Risk assessment of R&D projects: a new approach based on IVIF AHP and fuzzy axiomatic design

Both national and international encouragements for research and development (R&D) projects have been growing worldwide. Since R&D projects includes various uncertainties related to time, technology, finance, and knowledge, risk management studies are highly significant for the success of these projects. In risk management, all of the potential actions that might have negative impacts on the processes or outputs of a project should be determined, and if it is possible, their negative impacts should be reduced before the project starts. In this study, after risks in R&D projects are determined, the alternative projects are prioritized with respect to these risks by using an approach based on interval-valued intuitionistic fuzzy AHP and fuzzy information axiom. Interval-valued intuitionistic fuzzy AHP is used to determine the importance degrees of the determined risk factors while fuzzy information axiom is used to evaluate R&D projects considering these risk factors. It is revealed that the most important risk is “Abnormal changes in cost” while the least important one is “Deficiencies in contract articles”.

Prioritizing failure risks of components based on information axiom for product redesign considering fuzzy and random uncertainties

The prioritization of the failure risks of the components in an existing product is critical for product redesign decision-making considering various uncertainties. Two issues need to be addressed in the failure risk prioritization process. One is the evaluation of the failure risk considering each failure mode for each component. Currently, many failure mode effects and analysis (FMEA) methods based on fuzzy logic seldom deal with the randomness in failure mode occurrence during the product operation stage. Therefore, in this research, the information axiom is extended to calculate the information contents of risk indices considering these two types of uncertainty. The second issue is the evaluation of the degree of failure risk for each of the components. The weighted sum of information content considering all failure modes is used to assess the risk of components based on a fuzzy logarithmic least squares method (FLLSM). Additionally, a case study to prioritize the failure risks of various components in a crawler crane is implemented to demonstrate the effectiveness of the developed approach.

An effective similarity determination model for case-based reasoning in support of low-carbon product design

In the initial stage of low-carbon product design, design information is always uncertain and incomplete, as well as the coupling properties between design attributes, thus it requires retrospective coordination for design conflicts resulting from the inclusion of low-carbon requirements. Reusing the prior design knowledge can promote design efficiency, however, the acquisition of similar cases knowledge not only needs to consider the similarity of design problems, but also the adaptability of candidate cases. This study presents an effective similarity determination model to support low-carbon product design, and targets of the proposed model are (1) to reasonably determine design ranges of attribute values for product cases retrieval by representing the uncertain design attributes with fuzzy set theory; (2) to construct an efficient indexing structure to generate the index set of similar cases based on the improved discretized highest similarity method by proposing two effective strategies; and, (3) to establish similarity estimation models for different types of attributes, and it calculates the information content of each attribute to evaluate the adaptability of cases based on the Information Axiom. The applicability of the proposed model is demonstrated through a case study of similar cases retrieval for the vacuum pump low-carbon design.

Development Cycle Modeling: Process Risk

The first part of this paper outlined the Statistical Agent-based Model of Development and Evaluation (SAbMDE) and demonstrated the model’s ability to estimate development cycle resource utilization. This second part of the paper explores the model’s ability to compute development cycle information content and process risk. Risk managers focus mostly on outcome risk, i.e., the likelihood that a running system will behave in an undesirable manner. SAbMDE assumes that a subset of outcome risks are not inherent and immutable but are, instead, the result of defects and vulnerabilities introduced during the system’s development process. The likelihood of defect and vulnerability introduction is a process risk. SAbMDE further assumes that measuring process risk is a prerequisite for minimizing defects and vulnerabilities and, therefore, outcome risk. The model implements the measurement with Shannon’s information–probability relationship similar to its use in Axiomatic Design Theory (ADT). This paper details the SAbMDE’s information and risk calculations and demonstrates those calculations with examples. The process risk calculation is consistent with and offers a mechanism for the ADT Information Axiom.

Axiomatic Design to Foster Additive Manufacturing-Specific Design Knowledge

Axiomatic design has the potential to help designers understand the increased design freedom and limitations of additive manufacturing prior to starting the actual design process. The purpose of this study is to verify the usefulness of Axiomatic Design in the design process of complex additively manufactured components. The article uses a case study involving the design of a non-assembly turbine to demonstrate that Axiomatic Design can be applied as a supportive tool to acquire information on new limitations imposed by additive manufacturing, such as minimum wall thickness and maximum size of parts. The use of axiomatic design is demonstrated by describing the process of decomposition of the non-assembly turbine and examining the suitability of the general design according to the independence axiom. The resulting decomposition chart is subsequently used as a basis by the authors to design individually two competing designs of a turbine. Finally, the information axiom is used to determine the design with the lowest information content according to design (part and support volume), performance (pressure drop) and economic parameters (cost).

A new weighted fuzzy information axiom method in production research

Purpose The purpose of this paper is to propose a novel weighting algorithm for fuzzy information axiom (IA) and to apply it to the evaluation process of 3D printers. Design/methodology/approach As a decision-making tool, IA method is presented to evaluate the performance of any design. Then, weighted IA methods are investigated and a new weighting procedure is introduced to the literature. Then, the existing axiomatic design methods and the proposed new method are classified into two groups: weighting based on information content and weighting based on design ranges. The weighting based on information content approach consists of four methods including pessimistic and optimistic approaches. The philosophy of the weighting based on design ranges is to narrow design ranges in order to decrease fuzziness in the model. To prove the robustness and the performance of the proposed weighting method, the results are compared with the existing methods in the literature. Then, the new approach is applied to evaluate 3D printers. Findings The results of the proposed study show that the proposed weighting algorithm has better performance than the old ones for IA. Therefore, the proposed weighting algorithm should be used for the weighting tool of IA thereafter. Originality/value An effective weighting method compatible with the philosophy of IA method has been proposed. Furthermore, the performances of 3D printers are compared by using the proposed method.