Browsing by Author "Che, Zhen-Guo"

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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.
AN INTERACTIVE PERSPECTIVE MODEL BASED ON AHP AND 0-1 IP FOR THE STAFF JOB ROTATION OF NURSES IN LARGE-SCALE HOSPITALS
(2011-02-01)
Che, Zhen-Hua
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Kuo, Han-Yuan
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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
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Department of Information Management and Finance
Job rotation is an efficient approach to training staff nurses in diversified skills, enabling them to be more capable of taking care of their patients. This paper focuses on application research that emphasizes the development of a new systematic approach to accomplishing job rotation for staff nurses in a large-scale hospital system with branches located in different geographical areas. The proposed approach is based on the analytic hierarchy process (A HP) methodology and 0-1 integer programming (IP) for implementing job rotation with multiple factors and interactive perspectives. The A HP method is employed to assess and set the weights for each nurse candidate and job/department candidate. The 0-1 IP model applies these weights to find an optimal rotation plan. The results show that the proposed approach is able to help decision makers provide feasible and quality solutions for achieving job rotation in a hospital system.
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
;
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.
FEED-FORWARD NEURAL NETWORKS TRAINING: A COMPARISON BETWEEN GENETIC ALGORITHM AND BACK-PROPAGATION LEARNING ALGORITHM
(2011-10-01)
Che, Zhen-Guo
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Chiang, Tzu-An
;
Che, Zhen-Hua
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
This study discusses the advantages and characteristics of the genetic algorithm and back-propagation neural network to train a feed-forward neural network to cope with weighting adjustment problems. We compare the performances of a back-propagation neural network and genetic algorithm in the training outcomes of three examples by referring to the measurement indicators and experiment data. The results show that the back-propagation neural network is superior to the genetic algorithm. Also, the back-propagation neural network has faster training speed than the genetic algorithm. However, the back-propagation neural network has the shortcoming of overtraining, while the genetic algorithm does not. The experiment result proves that the back-propagation neural network yields better outcomes than the genetic algorithm.
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."