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Development of Part Sales Agent Coupled with Virtual Manufacturing in Internet Environment  

Choi, Hyung-Rim (동아대학교 경영정보과학부)
Kim, Hyun-Soo (동아대학교 경영정보과학부)
Park, Young-Jae (동아대학교 대학원 경영정보학과)
Park, Byoung-Joo (동아대학교 대학원 경영정보학과)
Lee, Kyoung-Jun (서울대학교 행정대학원)
Publication Information
Asia pacific journal of information systems / v.12, no.4, 2002 , pp. 193-213 More about this Journal
Abstract
The sales activity of most of small manufacturing companies is based on orders of buyers. The process of promotion, negotiation, receipt and selection of orders of the manufacturers is closely coupled with the load status of the production lines. The decision on whether to accept an order or not, or the selection of optimal order set among excessive orders is entirely dependent on the schedule of production lines. However, in the real world, since the production scheduling activity is mainly performed by human experts, most of small manufacturers are suffer from being unable to meet due dates, lack of rapid decision on the acceptance of new order. Recently, Internet based Electronic Commerce is recognized as one of the alternatives for strengthening sales power of small and medium companies. However, small and medium manufacturers can't adjust properly to the new environment because they are in short of money, personnel, and technology. To cope with this problem, this paper deals with development of part sales agent coupled with virtual manufacturing in Internet environment that consist of selection agent, advertisement agent, selection agent, negotiation agent, and virtual manufacturing system. This paper develops a time-bounded negotiation mechanism for small and medium manufacturers in agent-based automated negotiation between customers and negotiation agents. Furthermore, to select optimal order set maximized profit, we first formulate the order selection problem with mixed integer programming, but the computation time of IP is not acceptable for real world scale problem. To overcome this problem and dynamic nature of virtual manufacturing, we suggest a genetic algorithm approach, which shows a reasonable computation time for real world case and good incremental problem solving capability.
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