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Endüstriyel Robot Seçiminde Hibrit Çok Kriterli Karar Yaklaşımı

Year 2020, Ejosat Special Issue 2020 (ISMSIT), 1 - 9, 30.11.2020
https://doi.org/10.31590/ejosat.818275

Abstract

Dünya çapında çok sayıda imalat şirketi, artan verimlilik ve karlılık gibi avantajları nedeniyle endüstriyel robotları yaygın olarak benimsemiştir. Çok çeşitli uygulamalar için çok sayıda özellik ve beceriye sahip robotlar mevcuttur. Bunlar kaynak, montaj, malzeme taşıma, yükleme ve boyama dahil olmak üzere çeşitli endüstriyel uygulamalarda çok sayıda görevi yerine getirebilirler. Belirli bir uygulama için robot seçimi, karmaşıklığı, gelişmiş özellikleri ve olanakları nedeniyle çok yönlü bir görevdir. Karar vericinin, çeşitli özellikleri dikkate alarak, faydaları en üst düzeye çıkararak ve maliyetleri en aza indirerek en uygun robotu seçmesi gerekir. Bu bağlamda, bu çalışmanın temel amacı, endüstriyel robot seçimi için bir hibrit çok kriterli karar analizi yaklaşımı sunmaktır. Optimal robot seçimi, standart sapma (SD), ortalama ağırlık (MW) ve Shannon entropisi olmak üzere üç ağırlıklandırma yöntemine ve üç çok kriterli karar verme yöntemine, yani ARAS (additive ratio assessment), SAW (simple additive weighting) ve WPM (weighted product method) dayalı olarak gerçekleştirilir. Kriter ağırlıklarını belirlerken öznel değerlendirmeleri ortadan kaldırmak için nesnel ağırlıklandırma yöntemleri, SD, MW ve Shannon entropisi benimsenmiştir. Her bir MCDA yönteminin girdisi olarak her ağırlıklandırma yönteminin çıktısını kullanarak, dokuz farklı sıralama modeli geliştirilmiştir. Tüm modeller arasındaki korelasyon, Kendall'ın korelasyon katsayıları ile incelenmektedir. Tüm yöntem çiftlerinin sonuçları, nihai bir fikir birliği sıralamasına ulaşmak için Borda yöntemiyle entegre edilir. Sonuçlar, önerilen hibrit yaklaşımın bu çalışmanın amacı için başarıyla kullanılabileceğini ve ARAS'ın en tutarlı yöntem olduğunu göstermektedir.

References

  • Ali, A., & Rashid, T. (2020). Best–worst method for robot selection. Soft Computing. doi:10.1007/s00500-020-05169-z
  • Athawale, V. M., & Chakraborty, S. (2011). A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection. International journal of industrial engineering computations, 2(4), 831-850.
  • Bhangale, P. P., Agrawal, V. P., & Saha, S. K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39(12), 1345-1366. doi:https://doi.org/10.1016/j.mechmachtheory.2004.05.020
  • Borda, J. C. d. (1784). Mémoire sur les élections au scrutin. Histoire de l'Academie Royale des Sciences pour 1781 (Paris, 1784).
  • Boubekri, N., Sahoui, M., & Lakrib, C. (1991). Development of an expert system for industrial robot selection. Computers & Industrial Engineering, 20(1), 119-127. doi:https://doi.org/10.1016/0360-8352(91)90047-A
  • Breaz, R. E., Bologa, O., & Racz, S. G. (2017). Selecting industrial robots for milling applications using AHP. Procedia Computer Science, 122, 346-353. doi:https://doi.org/10.1016/j.procs.2017.11.379
  • Chatterjee, P., Manikrao Athawale, V., & Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26(5), 483-489. doi:https://doi.org/10.1016/j.rcim.2010.03.007
  • Fu, Y., Li, M., Luo, H., & Huang, G. Q. (2019). Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making. Robotics and Autonomous Systems, 122, 103304. doi:https://doi.org/10.1016/j.robot.2019.103304
  • International Organization for Standardization. (2012). Robots and robotic devices — Vocabulary. Robots and robotic devices — Vocabulary. Retrieved from https://www.iso.org/obp/ui/#iso:std:iso:8373:ed-2:v1:en:term:2.1
  • Kendall, M. G. (1948). Rank correlation methods. Oxford, England: Griffin.
  • Keshavarz Ghorabaee, M. (2016). Developing an MCDM method for robot selection with interval type-2 fuzzy sets. Robotics and Computer-Integrated Manufacturing, 37, 221-232. doi:https://doi.org/10.1016/j.rcim.2015.04.007
  • Kumar, R., & Garg, R. K. (2010). Optimal selection of robots by using distance based approach method. Robotics and Computer-Integrated Manufacturing, 26(5), 500-506. doi:https://doi.org/10.1016/j.rcim.2010.03.012
  • Mohsen, O., & Fereshteh, N. (2017). An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP). Safety Science, 92, 160-172. doi:https://doi.org/10.1016/j.ssci.2016.10.006
  • Narayanamoorthy, S., Geetha, S., Rakkiyappan, R., & Joo, Y. H. (2019). Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection. Expert Systems with Applications, 121, 28-37. doi:https://doi.org/10.1016/j.eswa.2018.12.015
  • Nasrollahi, M., Ramezani, J., & Sadraei, M. (2020). A FBWM-PROMETHEE approach for industrial robot selection. Heliyon, 6(5). doi:10.1016/j.heliyon.2020.e03859
  • Rao, R. V. (2007). Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods: Springer Science & Business Media.
  • Rao, R. V., Patel, B. K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367-375. doi:https://doi.org/10.1016/j.robot.2011.01.005
  • Şahin, M. (2020). A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy. International Journal of Environmental Science and Technology. doi:10.1007/s13762-020-02922-7
  • Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x
  • Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159-172.

A Hybrid Multicriteria Decision Approach for Industrial Robot Selection

Year 2020, Ejosat Special Issue 2020 (ISMSIT), 1 - 9, 30.11.2020
https://doi.org/10.31590/ejosat.818275

Abstract

Numerous manufacturing companies worldwide have widely adopted industrial robots due to their advantages, such as increased efficiency and profitability. Robots with numerous features and abilities are available for a wide variety of applications. They can handle numerous tasks in various industrial applications, including welding, assembly, material handling, loading, and painting. The selection of a robot for a particular application is a multifaceted task due to its complexity, advanced features, and facilities. The decision-maker needs to choose the most suitable robot, taking into account the various features, maximizing benefits, and minimizing costs. In this context, the main objective of this study is to present an integrated multiple criteria decision analysis (MCDA) approach for industrial robot selection. The selection of the optimal robot is conducted based on three weighting methods, namely standard deviation (SD), mean weight (MW), and Shannon entropy, and three MCDA methods, namely additive ratio assessment (ARAS), simple additive weighting (SAW), and weighted product method (WPM). The objective weighting methods, SD, MW, and Shannon entropy, are adopted to eliminate subjective evaluations while determining attribute weights. Using the output of each weighting method as the input of each MCDA method, nine different ranking models are developed. The correlation between all models is examined through Kendall’s correlation coefficients. The results of all method pairs are integrated through the Borda method to reach a final consensus ranking. The results indicate that the proposed hybrid approach can be utilized successfully for the purpose of the present study, and ARAS is the most robust method.

References

  • Ali, A., & Rashid, T. (2020). Best–worst method for robot selection. Soft Computing. doi:10.1007/s00500-020-05169-z
  • Athawale, V. M., & Chakraborty, S. (2011). A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection. International journal of industrial engineering computations, 2(4), 831-850.
  • Bhangale, P. P., Agrawal, V. P., & Saha, S. K. (2004). Attribute based specification, comparison and selection of a robot. Mechanism and Machine Theory, 39(12), 1345-1366. doi:https://doi.org/10.1016/j.mechmachtheory.2004.05.020
  • Borda, J. C. d. (1784). Mémoire sur les élections au scrutin. Histoire de l'Academie Royale des Sciences pour 1781 (Paris, 1784).
  • Boubekri, N., Sahoui, M., & Lakrib, C. (1991). Development of an expert system for industrial robot selection. Computers & Industrial Engineering, 20(1), 119-127. doi:https://doi.org/10.1016/0360-8352(91)90047-A
  • Breaz, R. E., Bologa, O., & Racz, S. G. (2017). Selecting industrial robots for milling applications using AHP. Procedia Computer Science, 122, 346-353. doi:https://doi.org/10.1016/j.procs.2017.11.379
  • Chatterjee, P., Manikrao Athawale, V., & Chakraborty, S. (2010). Selection of industrial robots using compromise ranking and outranking methods. Robotics and Computer-Integrated Manufacturing, 26(5), 483-489. doi:https://doi.org/10.1016/j.rcim.2010.03.007
  • Fu, Y., Li, M., Luo, H., & Huang, G. Q. (2019). Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making. Robotics and Autonomous Systems, 122, 103304. doi:https://doi.org/10.1016/j.robot.2019.103304
  • International Organization for Standardization. (2012). Robots and robotic devices — Vocabulary. Robots and robotic devices — Vocabulary. Retrieved from https://www.iso.org/obp/ui/#iso:std:iso:8373:ed-2:v1:en:term:2.1
  • Kendall, M. G. (1948). Rank correlation methods. Oxford, England: Griffin.
  • Keshavarz Ghorabaee, M. (2016). Developing an MCDM method for robot selection with interval type-2 fuzzy sets. Robotics and Computer-Integrated Manufacturing, 37, 221-232. doi:https://doi.org/10.1016/j.rcim.2015.04.007
  • Kumar, R., & Garg, R. K. (2010). Optimal selection of robots by using distance based approach method. Robotics and Computer-Integrated Manufacturing, 26(5), 500-506. doi:https://doi.org/10.1016/j.rcim.2010.03.012
  • Mohsen, O., & Fereshteh, N. (2017). An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP). Safety Science, 92, 160-172. doi:https://doi.org/10.1016/j.ssci.2016.10.006
  • Narayanamoorthy, S., Geetha, S., Rakkiyappan, R., & Joo, Y. H. (2019). Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection. Expert Systems with Applications, 121, 28-37. doi:https://doi.org/10.1016/j.eswa.2018.12.015
  • Nasrollahi, M., Ramezani, J., & Sadraei, M. (2020). A FBWM-PROMETHEE approach for industrial robot selection. Heliyon, 6(5). doi:10.1016/j.heliyon.2020.e03859
  • Rao, R. V. (2007). Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods: Springer Science & Business Media.
  • Rao, R. V., Patel, B. K., & Parnichkun, M. (2011). Industrial robot selection using a novel decision making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6), 367-375. doi:https://doi.org/10.1016/j.robot.2011.01.005
  • Şahin, M. (2020). A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy. International Journal of Environmental Science and Technology. doi:10.1007/s13762-020-02922-7
  • Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x
  • Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159-172.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mehmet Şahin 0000-0001-7078-7396

Publication Date November 30, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ISMSIT)

Cite

APA Şahin, M. (2020). A Hybrid Multicriteria Decision Approach for Industrial Robot Selection. Avrupa Bilim Ve Teknoloji Dergisi1-9. https://doi.org/10.31590/ejosat.818275