Purpose

This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers.

Design/methodology/approach

The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space.

Findings

The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques.

Research limitations/implications

The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations.

Originality/value

Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

中文翻译：

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集成基于模糊案例推理、参数和基于特征的加工过程成本估算方法

目的

本文旨在提出一种智能系统，当收到来自客户的新零件订单时，该系统可用作成本估算器。

设计/方法论/方法

本研究中应用的方法是基于案例的推理（CBR）、层次分析法、基于规则的推理和案例检索的模糊集理论。使用参数化和基于特征的成本估算技术对检索到的案例进行了修订。案例使用面向对象 (OO) 方法表示，以在 n 维欧几里得向量空间中表征它们。

发现

建议的成本估算器检索与当前新订单具有最相似成本估算的历史案例。此外，它使用参数化和基于特征的成本估算技术，根据新案例和检索到的案例之间的属性差异来修改检索到的成本估算。

研究限制/影响

通过在基于计算机的实验室环境中考虑不同的车床操作，使用数值示例说明了所提出的系统；然而，它的适用性并未在工业环境中得到验证。

创意/价值

过去提出了不同的智能方法；然而，模糊 CBR、参数化和面向特征的方法的组合在产品成本估算问题中没有得到解决。