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Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection

Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection

Mehmet Ali Ilgın
Copyright: © 2020 |Pages: 17
ISBN13: 9781799817543|ISBN10: 1799817547|EISBN13: 9781799817550
DOI: 10.4018/978-1-7998-1754-3.ch005
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MLA

Ilgın, Mehmet Ali. "Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection." Robotic Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 100-116. https://doi.org/10.4018/978-1-7998-1754-3.ch005

APA

Ilgın, M. A. (2020). Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection. In I. Management Association (Ed.), Robotic Systems: Concepts, Methodologies, Tools, and Applications (pp. 100-116). IGI Global. https://doi.org/10.4018/978-1-7998-1754-3.ch005

Chicago

Ilgın, Mehmet Ali. "Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection." In Robotic Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 100-116. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1754-3.ch005

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Abstract

An increasing number of companies are using robots to perform a variety of repetitive and hazourdous tasks. Existence of many different robot alternatives force companies to consider several conflicting criteria before determining the most suitable robot alternative. Researchers have developed various multi-criteria decision making based methodologies in order to assist the decision makers in robot selection process. However, those methodologies require decision makers to assign physically meaningless weights to evaluation criteria. This article eliminates this weight assignment process by proposing a robot selection methodology based on linear physical programming. In addition, fuzzy logic was integrated into the proposed approach in order to determine the preference values of subjective robot evaluation criteria. A numerical example is also provided in order to present the applicability of the proposed methodology.

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