A conceptual design decision approach by integrating rough Bayesian network and game theory under uncertain behavior selections. (15th September 2022)

• Record Type: Journal Article
• Authors: Jing, Liting; Li, Qizhi; Ma, Junfeng; Xie, Jing; Peng, Xiang; Li, Jiquan; Jiang, Shaofei
• Is Part Of: Expert systems with applications. Volume 202(2022)
• Publication Date: 2022-09-15
• Subjects: Conceptual scheme -- Functional Bayesian network -- Non-cooperative game -- Function-behavior-structure -- Multi-criteria decision-making; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117108
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A conceptual scheme decision model using rough BN probability model in FBS model. Importance degree based on functional model is proposed to obtain the core sub-functions. The sub-function BN model to analyze the interactivity of sub-functions. Transform the FBS design process into a non-cooperative game model for sub-functions. Abstract: Conceptual design decision plays a vital role in the new product development as it affects the direction of subsequent design activities. However, pertinent literature advocates the uncertainty assessment of the terminal scheme, but ignores the function interactions and the uncertain behavior selections derived from user's preferences in the function-behavior-structure (FBS) design process. Besides, the decision-makers (DM)'s fuzzy judgments for behavior selections in the FBS model are not been addressed. To fill this gap, a conceptual design decision approach by integrating rough Bayesian Network (BN) and game theory under uncertain behavior selections is proposed, which could provide a graphic probabilistic model-based reasoning for the uncertain design process. In this approach, firstly, a sub-function importance model is constructed to achieve the extraction of the core sub-function module. Then, BN approach is developed to analyze the effect of uncertain behavior on the solution of sub-functions, and then support to predict whether to adopt the optimal scheme. And sub-function BN model is constructed based on the FBS model, and an initial BN model is updated by rough set technology. Finally, the probability distribution of uncertain behavior in interactive sub-functions is obtained from BN model, which is used to transform the uncertain behavior solving problem among sub-functions into a non-cooperative game process based on behavioral probabilities, and the optimal scheme is selected. A case study of tree climbing and trimming machine is used to validate the proposed approach and five principle solutions are selected, then the comparison results showed that the sub-function BN is able to provide a valuable design recommendation in new product development.