Browsing by Author "Che, Z. H."

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A dispatching rule for photolithography scheduling with an on-line rework strategy
(2006-07-01)
Sha, D. Y.
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Hsu, S. Y.
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Che, Z. H.
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Chen, C. H.
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工業工程與管理學系
Generally speaking, wafer fabrication factories define the photolithography area as the dispatching center of the entire factory. To establish a set of operative dispatching rules in the photolithography area while taking into consideration the rework of defective products would assist in coordinating and balancing the workload of the entire production line. Furthermore, it would help to enhance both the productivity and efficiency of the wafer fabrication, reduce the on-line WIP stock, shorten the production cycle time, and satisfy the requirements of customers regarding production due time and product quality. This research uses on-line rework as the basis for bringing the factor of reworking of a batch process into the dispatching rule for measurement. It then develops the dispatching rule (Rw-DR) which includes the rework strategy. In addition, this research focuses on the batch with high finished proportion in the photolithography area for finding a way to complete the manufacturing procedure faster, lighten the machine workload of the waiting line, and at the same time increase the output quantity. (c) 2006 Elsevier Ltd. All rights reserved.
A Genetic Algorithm for Integration Planning of Assembly Operation and Machine Layout
(2010)
Chiang, C. J.
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Che, Z. H.
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Chiang, Tzu-An
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Che, Zhen-Guo
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資訊管理與財務金融系註：原資管所+財金所
This study is intended to examine the issue regarding the integration of assembly operations and machine layouts. For this issue, we consider a scenario of multiple orders and therefore build up an optimized mathematical model of synchronous planning. In the study, we develop an optimization mathematical model that uses a genetic algorithm (GA) for finding solutions and identify the most proper parameter value that best fit the GA by means of the experimental design. Ultimately, we use a case for a methodological application and the result shows that the GA may effectively integrate assembly operations and machine layouts for finding solutions.
A multi-criterion interaction-oriented model with proportional rule for designing supply chain networks
(2007-11-01)
Che, Z. H.
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Wang, H. S.
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Sha, D. Y.
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工業工程與管理學系
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Department of Industrial Engineering and Management
Supply chain management has offered a way to improve the industrial environment becomes more competitive. While, the commonly seen methodologies may be effective in solving the production-distribution problem only from supplier- or customer-oriented consideration, those cannot present the interactive relationship between upstream and downstream enterprises. In the competitive semiconductor industry environment, considering the viewpoints of the supplier and consumer simultaneously is particularly required, because multiple manufacturing and demanding steps are performed at separate situations, concurrently. In this paper, we propose an interaction-oriented approach, which bases on the analytic hierarchy process (AHP) methodology and proportional rule, to solve the semiconductor distribution problem with multiple quantitative and qualitative criteria. The developed approach gives an expected satisfaction for the all participators of the whole chain while the cooperative information is shared perfectly and effectively. Analysis results demonstrate the proposed methodology is efficient and effective through a real world case study. (c) 2006 Elsevier Ltd. All rights reserved.
Cooperator selection and industry assignment in supply chain network with line balancing technology
(2009-09-01)
Che, Zhen-Guo
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Che, Z. H.
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Hsu, T. A.
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Losses from cooperator delivery delay may greatly undermine the supply chain network performance leading to losses in the increased business cost. This paper mainly discusses and explores how to create the optimized cooperators and industry sets intelligently in the supply chain network. A mathematical model and a genetic algorithm solving model for cooperator selection and industry assignment in supply chain network are presented to minimize the total delivery delay loss. The mathematical model based on the line balancing technology since the supply chain network can be treated as the extension of assembly production line can be used as a foundation for further practical development in the design of supply chain network. The genetic algorithm solving model is adopted to get a satisfactory near-optimal solution with great speed. The application results in real cases show that the solving model presented by this research can quickly and effectively plan the most suitable type of the cooperators and industry sets in supply chain network. (C) 2009 Elsevier Ltd. All rights reserved.
Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management
(2012-08-01)
Che, Z. H.
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Chiang, Tzu-An
;
Che, Zhen-Guo
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
"Supply chain planning has been regarded as a key strategic decision-making activity for the enterprises under the current business circumstances. From the point of supply chain planning, the important issues are to find suitable and quality partners and to decide upon an appropriate production-distribution plan. In this study, hence, we address to develop a decision methodology for supply chain planning in multi-echelon non-balanced supply chain system, taking into account such four criteria as cost, quality, delivery and supplier relationship management and considering quantity discount and capacity constraints. The proposed methodology is based on the analytic network process and turbo particle swarm optimization (TPSO), to evaluate partners and to determine an optimal supply chain network pattern and production-distribution mode. Finally, to demonstrate the performance of the proposed TPSO algorithm, comparative numerical experiments are performed by TPSO, particle swarm optimization (PSO) and genetic algorithm (GA). Empirical analysis results demonstrate that TPSO can outperform PSO and GA in non-balanced supply chain planning problems."