An intelligent decision support system for manufacturing technology investments
An intelligent decision support system for manufacturing technology investments
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DOI：
10.1016/j.ijpe.2005.02.010
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发表时间：
2006-11
影响因子：
12
通讯作者：
K. Tan;C. Lim;K. Platts;H. S. Koay
中科院分区：
文献类型：
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作者：
K. Tan;C. Lim;K. Platts;H. S. Koay
对新的制造技术投资做出战略决策是很困难的。新技术通常成本高昂，受到多种因素的影响，而且在实施之前往往很难证明其潜在的好处是合理的。传统上，决策是根据直觉和过去的经验做出的，有时还需要多标准决策支持工具的支持。然而，这些方法不能保留和重用知识，因此管理者无法有效利用他们先前完成的项目的知识和经验来帮助确定未来项目的优先级。本文提出了一种集成基于案例推理（CBR）和模糊ARTMAP（FAM）神经网络模型的混合智能系统，以支持管理者做出及时、最优的制造技术投资决策。该系统包括一个案例库，其中保存了过去技术投资项目的详细信息。每个项目提案都有一组由人类专家确定的特征。然后使用 FAM 网络将新提案的特征与历史案例的特征进行匹配。检索并改编类似的案例，并且有关这些案例的信息可以用作新项目优先级排序的输入。通过案例研究来说明该方法的适用性和有效性，并对结果进行呈现和分析。讨论了所提出方法的影响，并概述了进一步工作的建议。
Making strategic decision on new manufacturing technology investments is difficult. New technologies are usually costly, affected by numerous factors, and the potential benefits are often hard to justify prior to implementation. Traditionally, decisions are made based upon intuition and past experience, sometimes with the support of multicriteria decision support tools. However, these approaches do not retain and reuse knowledge, thus managers are not able to make effective use of their knowledge and experience of previously completed projects to help with the prioritisation of future projects. In this paper, a hybrid intelligent system integrating case-based reasoning (CBR) and the fuzzy ARTMAP (FAM) neural network model is proposed to support managers in making timely and optimal manufacturing technology investment decisions. The system comprises a case library that holds the details of past technology investment projects. Each project proposal is characterised by a set of features determined by human experts. The FAM network is then employed to match the features of a new proposal with those from historical cases. Similar cases are retrieved and adapted, and information on these cases can be utilised as an input to prioritisation of new projects. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Implications of the proposed approach are discussed, and suggestions for further work are outlined.