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APEKS — metoda podejmowania decyzji

Szybka Jan, Pabian Sylwester

Science, Technology and Innovation

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2021

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Vol. 12, no. 1

45--50

PL

Metoda APEKS została opracowana w latach siedemdziesiątych XX wieku. Posiada ona szerokie zastosowanie w podejmowaniu decyzji. W artykule scharakteryzowano metodę APEKS, która jest metodą wielokryterialną i składa się z dziesięciu kroków. Przedstawiono zastosowanie tej metody na przykładzie wyboru samochodu. Analizowano problem wyboru samochodu osobowego, uwzględniając sześć kryteriów oceny: zużycie paliwa, moc, cena, roczne koszty eksploatacji, walory estetyczne oraz walory użytkowe. Postępując zgodnie z metodą APEKS, analizę zakończono wyborem najlepszego wariantu. Wykorzystano metodę wymuszonych decyzji, polegającą na indywidualnym, wzajemnym porównaniu wszystkich kryteriów. Służy do tego wariant APEKS, który posiada wszystkie najlepsze cechy wariantów do wyboru. Dowodzi to, że APEKS jest wariantem wyidealizowanym oraz fikcyjnym.

EN

The APEKS method was developed in the 1970s. It has a wide range of applications for making a decision. The article describes the APEKS method, which is a multi-criteria method and consists of 10 steps. The application of this method was presented in the example of car selection. The problem of choosing a passenger car was analyzed taking into account 6 evaluation criteria: fuel consumption, power, price, annual operating costs, aesthetic values, and utility values. Following the APEKS method, the analysis was completed with the selection of the best variant, using the forced decision method, consisting of an individual comparison of all criteria with one another. The APEKS variant is used for this, which has all the best features of the variants to choose from. This indicates that APEKS is an idealized and fictional variant.

2

The Effect of Different Decision-Making Methods on Multi-Objective Optimisation of Predictive Torque Control Strategy

Gurel Aycan, Zerdali Emrah

Power Electronics and Drives

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2021

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Vol. 6 (41)

289--300

EN

Today, a clear trend in electrification process has emerged in all areas to cope with carbon emissions. For this purpose, the widespread use of electric cars and wind energy conversion systems has increased the attention and importance of electric machines. To overcome limitations in mature control techniques, model predictive control (MPC) strategies have been proposed. Of these strategies, predictive torque control (PTC) has been well accepted in the control of electric machines. However, it suffers from the selection of weighting factors in the cost function. In this paper, the weighting factor associated with the flux error term is optimised by the non-dominated sorting genetic algorithm (NSGA-II) algorithm through torque and flux errors. The NSGA-II algorithm generates a set of optimal solutions called Pareto front solutions, and a possible solution must be selected from among the Pareto front solutions for use in the PTC strategy. Unlike the current literature, three decision-making methods are applied to the Pareto front solutions and the weighting factors selected by each method are tested under different operating conditions in terms of torque ripples, flux ripples, cur-rent harmonics and average switching frequencies. Finally, a decision-making method is recommended.

3

Methodology for underground mining method selection

Mijalkovski Stojance, Peltechki Dragi, Despodov Zoran, Mirakovski Dejan, Adjiski Vancho, Doneva Nikolinka

Mining Science

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2021

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Vol. 28

201--216

EN

The mining method selection for underground mining is one of the most important decisions when designing a mine. This selection depends on the mining-geological, mining-technical and economic factors. The mining method selection for underground mining can be described as a multi-criteria decision-making process, as several factors are involved in the selection process. In this paper, a methodology for rational and optimal mining method selection for underground mining of metallic mineral resources has been developed. First, a rational selection of the four best-ranked mining methods for underground mining is performed using numerical methods (Nicholas' approach and the modified approach of Nicholas, i.e., UBC selection of mining method). This is followed by the optimal selection of underground mining method using multi-criteria decision-making methods (ELECTRE, PROMETHEE, AHP, and integrated AHP-PROMETHEE) and by comparing the obtained rankings, the optimal mining method is selected.

4

The choice of cloud technology for big data

Veres O., Kozak N.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2017

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Vol. 6, No 2

59--66

EN

This article describes specific features of cloud technology types and their existing classifications, as well as the peculiarities of their implementation in the process of designing the DDS for Big Data Management. The application of the analytic hierarchy process for the choice of cloud technology within the project of DDS for Big Data Management is suggested and described within this paper. Multi-criteria decision making task with a defined set of options and criteria is solved.