Browsing by Author "Hsu, Yang-Kang"

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An integrated approach to the design and operation for spare parts logistic systems
(2011-04-01)
Wu, Muh-Cherng
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Hsu, Yang-Kang
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Huang, Liang-Chuan
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper attempts to solve a comprehensive design problem for a spare part logistic system. The design factors encompass logistic network design, part vendor selection, and transportation modes selection. Two approaches to solve the problem were proposed. In Approach 1, we simultaneously considered all the design factors and proposed two algorithms (SGA-1 and TGA-1). In Approach 2, the design problem was solved in two stages. Firstly, we aimed to find a near-optimal logistic network. Secondly, with the obtained logistic network, we proposed three algorithms (SGA-2. TGA-2, and NN-GA-Tabu) to find optimal combinations for part vendor and transportation modes selection. Numerical experiments indicate that Approach 2 outperforms Approach 1, and the NN-GA-Tabu outperforms all the other four algorithms. The proposed NN-GA-Tabu might also be a good solution architecture for solving other comprehensive space search problems. (C) 2010 Elsevier Ltd. All rights reserved.
Design of BOM configuration for reducing spare parts logistic costs
(2008-05-04)
Wu, Muh-Cherng
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Hsu, Yang-Kang
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper proposes an approach to reduce the total operational cost of a spare part logistic system by appropriately designing the BOM (bill of material) configuration. A spare part may have several vendors. Parts supplied by different vendors may vary in failure rates and prices - the higher the failure rate, the lower the price. Selecting vendors for spare parts is therefore a trade-off decision. Consider a machine where the BOM is composed of s critical parts and each part has k vendors. The number of possible BOM configurations for the machine is then k(s). For each BOM configuration, we can use OPUS10 (proprietary software) to calculate an optimum inventory policy and its associated total logistic cost. Exhaustively searching the solution space by OPUS10 can yield an optimal BOM configuration; however, it may be formidably time-consuming. To remedy the time-consuming problem, this research proposes a GA-neural network approach to solve the BOM configuration design problem. A neural network is developed to efficiently emulate the function of OPUS 10 and a GA (genetic algorithm) is developed to quickly find a near-optimal BOM configuration. Experiment results indicate that the approach can obtain an effective BOM configuration efficiently. (c) 2007 Elsevier Ltd. All rights reserved.