Application of the Multi-Criteria Optimization Method to Repair Landslides with Additional Soil Collapse

Gvozdović, Nikola; Božić-Tomić, Kristina; Marković, Ljubo; Marković, Ljiljana Milić; Koprivica, Suzana; Kovačević, Miljan; Jović, Srđan

doi:10.3390/axioms11040182

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…It is therefore reasonable to use MCDM methods because of their success in considering the multidimensional nature of FP with precision. As a matter of fact, MCDM methods have been preferred as a selection and ranking method in many previous studies and in many different scientific fields ( Kabir & Hasin, 2013 ; Stević et al, 2022a ; Pala, 2022 ; Gvozdović et al, 2022 ; Oral, 2021 ; Zhang et al, 2022 ). Of course, a single criterion or ratio is insufficient for an overall assessment of the multidimensional performance of companies.…”

Section: Introductionmentioning

confidence: 99%

Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics

Baydaş

Eren

Stević

et al. 2023

PeerJ Computer Science

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When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). İn the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adéquat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria.

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Section: Introductionmentioning

confidence: 99%

Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics

Baydaş

Eren

Stević

et al. 2023

PeerJ Computer Science

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“…In several different disciplines, engineers have to take many technological and managerial decisions at different stages for optimization purposes. The literature shows different examples of this, such as the case of [2][3][4], with research that successfully involves optimization for different purposes. The ultimate goal is either to minimize the effort required or to maximize the desired benefit.…”

Section: Introductionmentioning

confidence: 99%

A Study of Stopping Rules in the Steepest Ascent Methodology for the Optimization of a Simulated Process

García-Nava

Rodríguez‐Picón²,

Méndez‐González³

et al. 2022

Axioms

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Competitiveness motivates organizations to implement statistical approaches for improvement purposes. The literature offers a variety of quantitative methods intended to analyze and improve processes such as the design of experiments, steepest paths and stopping rules that search optimum responses. The objective of this paper is to run a first-order experiment to develop a steepest ascent path to subsequently apply three stopping rules (Myers and Khuri stopping rule, recursive parabolic rule and recursive parabolic rule enhanced) to identify the optimum experimentation stop from two different simulated cases. The method includes the consideration of the case study, the fitting of a linear model, the development of the steepest path and the application of stopping rules. Results suggest that procedures’ performances are similar when the response obeys a parametric function and differ when the response exhibits stochastic behavior. The discussion section shows a structured analysis to visualize these results and the output of each of the stopping rules in the two analyzed cases.

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Application of the Multi-Criteria Optimization Method to Repair Landslides with Additional Soil Collapse

Cited by 2 publications

References 17 publications

Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics

Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics

A Study of Stopping Rules in the Steepest Ascent Methodology for the Optimization of a Simulated Process

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