journal article·2021-03-05Open access

Multiple-Criteria Fuzzy Optimization of the Heat Treatment Processes for Two Steel Rolled Products

Ludmila Dymova, Krzysztof Kaczmarek, Pavel Sevastjanov

Applied Sciences· Volume 11· Issue 5

Abstract

This paper presents a developed method for fuzzy multiple-criteria optimization of the rolled-steel heat treatment processes in the modern metallurgical plant. At the first stage of the study, by means of passive industrial experiments or a mathematical simulation of heat transfer processes, and using statistical methods, the regression dependencies of the output parameters of process quality on the input variables that are technological parameters are established. Then, based on the quality parameters, membership functions are formed that represent local criteria of the process quality, and their ranks are calculated using the matrix of pairwise comparisons. The practically useful methodology of the fuzzy multiple-criteria optimization of technological processes is proposed. To illustrate this methodology’s practical efficiency, the solutions of two optimization problems are found by maximizing the global criterion that aggregates local criteria using their ranks. It is shown that the efficiency of the obtained optimal heat treatment modes significantly exceeds the efficiency of the technology used earlier in the plant.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Statistical and Computational Modeling · Engineering Diagnostics and Reliability · Mathematical Control Systems and Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Computer Science, Czestochowa University of Technology, Dąbrowskiego 73, 42-201 Częstochowa, Poland

References (34)

Show source-provided references
1. Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: Review of current practice and recommendations10.1007/s11367-019-01663-1 ↗
2. An overview of methods to evaluate uncertainty of deterministic models in decision support10.1016/j.envsoft.2014.09.017 ↗
3. A comprehensive uncertainty analysis of the analytic hierarchy process methodology applied in the context of environmental decision making10.1002/mcda.1648 ↗
4. Uncertainty analysis of a European high-resolution emission inventory of CO2 and CO to support inverse modelling and network design10.5194/acp-20-1795-2020 ↗
5. Modified fuzzy group decision-making approach to cost overrun risk assessment of power plant projects10.1061/(ASCE)CO.1943-7862.0001593 ↗
6. A new method selection approach for fuzzy group multicriteria decision making10.1016/j.asoc.2012.12.009 ↗
7. Wang, Y., Chu, J., and Liu, Y. (2020). Multi-Criteria Pythagorean Fuzzy Group Decision Approach Based on Social Network Analysis. Symmetry, 12.10.3390/sym12020255 ↗
8. Generalised operations on hesitant fuzzy values in the framework of Dempster-Shafer theory10.1016/j.ins.2015.03.041 ↗
9. Group decision making based on acceptable consistency analysis of interval linguistic hesitant fuzzy preference relations10.1016/j.ins.2020.03.070 ↗
10. The magical number seven plus or minus two: Some limits on our capacity for processing information10.1037/h0043158 ↗
11. George Miller’s magical number of immediate memory in retrospect: Observations on the faltering progression of science10.1037/a0039035 ↗
12. Ecological impact classification with fuzzy sets10.1016/S0304-3800(96)00051-8 ↗
13. Aragon, T.J. (2017). Deriving Criteria Weights for Health Decision Making: A Brief Tutorial, UC Berkeley, School of Public Health.
14. Ramík, J. (2020). Pairwise Comparisons Method. Theory and Applications in Decision Making, Springer International Publishing.10.1007/978-3-030-39891-0 ↗
15. Entani, T. (2017, January 9–12). Estimating fuzzy weight vector from interval pairwise comparison matrix with various processed matrices. Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy.10.1109/FUZZ-IEEE.2017.8015605 ↗
16. The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: An application of smartphone recommendation10.1016/j.engappai.2017.02.001 ↗
17. Fuzzy sets and decision analysis10.1016/S0165-0114(97)00087-0 ↗
18. Social choice axioms for fuzzy set aggregation10.1016/0165-0114(91)90254-N ↗
19. On ordered weighted averaging aggregation operators in multicriteria decision making10.1109/21.87068 ↗
20. Xiao, Z., Kuang, T., and Rong, T. (2010, January 16–17). The Aggregation of Aggregating Methods in MCDM Based on the Fuzzy Soft Sets. Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems, Wuhan, China.10.1109/GCIS.2010.259 ↗
21. Aggregation of aggregating modes in MCDM: Synthesis of Type 2 and Level 2 fuzzy sets10.1016/j.omega.2005.09.005 ↗
22. Kaczmarek, K., Dymova, L., and Sevastjanov, P. (2019). A two phase method for solving the distribution problem in a fuzzy setting. Entropy, 21.10.3390/e21121214 ↗
23. Reasonable properties for the ordering of fuzzy quantities (I) (II)10.1016/S0165-0114(99)00063-9 ↗
24. A preference aggregation method through the estimation of utility intervals10.1016/j.cor.2004.01.005 ↗
25. Ranking fuzzy numbers using a-weighted valuations international journal of uncertainty10.1142/S021848850000040X ↗
26. Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster-Shafer theory10.1016/j.ins.2007.05.001 ↗
27. The Desirability Function
29. A comparison of two methods for determining the weights of belonging to fuzzy sets10.1007/BF00933438 ↗
30. Dymova, L., Kaczmarek, K., Sevastjanov, P., and Kulawik, J. (2021). A fuzzy multiple criteria decision making approach with a complete user friendly computer implementation. Entropy, 23.10.3390/e23020203 ↗
31. An approach to generalization of the intuitionistic fuzzy TOPSIS method in the framework of evidence theory10.2478/jaiscr-2021-0010 ↗
32. MCDM in a fuzzy setting: Investment projects assessment application10.1016/j.ijpe.2004.09.014 ↗
33. Törn, A., and Žilinskas, A. (1989). Global optimization. Lecture Notes in Computer Science, Springer.10.1007/3-540-50871-6 ↗
34. Population set-based global algorithms: Some modifications and numerical studies10.1016/S0305-0548(03)00116-3 ↗