This paper presents that the consistency test with consideration of a tolerance deviation in fuzzy AHP proposed by L. C. Leung and D. Cao (2000) is not efficient and has some errors, hence a new method of fuzzy consistency test by direct fuzzification of (Quick Response) QR algorithm -which is one of numerical methods for calculating eigenvalues of an arbitrary matrix -has been proposed.
Purpose Iran Nanotechnology Initiative Council could significantly increase the production of scientific articles in the field by imposing ten-year incentive policies so that Iran ranked 7 in this area in the year 2015. But this progress was insufficient to speed up the production and commercialization of nanotechnology products and Iran ranked 44 with a share of 0.03 per cent of nanotechnology production in the world. Therefore, Iran Nanotechnology Initiative Council as a governmental policymaker institution in this area has sought for the policy threefold increase of funding to speed up the production and commercialization of products in this field. But given that the result was not so clear, this research was formed in the form of modeling Iran Nanotechnology Innovation Network and testing various scenarios to increase its efficiency. Design/methodology/approach This paper uses simulation framework of innovation networks (SKIN) in an attempt to model the production innovation network in Iran in the field of nanotechnology that can measure the effect of incentive policies in changing the network structure and, consequently, increasing the level and pace of innovation in it. Given that the volume of articles produced in Iranian universities in the field of Nanotechnology had a high speed and volume in comparison with the volume of technical knowledge produced by companies, and because the SKIN framework did not consider the distinction between the two, in the first step, the framework is developed using the model of absorptive capacity of knowledge provided by Cohen and Levinthal (1990) and then the developed model was used to model the Innovation Network. Findings Finally, two policies of threefold increase of budget (Scenario 1) and increasing the support for joint projects (with maintaining the current budget level) (Scenario 2) were tested in this model. The social network analysis method was used to analyze the results of the two scenarios, where innovation network topology was compared (as an index to measure the network efficiency) in three states of current status of the network (the baseline scenario), implementing the first and second scenarios of extraction and with each other. Originality/value This paper models Iranian Nanotechnology Innovation Network for studying the evolution of the network as a result of executing different supportive scenarios.
This paper presents the case study of fuzzy statistical process control which has been simulated for variable and discontinuous production within a particular time frame in a key manufacturing workshop. In order to reduce waste production and increase productivity, dimensional inspection from raw product is categorized into three groups: product of type A, product of type B, and discard. In first part, the appearance characteristics of product is defined as fuzzy membership function as the input of the system in order to allocate the output obtained from fuzzy inference of product to one of the three quality levels. Afterwards, each quality level is assigned to its own group by means of Monte Carlo simulation techniques. In the second part, with fuzzy development of a multinomial p chart, the production process is illustrated as a control chart within the particular period of time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.