Pro-quality choice a machine by using ordered fuzzy numbers model

• Autorzy: Pacana Andrzej , Siwiec Dominika

Identyfikatory

DOI

10.2478/cqpi-2020-0020

• Języki publikacji: EN

Abstrakty

EN

Meeting the required quality level of products taking into account customer requirements is the essence of thriving enterprises. In this context, it is necessary to make decisions that take into account mentioned the quality level but also the cost aspect relevant to both customer and producer. It was concluded that it is possible to make analyse in which connected the quality level with the cost aspect will condition the make the best choice. Therefore, the aim of work was to propose the pro-quality method of choice by using the ordered fuzzy numbers connected with cost-quality analysis (AKJ). The subject of the study were machines used in pad printing technique, so-called pad print, which choice resulted from their problematic choice to specific and often variable working conditions. As part of the method by using ordered fuzzy numbers, using the Fuzzy TOPSIS method (The Technique for Order of Preference by Similarity to Ideal Solution) the most favorable machine by quality was determined. Subsequently, a pro-quality machine choice was made, and this choice combined the obtained quality level with the purchase cost through the use of cost-quality analysis. The proposed method is some kind of new approach to making the best decision, where the aspect of quality with the cost was connected. Therefore, the proposed method can be used to solve different types of decision problems in production and services enterprises.

Słowa kluczowe

EN

production engineering; mechanical engineering; Fuzzy TOPSIS; proquality choice; ordered fuzzy numbers

PL

inżynieria produkcji; inżynieria mechaniczna; liczby rozmyte

• Wydawca: De Gruyter
• Czasopismo: Quality Production Improvement - QPI
• Rocznik: 2020
• Tom: Vol. 2, Iss. 1
• Strony: 180--187
• Opis fizyczny: Bibliogr. 15 poz., tab.

Twórcy

autor

Pacana Andrzej

• Rzeszow University of Technology, Rzeszow, Poland

autor

Siwiec Dominika

• Rzeszow University of Technology, Rzeszow, Poland

Bibliografia

• 1.Boran, F.E., Genc, S., Kurt, M., Akay, D., 2009. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Expert Systems with Applications, 36, 8, 11363-11368, DOI: https://doi.org/10.1016/j.eswa.2009.03.039
• 2.Buyukozkan, G., Cifci, G., 2012. A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers, Expert Systems with Applications, 39, 3, 3000-3011, DOI: https://doi.org/10.1016/j.eswa.2011.08.162
• 3.Dagdeviren, M., Yavuz, S., Kilinc, N., 2009. Weapon selection using the AHP and TOPSIS methods under fuzzy environment, Expert Systems with Applications, 36, 4, 8143-8151, DOI: https://doi.org/10.1016/j.eswa.2008.10.016
• 4.Mu, E., Pereyra-Rojas, M., 2017. Practical Decision Making. SpringerBriefs in Operations Research, Appendix A: Practical Questions Related to AHP Modeling, 105-106, DOI: 10.1007/978-3-319-33861-3
• 5.Nadaban, S., Dzitac, S., Dzitac, I., 2016. Fuzzy TOPSIS: A General View, Procedia Computer Science, 91, 823-831, DOI: 10.1016/j.procs.2016.07.088
• 6.Pacana, A., Siwiec, D., Bednárová, L. 2019. Analysis of the incompatibility of the product with fluorescent method, Metalurgija, 58(3-4), 337-340.
• 7.Rudnik, K., Kacprzak, D., 2015. Rozmyta metoda TOPSIS wykorzystują skierowane liczby rozmyte, PTZP, 958-986.
• 8.Siwiec, D., Bednárowá, L., Pacana, A. 2020. Metoda doboru penetrantów dla przemysłowych badań nieniszczących, Przemysł Chemiczny, 99(5), 771-773, DOI: 10.15199/62.2020.5.18
• 9.Siwiec, D., Bednárowá, L., Pacana, A., Zawada, M., Rusko, M. 2019. Wspomaganie decyzji w procesie doboru penetrantów fluorescencyjnych do przemysłowych badań nieniszczących, Przemysł Chemiczny, 98(10), 1594-1596, DOI: 10.15199/62.2019.10.12
• 10.Sun, C.C., 2010. A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods, Expert Systems with Applications, 37, 12, 7745-7754, DOI: https://doi.org/10.1016/j.eswa.2010.04.066
• 11.Suner, A., Celikoglu, C., Dicle, O., Sokmen, S., 2012. Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer. Artjfjcal Intelligence in Medicine, 1-10, DOI: http://dx.doi.org/10.1016/j.artmed.2012.05.003
• 12.Wang, T.C., Chang, T.H., 2007. Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment, Expert Systems with Applications, 33, 4, 870- 880, DOI: https://doi.org/10.1016/j.eswa.2006.07.003
• 13.Wang, T.C., Lee, H.D., 2010. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights, Expert Systems with Applications, 36, 5, 8980-8985, DOI: https://doi.org/10.1016/j.eswa.2008.11.035
• 14.Wang, Y.M., Elhag, T., 2006. Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment, Expert Systems with Applications, 31, 2, 309- 319, DOI: https://doi.org/10.1016/j.eswa.2005.09.040
• 15.Xu, Z., Zhang, X., 2013. Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information, Knowledge-Based Systems, 52, 53-64, DOI: https://doi.org/10.1016/j.knosys.2013.05.011

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).