic rework policies in multi-stage production systems and analyzes them in terms of costs of setup, production, inspection, and inventory. For analyzing the performance of the rework policies, we propose mathematical models which can compute total production cost and determine the optimal production batch size to minimize the total production cost. The rework policies are classified by the number and location of inspection stages. Using the mathematical models, we could analyze the performances of rework policies and suggest the optimal rework policy according to each manufacturing environment. The analyses show that the proposed model can be used to analytically evaluate and choose the rework policies.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.1180-1180
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• 2010

본 연구는 비안정적인 rework 발생 확률 자체가 납기 및 제품 품질에 매우 나쁜 영향을 미치는 복잡한 제조공정을 대상으로 rework 발생 확률의 변화에 따라 작업의 투입정책(dispatching policy)을 동적으로 변화시킬 수 있는 스케줄링 기법을 제안한다. 본 연구에서는 강화학습(reinforcement learning) 기법을 이용하여 시간의 흐름에 따라 변화하는 rework 발생 확률을 기반으로 작업 투입정책의 모수를 동적으로 조정함으로써 효율적인 투입계획을 수립하는 적응형 스케줄링 알고리즘을 제안하고, 다양한 현실적인 시나리오를 개발하여 그 성능을 테스트한다.

• IE interfaces
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• v.24 no.1
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• pp.87-96
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• 2011

In this paper, we address an optimization problem for minimizing the inspection and rework cost in an inspection-rework system, which forms a network of nodes including a K-stage inspection system, storage areas for items, a source inspection shop, and a re-inspection shop. We assume that (n, 0) acceptance sampling is performed in the source inspection shop and that only 100(1-r)% of items of rejected lots are re-inspected in the re-inspection shop. Since all the nodes are interrelated, in order to formulate our steady-state objective function, we make a steady-state network flow analysis between nodes, and derive both the steady-state amount of flows between nodes and the steady-state fraction defectives by solving a nonlinear balance equation. Finally we provide some fundamental properties and an enumeration procedure for determining the optimal values of (K, r) which both minimizes our objective function and attains a given target average outgoing quality.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.17
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• pp.81-85
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• 1988

This paper presents a model that considers combinations of rework, repair, replacement and scrapping. Policy-Iteration method of inspection is proposed for a serial manufacturing system whose repair cost, scrap cost and inspection cost. when it fails, can be formulated by Markovian approach. Policy-Iteration stops when new inspection policy is the same as previous inspection policy. A numerial example is presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.930-937
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• 2007

In this paper, we address a design problem and a case study of a K-stage inspection system, which is composed of K stages, each of which includes an inspection process and a rework process. Assuming the type I and II errors of inspectors and the re-inspection policy for items classified as good, we determine the smallest integer of K which can achieve a given target defective rate. If K does not exist, holding the current values of the type I, II errors, we search reversely a new vector, (the defective rate of an assembly line, the defective rate of a rework process), which can give the target defective rate. Our formulas and methodology based on our K-stage inspection system could be applied and extended to similar situations with slight modifications.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.141-157
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• 2007

In this paper, we address a design problem and a case study of a K-stage back-light-unit(BLU) inspection system, which is composed of K stages, each of which includes an inspection process and a rework process. Assuming the type I, II errors and the inspection-free policy for items classified as good, we determine the smallest integer of K which can achieve a given target defective rate. If K does not exist, holding the current values of the type I, II errors, we search reversely the defective rate of an assembly line and the defective rate of a rework process, to meet the target defective rate. Our formulae and methodology based on a K-stage inspection system could be applied and extended to similar situations with slight modification.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.151-157
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• 1993

In this paper, we investigate the effects of yield randomness for lot-sizing in a multi-stage production system. The practical importance of incorporating yield randomness into production models has been emphasized by many researchers. Yield randomness, especially in semiconductor manufacturing, poses a mojor challenge for production planning and control. The task becomes even more difficult if the demand for final product is uncertain. An attempt to meet the demand with a higher level of confidence forces one to release more input in the fabrication line. This leads to excessive work-in-process (WIP) inventories which cause jobs to spend unpredictably longer time waiting for the machines. The result is that it is more difficult to meet demand with exceptionally long cycle time and puts further pressure to increase the safety stocks. Due to this spiral effect, it is common to find that the capital tied in inventory is the msot significant factor undermining profitability. We propose a policy to determine the quantity to be processed at each stage of a multi-stage production system in which the yield at each stage may be random and may need rework.

• Journal of Korean Society for Quality Management
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• v.17 no.2
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• pp.158-167
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• 1989

In this paper, a model is presented which incoporates the possibility of quality control at each of the production stages. We consider the TYPE I error and the TYPE II error in inspection procedures. In addition to alternative quality control configurations, the model integrates the carrying costs of in-process and finished inventory, quality of production process, reprocessing including fixed and variable cost. Our policy determines the value of optimal lot-size and reprocessing batch size in reducing the total costs. A numerical example illustrates the model framework and highlights the role of the decision variables.