مقاله انگلیسی رایگان در مورد یک رهیافت استدلالی مبتنی بر مورد برای تخمین هزینه (الزویر)

A turbulent environment imposes organisations to be smart, agile, and responsive to fast changes of business needs. In order to survive and maintain development, organisations have to improve their new product development process and product quality, adjust their products to customer’s requirements, accelerate the process of commercialisation, and be ahead of their competitors [1]. A successful launch of products on the market seems to be critical activity in drifting a company towards a favourable competitive position. The new product development process includes the stages of identifying customer needs, generating concepts, selecting a concept (or a set of concepts), designing a product, testing prototypes of a new product, and launching [2, 3]. As the stage of concept selection precedes the more expensive and long-term development of the selected products, it is the critical stage of the NPD process and one of the most important decisions that impact business success. The selection of product concepts usually bases on the metrics such as the cost and time of a NPD project or the potential profit from a new product. This study is addressed to one-of-a-kind product development, in which customer requirements are increasingly involved. One-of-a-kind production companies largely depend on their ability to develop newer, more qualitative and innovative products within a short period of time [4]. It is widely accepted in many one-of-a-kind production companies that design process relies significantly on past design experience and knowledge, instead of designing a product from scratch [4, 5, 6]. A promising methodology for assisting conceptual product design is case-based reasoning (CBR). CBR is a process for solving a new problem case by referring to the solutions of similar past cases [7]. CBR simulates the human problem-processing model and can have the self-learning function by constant accumulation of past experience [8]. A CBR system usually consists of three modules: a case representation scheme, a similarity metric, and a case retrieval mechanism. In recent years, computational intelligence techniques such as neural networks, fuzzy logic, genetic algorithms, and multi-agent systems have also been integrated with CBR to construct the retrieval mechanism [9, 10, 11].