Using Neural Networks to Solve a Disassembly-to-Order Problem(<Special Issue>TOTAL OPERATIONS MANAGEMENT)
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概要
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Neural Networks (NN) technique is widely used to solve problems with complex or unknown input-output relationships. In this paper, NN concept is implemented in order to solve the disassembly-to-order (DTO) problem. DTO is a system where a variety of returned products are disassembled to fulfill the demand for specified numbers of components and materials. The main objective is to determine the optimal number of take-back EOL products for the DTO system which satisfy the desired criteria of the system. Since take-back EOL products are in uncertain conditions, model formulation is challenging. NN, which is capable of recognizing the hidden relationship or pattern ofa given input-output data, is a very promising technique to solve the DTO problem. In this paper, we use NN to solve the DTO problem. First, NN is trained by a set of data which has the components demanded as input and optimal number of take-back products as output until the relationships between the two are recognized. After that, the trained NN is used to obtain the optimal number of take-back products for the component demands with unknown solutions. A numerical example is considered to illustrate the methodology.
- 2010-04-00
著者
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Nakashima Kenichi
Department Of Chemistry Faculty Of Science And Engineering Saga University
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Gupta Surendra
Laboratory For Responsible Manufacturing 334 Sn Department Of Mie Northeastern University
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GUPTA Surendra
Laboratory for Responsible Manufacturing, Department of MIE, Northeastern University
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IMTANAVANICH Prasit
Laboratory for Responsible Manufacturing, Department of MIE, Northeastern University
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