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Multiple-Criteria Decision Making II

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A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 29834

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Special Issue Editors

Dr. Goran Ćirović

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Guest Editor

Faculty of Civil Engineering Management, University “UNION–Nikola Tesla”, 11000 Belgrade, Serbia
Interests: computational intelligence; Multi-criteria decision making problems; rough set theory; fuzzy set theory; optimization; neuro-fuzzy systems
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Dr. Dragan Pamučar

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Guest Editor

Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, 11000 Belgrade, Serbia
Interests: multi-criteria decision making problems; computational intelligence; sustainability neuro-fuzzy systems; fuzzy; rough and intuitionistic fuzzy set theory; neutrosophic theory
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Special Issue Information

Dear Colleagues,

This issue is a continuation of the previous successful Special Issue "Multiple-Criteria Decision Making".

Decision making on real world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision making problems with imprecise, ambiguous or vague data.

This Special Issue on “Multiple Criteria Decision Making” aims to incorporate recent developments in the area of the multi-criteria decision making field. Topics include, but are not limited to:

MCDM optimization in engineering;
Environmental sustainability in engineering processes;
Multi-criteria production and logistics processes planning;
New trends in multi-criteria evaluation of sustainable processes;
Multi-criteria decision-making in strategic management based on sustainable criteria.

Dr. Goran Ćirović
Dr. Dragan Pamučar
Guest Editors

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Keywords

decision making
fuzzy set theory
rough set theory
neutrosophic set theory
sustainable development
modeling in engineering
sustainable waste management
sustainable processes
environmental engineering
engineering sustainability

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Related Special Issue

Multiple-Criteria Decision Making in Axioms (15 articles)

Published Papers (11 papers)

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Research

24 pages, 5608 KiB

Open AccessArticle

Decision-Support System for Estimating Resource Consumption in Bridge Construction Based on Machine Learning

by Miljan Kovačević, Nenad Ivanišević, Dragan Stević, Ljiljana Milić Marković, Borko Bulajić, Ljubo Marković and Nikola Gvozdović

Axioms 2023, 12(1), 19; https://doi.org/10.3390/axioms12010019 - 24 Dec 2022

Cited by 5 | Viewed by 2247

Abstract

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson’s linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model’s accuracy. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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30 pages, 398 KiB

Open AccessArticle

Linear Diophantine Fuzzy Fairly Averaging Operator for Suitable Biomedical Material Selection

by Hafiz Muhammad Athar Farid, Rukhsana Kausar, Muhammad Riaz, Dragan Marinkovic and Miomir Stankovic

Axioms 2022, 11(12), 735; https://doi.org/10.3390/axioms11120735 - 15 Dec 2022

Cited by 12 | Viewed by 1454

Abstract

Nowadays, there is an ever-increasing diversity of materials available, each with its own set of features, capabilities, benefits, and drawbacks. There is no single definitive criteria for selecting the perfect biomedical material; designers and engineers must consider a vast array of distinct biomedical material selection qualities. The goal of this study is to establish fairly operational rules and aggregation operators (AOs) in a linear Diophantine fuzzy context. To achieve this goal, we devised innovative operational principles that make use of the notion of proportional distribution to provide an equitable or fair aggregate for linear Diophantine fuzzy numbers (LDFNs). Furthermore, a multi-criteria decision-making (MCDM) approach is built by combining recommended fairly AOs with evaluations from multiple decision-makers (DMs) and partial weight information under the linear Diophantine fuzzy paradigm. The weights of the criterion are determined using incomplete data with the help of a linear programming model. The enhanced technique might be used in the selection of compounds in a variety of applications, including biomedical programmes where the chemicals used in prostheses must have qualities similar to those of human tissues. The approach presented for the femoral component of the hip joint prosthesis may be used by orthopaedists and practitioners who will choose bio-materials. This is due to the fact that biomedical materials are employed in many sections of the human body for various functions. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

18 pages, 2883 KiB

Open AccessArticle

Risk Analysis of Green Supply Chain Using a Hybrid Multi-Criteria Decision Model: Evidence from Laptop Manufacturer Industry

by I-Fei Chen, Pi-Ying Kuo, Ruey-Chyn Tsaur, Santanu Sarkar and Shih-Chun Huang

Axioms 2022, 11(12), 668; https://doi.org/10.3390/axioms11120668 - 24 Nov 2022

Cited by 5 | Viewed by 1884

Abstract

Green supply chain management has become enormously significant over the last two decades. Traditional supply chain risk management is inept at dealing with the intangible criteria related to environmental issues. Contrary to most of the previous research, which emphasized risks in merely one or two phases of the green supply chain, this study provides a systematic checklist of the cradle-to-grave approach to risk identification and prioritization using a hybrid method. Based on a world-leading Taiwanese laptop manufacturer, we first identified the risk factors of the green supply chain with respect to the components and subcomponents of Risk Priority Numbers (RPN) on the Failure Mode and Effects Analysis (FMEA). Second, we used the Analytic Network Process (ANP) to derive the relative weights of the subcomponents of RPN. Third, we combined grey relational analysis and ANP weights to derive the relative importance of each risk criterion in each risk factor in the green supply chain. The empirical results verified that our proposed method can be applied to the laptop manufacturing industry and found industry-specific green risk criteria in each factor. Therefore, following this, enterprises can control the possible risks for continuous improvement in their green activities. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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21 pages, 977 KiB

Open AccessArticle

Multi-Criteria Group Decision-Making Models in a Multi-Choice Environment

by Qazi Shoeb Ahmad, Mohammad Faisal Khan and Naeem Ahmad

Axioms 2022, 11(11), 659; https://doi.org/10.3390/axioms11110659 - 21 Nov 2022

Cited by 3 | Viewed by 1918

Abstract

The best–worst method (BWM) has recently demonstrated its applicability in addressing various decision-making problems in a practical setting. The traditional BWM method is based on deterministic information gathered from experts as pairwise comparisons of several criteria. The advantage of BWM is that it uses fewer calculations and analyses while maintaining good, acceptable consistency ratio values. A multi-choice best–worst method (MCBWM), which considers several options for pairwise comparison of preferences between the criteria, has recently been developed. The experts are given the option to select values from several comparison scales. The MCBWM technique has been shown to be better. Presenting the options for which an optimal solution has been found simplifies the calculation and establishes the ideal weight values. This study proposes two different mathematical programming models for solving multi-criteria decision-making problems having multiple decision-makers. The two methods are proposed considering the multi-choice uncertainty assumption in pairwise criteria comparisons. Additionally, it considers the best–worst method as the base model. The multi-choice uncertainty is applied to determine the best choice out of multiple choices. It gives a real-life scenario to the decision-making problems. Although there are many other forms of uncertainty, such as fuzzy, intuitionistic fuzzy, neutrosophic, probabilistic, etc., it focuses on choices instead of ambiguity in terms of the probabilistic or fuzzy nature of parameters. The parameter considered as multi-choice is the pairwise comparison. These parameters are handled by applying the Lagrange interpolating polynomial method. The proposed models are novel in terms of their mathematical structure and group decision-making approach. The models are formulated and further validated by solving numerical examples. It provides a framework for solving mcdm problems where the weightage to the decision-makers is also incorporated. The CR values for all the models of example 1 and 2, and the case study has been found acceptable. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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16 pages, 546 KiB

Open AccessArticle

How to Incorporate Preference Information in a Weight-Restricted DEA Model: A Straightforward Solution Applied in the Field of Economics, Based on Simos’ Revised Method

by María Auxiliadora De Vicente Oliva and Alberto Romero-Ania

Axioms 2022, 11(8), 367; https://doi.org/10.3390/axioms11080367 - 27 Jul 2022

Cited by 2 | Viewed by 1715

Abstract

Data envelopment analysis (DEA) is one the most successful techniques in the field of Operations Research. DEA is a non-parametric and objective approach for evaluating the relative efficiency of a set of decision-making units. The original DEA proposal contemplated the total freedom of variation of weights. This free variation may lead to situations with non-realistic weights and to the impossibility of incorporating the judgments of decision-makers. This work studies the links between multicriteria decision analysis (MCDA) and DEA by introducing weight restrictions in a DEA model using a methodology developed to obtain criteria weights in a MCDA context: the so-called Revised Simos’ Procedure. The presented approach is suitable to be applied in the field of economics and management, being an intuitive and simple enough method for decision-makers who are not familiar with working with DEA models or multicriteria decision analysis. A classic example is presented, where the results found with this approach are compared with the results of other approaches which also use multicriteria decision analysis as a tool to obtain weight restrictions for a DEA model. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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15 pages, 2560 KiB

Open AccessArticle

Using a Constructivist Multi-Criteria Decision Aid Model (MCDA-C) to Develop a Novel Approach to Self-Manage Motivation in Organizations

by Leonardo Ensslin, Ademar Dutra, Sandra Rolim Ensslin, Luiz Antonio Giardino Graziano and André Andrade Longaray

Axioms 2022, 11(7), 331; https://doi.org/10.3390/axioms11070331 - 8 Jul 2022

Cited by 1 | Viewed by 2158

Abstract

The Brazilian Federal Highway Patrol is an organ of public administration with the constitutional mission to carry out the extensive patrolling of federal highways. Stress and physical and emotional exhaustion are constant, causing illnesses along with frustration, demotivation, and depression. The objective of this research is the development of a self-evaluation model of performance for the Federal Highway Patrols, using their perceptions to enhance their motivation at work considering the factors under their control. The constructivist multi-criteria decision aid (MCDA-C) methodology was used to elaborate the model. The proposed model identifies, establishes, and structures a process to measure the performance of patrol officers on the relevant aspects that they can manage, to guide their decisions toward the desired consequences, thus regulating their stress levels. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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18 pages, 708 KiB

Open AccessArticle

A Cross-Country European Efficiency Measurement of Maritime Transport: A Data Envelopment Analysis Approach

by Phi-Hung Nguyen, Thi-Ly Nguyen, Thi-Giang Nguyen, Duc-Thinh Nguyen, Thi-Hoai Tran, Hong-Cham Le and Huong-Thuy Phung

Axioms 2022, 11(5), 206; https://doi.org/10.3390/axioms11050206 - 27 Apr 2022

Cited by 13 | Viewed by 3026

Abstract

Maritime transport, which includes shipping and port operations, is the fundamental basis of international trade and globalization. In transportation management, efficiency is critical for verifying performance and proposing the best countermeasure to meet predetermined goals. Various efforts in this field have been made to solve this problem satisfactorily. However, the significant proportion of conventional approaches are based on long-term observations and professional expertise, with only a few exceptions based on practice-based historical data. Data Envelopment Analysis (DEA) is a non-parametric technique for analyzing various output and input variables parallelly. The efficiency of maritime transport in European countries is explored using a two-stage DEA approach based on Malmquist and Epsilon-Based Measure (EBM). First, the Malmquist model analyses countries’ total productivity growth rates and their breakdown into technical efficiency (catch-up) and technology change (frontier-shift). Second, the EBM model is used to determine the efficiency and inefficiency of the maritime transportation systems in each European country. Apart from identifying the best-performing countries in specific areas over the study period (2016–2019), the results highlight that the gap in applying the EBM method to maritime transport has been successfully closed and that the emerging paradigm, when combined with the Malmquist model, can be a sustainable and appropriate evaluation model for other research areas. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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23 pages, 12249 KiB

Open AccessArticle

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

by Nikola Gvozdović, Kristina Božić-Tomić, Ljubo Marković, Ljiljana Milić Marković, Suzana Koprivica, Miljan Kovačević and Srdjan Jovic

Axioms 2022, 11(4), 182; https://doi.org/10.3390/axioms11040182 - 18 Apr 2022

Cited by 2 | Viewed by 2397

Abstract

In current practice, the remediation of landslides has shown that the biggest problem is the increase in the number of works, and therefore the price of the works. This is due to several factors, including characteristic of the soil, such as the collapse (collapse) of the surrounding ground around the main slide during landslide remediation. Unless these soil erosion effects are taken into account, recovery costs will overrun, which can jeopardize the planned budget. This paper presents a multi-criteria optimization of landslide remediation using the PROMETHEE method and determines the optional number of walls for the additional soil erosion. In a case study on examples of real landslides in the Republic of Serbia, the application of the method is presented and appropriate conclusions are drawn. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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33 pages, 5053 KiB

Open AccessArticle

Development of a Model for Evaluating the Efficiency of Transport Companies: PCA–DEA–MCDM Model

by Željko Stević, Smiljka Miškić, Dragan Vojinović, Eldina Huskanović, Miomir Stanković and Dragan Pamučar

Axioms 2022, 11(3), 140; https://doi.org/10.3390/axioms11030140 - 18 Mar 2022

Cited by 32 | Viewed by 4349

Abstract

The efficiency of transport companies is a very important factor for the companies themselves, as well as for the entire economic system. The main goal of this paper is to develop an integrated model for determining the efficiency of representative transport companies over a period of eight years. An original model was developed that includes the integration of DEA (Data Envelopment Analysis), PCA (Principal Component Analysis), CRITIC (Criteria Importance Through Inter criteria Correlatio), Entropy and MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) methods in order to determine the final efficiency of transport companies based on 10 input–output parameters. The results showed that the most efficient business performance was achieved in the period 2014–2017, followed by slightly less efficient results. Then, extensive sensitivity analysis and comparative analysis were performed, which confirmed, to some extent, the previously obtained results. In the sensitivity analysis, 30 scenarios with changes in the weights of criteria were created, while the comparative analysis was carried out with three other MCDM (Multi-Criteria Decision-Making) methods. Finally, the rank correlation index was determined using the Spearman and WS (Wojciech Salabun) correlation coefficients. According to the final results, very efficient years can be separated that can be the benchmark for furthering the business. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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19 pages, 10618 KiB

Open AccessArticle

Modification of the Logarithm Methodology of Additive Weights (LMAW) by a Triangular Fuzzy Number and Its Application in Multi-Criteria Decision Making

by Darko Božanić, Dragan Pamučar, Aleksandar Milić, Dragan Marinković and Nenad Komazec

Axioms 2022, 11(3), 89; https://doi.org/10.3390/axioms11030089 - 23 Feb 2022

Cited by 29 | Viewed by 3814

Abstract

The Logarithm Methodology of Additive Weights (LMAW) method is a very young method and in its basic form is defined for crisp values. In this paper, the LMAW method was improved by being modified with triangular fuzzy numbers. The modification significantly improved the capacity of the LMAW method to consider uncertainty in decision making. The special importance of the method is reflected in a relatively simple mathematical apparatus due to which it is possible to define, with high quality, weight coefficients of criteria and rank alternative solutions in uncertain environments. The method was tested in solving the problem of the location selection for a landing operations point (LOP) in combat operations of the army. The validation of the obtained results was performed: (1) by means of comparison with the Fuzzy Simple Additive Weighting (FSAW) Method, the Fuzzy Multi-Attributive Border Approximation area Comparison (FMABAC), the fuzzy Višekriterijumsko KOmpromisno Rangiranje (FVIKOR), the fuzzy COmpressed PRoportional ASsessment (FCOPRAS), and the fuzzy Multi Attributive Ideal-Real Comparative Analysis (FMAIRCA); (2) by means of sensitivity analysis by changing the weight coefficients of criteria; and (3) using simulation software. In comparison with other methods, the quality of the ranking of alternative solutions was confirmed, which highlighted the special importance of the fuzzy LMAW method relative to that of certain standard methods, respectively, the ones that are often used and confirmed in practice. On the other hand, the sensitivity analysis, including the changing of the weight coefficients of criteria, showed that the model could tolerate smaller errors in defining the weight coefficients of criteria, and it provided stable results. Finally, the validation of results achieved with the use of simulation software confirmed the obtained output results. The output results confirmed the quality of the modified method. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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15 pages, 391 KiB

Open AccessArticle

A New Entropy Measurement for the Analysis of Uncertain Data in MCDA Problems Using Intuitionistic Fuzzy Sets and COPRAS Method

by Parul Thakur, Bartłomiej Kizielewicz, Neeraj Gandotra, Andrii Shekhovtsov, Namita Saini, Arsham Borumand Saeid and Wojciech Sałabun

Axioms 2021, 10(4), 335; https://doi.org/10.3390/axioms10040335 - 7 Dec 2021

Cited by 20 | Viewed by 3093

Abstract

In this paper, we propose a new intuitionistic entropy measurement for multi-criteria decision-making (MCDM) problems. The entropy of an intuitionistic fuzzy set (IFS) measures uncertainty related to the data modelling as IFS. The entropy of fuzzy sets is widely used in decision support methods, where dealing with uncertain data grows in importance. The Complex Proportional Assessment (COPRAS) method identifies the preferences and ranking of decisional variants. It also allows for a more comprehensive analysis of complex decision-making problems, where many opposite criteria are observed. This approach allows us to minimize cost and maximize profit in the finally chosen decision (alternative). This paper presents a new entropy measurement for fuzzy intuitionistic sets and an application example using the IFS COPRAS method. The new entropy method was used in the decision-making process to calculate the objective weights. In addition, other entropy methods determining objective weights were also compared with the proposed approach. The presented results allow us to conclude that the new entropy measure can be applied to decision problems in uncertain data environments since the proposed entropy measure is stable and unambiguous. Full article

(This article belongs to the Special Issue Multiple-Criteria Decision Making II)

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