Application of expectation maximization method for purchase decision-making support in welding branch

• Autorzy: Kujawińska A. , Rogalewicz M. , Diering M.

Identyfikatory

DOI

10.1515/mper-2016-0014

• Języki publikacji: EN

Abstrakty

EN

The article presents a study of applying the proposed method of cluster analysis to support purchasing decisions in the welding industry. The authors analyze the usefulness of the non-hierarchical method, Expectation Maximization (EM), in the selection of material (212 combinations of flux and wire melt) for the SAW (Submerged Arc Welding) method process. The proposed approach to cluster analysis is proved as useful in supporting purchase decisions.

Słowa kluczowe

EN

cluster analysis; welding process; expectation; maximization method

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2016
• Tom: Vol. 7, No. 2
• Strony: 29--33
• Opis fizyczny: Bibliogr. 11 poz., tab., wykr.

Twórcy

autor

Kujawińska A.

• Poznan University of Technology, Chair of Management and Production Engineering, Poland

autor

Rogalewicz M.

• Poznan University of Technology, Chair of Management and Production Engineering, Poland

autor

Diering M.

• Poznan University of Technology, Chair of Management and Production Engineering, Poland

Bibliografia

• [1] Chai J., Liu J., Ngai E., Application of decision making techniques in supplier selection: A systematic review of literature, Exp. Sys. with Appl., 40, 10, 3872-3885, 2013.
• [2] Rogalewicz M., Kujawińska A., Piłacińska M., Selection of data mining method for multidimensional evaluation of the manufacturing process state, Man. and Prod. Eng. Review, 3, 2, 27-35, 2012.
• [3] Cao L., Domain-driven Data Mining: challenges and prospects, Know. and Data Eng., IEEE Trans., 22, 6, 755-769, 2010.
• [4] Janćikova Z., Roubićek V., Juchelkova D., Application of artificial intelligence methods for prediction of steel mechanical properties, Metalurgija, 47, 4, 339-342, 2008.
• [5] Grudzień Ł., Hamrol A., Information quality in design process documentation of quality management systems, Int. J. of Inf. Man., 36, 4, 599-606, 2016.
• [6] Popat S., Emmanuel M., Review and comparative [9] study of clustering techniques, Int. J. of Comp. Sc. and Inf. Tech., 5, 1, 805-812, 2014.
• [7] Stachowiak A., Żywica P., Dyczkowski K., Wójtowicz A., An Interval-Valued Fuzzy Classi_er Based on an Uncertainty-Aware Similarity Measure, Intelligent Systems’2014, Advances in Intelligent Systems and Computing, Springer, 32, 741-751, 2015.
• [8] Murtagh F., Contreras P., Algorithms for hierarchical clustering: an overview, Wiley Int. Rev.: Data Mining and Know. Disc., 2, 1, 86-97, 2012.
• [9] Jain K., Murty M., Flynn P., Data clustering: a review, ACM Comp. Surv. (CSUR), 31, 3, 264-323, 1999.
• [10] Abbas A., Comparisons between data clustering algorithms, Int. Arab. J. of Inf. Tech., 5, 1, 320-325, 2008.
• [11] Sika R., Ignaszak Z., Implementation of the KMES Quality system for data acquisition and processing on the example of chosen foundry, Arch. of Foundry, 8, 3, 97-102, 2008.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.