Existing Multi-criteria Decision-making Methods Research Articles

A Novel Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) Approach to Multiple Criteria Decision-Making

The ability to make decisions is crucial for achieving success in any field, particularly in areas that involve managing extensive information and knowledge. The process of decision-making in real-world scenarios often involves considering numerous factors and aspects. It can be challenging to make decisions in such complex environments. In this paper, we present a new technique that solves multicriteria decision-making (MCDM) problems by considering opportunity losses-based polar coordinate distance (OPLO-POCOD). MCDM is a subdiscipline of operations research in which some alternatives are evaluated concerning some criteria to choose the most optimal alternative(s). Opportunity loss is a fundamental concept in economics and management, which can be used as a basis for determining the value associated with information. The authors emphasize that the technique incorporates the concept of opportunity losses and uses distance vectors in polar coordinates, making it a compelling approach. By considering opportunity losses, decision-makers gain a better understanding of the trade-offs involved in selecting alternatives, enabling them to make more informed decisions. Finally, the proposed method is exhibited through the use of numerical an example to illustrate its process. Additionally, a comparative sensitivity analysis is conducted to evaluate the outcomes of OPLO-POCOD and compare them with existing MCDM methods. The OPLO-POCOD method is found to have high reliability compared to other methods, as indicated by Spearman’s correlation coefficient, which is greater than 0.9. The method shows a correlation of over 98.5% with TOPSIS, COPRAS, ARAS, and MCRAT methods, demonstrating its robustness and effectiveness. These analyses show the efficiency of the proposed method and highlight the stability of the results.

Comparative analysis of three categories of multi-criteria decision-making methods

Multi-criteria decision-making (MCDM) is a popular branch of decision theory in which many methods have been proposed to solve MCDM real-world problems. However, in spite of the multiple methods, it is common that different of them provide the same solutions. Therefore, this paper analyzes the causes of the same solutions from different MCDM methods, studies why different normalization methods can achieve the same solutions in MCDM methods through the theory of MCDM methods, and discusses the relationship between different MCDM methods by category. To achieve such goals, MCDM methods are classified into three categories according to their ranking methods. Some traditional MCDM methods are then extended by considering general normalization methods and distance measures. Finally, the characteristics of various MCDM methods are discussed by category and their evaluation indexes are also compared. The comparison results are represented by tables and flowchart models. The relationship between different categories of MCDM methods is shown through the comparison of the evaluation indexes. The aim of the proposed method is not to replace existing MCDM methods, but to fill some present research gaps about MCDM methods.

Development of a new multi-criteria decision-making method

Choosing the best option out of the many available options is always the goal to be achieved in all areas. However, the parameters (criteria) in each alternative are not the same, sometimes contradictory. In this situation, choosing the best option is an extremely difficult decision for the decision maker. Multi-criteria decision making (MCDM) is the ranking of alternatives based on the criteria of each alternative. More than one hundred multi-criteria decision-making methods have been proposed by the inventors. They are being used in many different fields. However, for decision makers, choosing an appropriate method to use in each specific case is a difficult task. CURLI (Collaborative Unbiased Rank List Integration) is a multi-criteria decision making method that distinguishes it from all others. That difference is reflected in the fact that when applying this method, the decision maker does not need to normalize the data nor determine the weights for the criteria. However, it will take a long time for decision makers to apply this method, especially when the number of options to rank is large. This study carried out the development of a new MCDM method based on the CURLI method. This new method is named CURLI-2. Many different examples are presented to evaluate the effectiveness of the proposed method. In each example, the result of ranking the alternatives using the CURLI-2 method has been compared with those using other different MCDM methods. The best alternative determined when using the CURLI-2 method always coincides with the use of existing MCDM methods. Using CURLI-2 method to rank alternatives will be much faster and simpler than using CURLI method. This is the advantage of CURLI-2 method compared with CURLI method

Development of the ELECTRE Method Under Pythagorean Fuzzy Sets Based on Existing Correlation Coefficients for Cotton Fabric Selection

ABSTRACT Cotton fabric selection is a challenging task in the garment product design and development process, and the selection of optimal alternative under the presence of multiple decision criteria becomes complex, and hence it is considered as a multi-criteria decision-making (MCDM) problem. In addition, the selection process involves fuzziness and uncertainty. In this study, Pythagorean fuzzy sets (PFSs) are introduced to handle uncertain information. Elimination and choice translating reality (ELECTRE) is a well-known outranking method for solving MCDM problems. Therefore, we extend the ELECTRE method under the PFS environment, and a correlation-based closeness coefficient is proposed to compare Pythagorean fuzzy numbers (PFNs). This paper applies the proposed PF-ELECTRE approach in solving a practical case involving the ranking cotton fabrics. To exhibit the superiority and robustness of the suggested method, sensitivity analysis is performed to examine the impacts of weights variation, as well as a comparative analysis is carried out between the PF-ELECTRE with several existing MCDM methods. The research contributes to the advancement and development of outranking MCDM methods through a novel PF-ELECTRE approach that utilizes the weighted correlation coefficient. Moreover, the developed method can obtain reliable results and can be used to other textile domains.

A new integrated approach based on base-criterion and utility additive methods and its application to supplier selection problem

Supplier selection is one of the biggest challenges facing manufacturing companies in an increasingly competitive environment. Selecting the right supplier can minimize the cost of raw materials, which constitute a large portion of the final production cost. During the past decade, attention has been drawn to the supplier selection problem, a complex process in which decision-makers assess multiple quantitative and qualitative characteristics. Approaches such as applied individual or hybrid Multi-Criteria Decision-Making (MCDM) methods have also been developed to elucidate this problem. This research aims to offer a novel integrated MCDM method for the supplier selection problem based on the combination of Base Criterion (BC) and Utility Additive (UA) methods. In this approach, one of the criteria is selected as the base criterion. This criterion is compared with other criteria by considering the DM's preferences. The main contribution of this study is to present an integrated approach that simultaneously calculates the criteria weights and the rank of alternatives, leading to fewer pairwise comparisons and more consistency. Also, the robust mathematical foundation of the suggested model, which incorporates utility functions, assures that the results align with decision-maker preferences as much as possible. The Base Criterion Utility Additive (BCUA) model proposed in this paper, formulated as a nonlinear programming (NLP) model, is solved using the Lingo software package. A numerical example and a real case in the electronics industry have been presented to corroborate the applicability and effectiveness of the BCUA model. Sensitivity analysis and a comparative analysis of the proposed method are discussed in comparison to already existing MCDM methods, including the Fully Consistency Method (FUCOM), Ordinal Priority Approach (OPA), Level Based Weight Assignment (LBWA), and Defining Interrelationships Between Ranked Criteria (DIBR). These analyses indicate that the criteria weights and alternative rankings are robust to changing the parameters δ and λ. In addition to selecting the most suitable supplier, the model presented here can also be used in any decision-making problem with multiple attributes and alternatives, particularly when decision-makers are faced with many alternatives.

Fuzzy dynamic MCDM method based on PRSRV for financial risk evaluation of new energy vehicle industry

The financial risk evaluation of new energy vehicle (NEV) industry is conducive to the popularization of NEVs, to encourage more investment into the NEV industry, and to promote the improvement of the risk control system. When evaluating the performance of NEV industry in China, it is usually full of uncertainty and dynamic. q-Rung orthopair fuzzy set (q-ROFS) has the characteristics of non-membership and membership with adjustable parameterization q, which is a very effective mathematical model to capture uncertainty. In this paper, the q-rung orthopair fuzzy (q-ROF) distance measure based triangle orthocenter is given. Then, q-ROF score function (SF) based distance measure is proposed for disposing of comparison issue. Moreover, we present nonlinear comprehensive weighting method by integrating subjective weight information and objective weight information (determining by water-filling theory). In order to solve the counter-intuitive phenomena and dynamic trend issue, the dynamic q-ROF aggregation operators are investigated and their properties are proved. Whereafter, q-ROF multi-criteria decision making (MCDM) approach based projection ranking by similarity to referencing vector (PRSRV) is proposed for evaluating financial risk of NEV industry, along with the sensitivity analysis. Finally, a comparison with some existing MCDM methods states that the presented method has strong data adaptability.

A new hybrid entropy-based decision support method and its application to online shopping selection

The current study proposes a new method called the hybrid entropy-based decision support method (named HEBM) to address multi-criteria decision making (MCDM) problems with uncertainty. In the proposed HEBM, the decision makers (DMs)’ imprecise evaluations are characterized with intuitionistic multiplicative values; weights of the criteria are generated by an improved method with hybrid entropy and cross-entropy measures; and alternatives rankings are determined by defined closeness coefficients. Simulation experiments verify the validity of the improved criteria weights generation method with hybrid entropy and cross-entropy measures. Compared with existing MCDM methods, the proposed HEBM has the following advantages: (1) it avoids the information loss and has higher accuracy; (2) the weights of the criteria derived can directly characterize the DMs’ preferences on the criteria; and (3) quantitative and qualitative information are both analyzed. Simulation experiments and comparative analyses are conducted to demonstrate the effectiveness and superiority of the proposed method. A case study of online shopping selection problems is presented to illustrate the applicability of the proposed method.

Multicriteria decision making based on novel score function of Fermatean fuzzy numbers, the CRITIC method, and the GLDS method

In this paper, we propose a new score function of Fermatean fuzzy numbers (FFNs). The proposed score function of FFNs can conquer the drawbacks of the existing score function of FFNs. We develop a new multicriteria decision making (MCDM) method based on the proposed score function of FFNs, the criteria importance through intercriteria correlation (CRITIC) method and the gained and lost dominance score (GLDS) method with Fermatean fuzzy information. The developed MCDM method can conquer the shortcomings of the existing MCDM methods for MCDM in the environments of FFNs. It provides us a very useful way for MCDM in the context of FFNs.

ABAC: Alternative by alternative comparison based multi-criteria decision making method

Decision-making appears as a complex and challenging task when it requires finding the most suitable alternative among the numerous alternatives in the presence of multiple, usually conflicting criteria. At the same time, stakeholders expect a simple, transparent, and traceable decision-making method. Multi-Criteria Decision-Making (MCDM) methods rank the alternatives considering multiple criteria. The rank reversal problem is an important issue in most existing conventional MCDM methods. This paper proposes a new alternative by alternative comparison-based MCDM Method (ABAC) that addresses the rank reversal problem. We prove that ABAC is free from the rank reversal problem. To illustrate and validate ABAC, we have taken the cloud service selection problem as an application. Further, to show the effectiveness of ABAC, we have provided several case studies covering various domains. We perform several experiments by simulating the ABAC method. We have compared ABAC and existing MCDM methods. The experimental results support that the ABAC method is a rank reversal free MCDM method. We also carry out sensitivity analysis for ABAC. Salient features of ABAC over existing MCDM methods are (i) it is simple; (ii) it is rank reversal free; (iii) it is more scalable.

Assessment of distribution center locations using a multi-expert subjective\u2013objective decision-making approach

Distribution is a strategic function of logistics in different companies. Establishing distribution centers (DCs) in appropriate locations helps companies to reach long-term goals and have better relations with their customers. Assessment of possible locations for opening new DCs can be considered as an MCDM (Multi-Criteria Decision-Making) problem. In this study, a decision-making approach is proposed to assess DC locations. The proposed approach is based on Stepwise Weight Assessment Ratio Analysis II (SWARA II), Method based on the Removal Effects of Criteria (MEREC), Weighted Aggregated Sum Product Assessment (WASPAS), simulation, and the assignment model. The assessment process is performed using the subjective and objective criteria weights determined based on multiple experts’ judgments. The decision matrix, subjective weights and objective weights are modeled based on the triangular probability distribution to assess the possible alternatives. Then, using simulation and the assignment model, the final aggregated results are determined. A case of DC locations assessment is addressed to show the applicability of the proposed approach. A comparative analysis is also made to verify the results. The analyses of this study show that the proposed approach is efficient in dealing with the assessment of DC locations, and the final results are congruent with those of existing MCDM methods.

Picture Fuzzy TOPSIS Method Based on CPFRS Model: An Application to Risk Management Problems

It is not widely acknowledged that multicriteria decision-making (MCDM) problems in some particular circumstances cannot be effectively solved by some traditional methods. This paper aims to construct a novel decision-making method to effectively solve these MCDM problems. Firstly, we propose a covering-based picture fuzzy rough set (CPFRS) model by combining the picture fuzzy (PF) neighborhood operator and some PF logical operators. Secondly, by combining the proposed CPFRS model with the principle of TOPSIS method, a new method is proposed to solve the MCDM problems under PF environments. Finally, we apply our proposed method to the risk management of green buildings. By comparing the proposed method with some existing MCDM methods, the established method is effective and flexible and can be applied to a wide range of environments.

Comparing Supply Chain Risks Ranking in Multi-Attribute Decision-Making Methods Using the Proposed Three-Dimensional Integration Mean Method

Risks play an important role in supply chain vulnerability, performance maintenance, and competitive advantage. Therefore, appropriate risk assessment and ranking are among the top priorities of managers and experts in the supply chain. Because there are several quantitative and qualitative risks in the supply chain, multi-criteria decision-making (MCDM) and, in particular, multi attribute decision making (MADM) methods are used to rank them. Depending on the type of supply chain, the difference in criteria and information available, it is possible to use existing MCDM methods to rank supply chain risks or introduce new methods. In these methods, options are compared on the basis of different criteria according to mathematical methods. Each of these methods uses their own approaches and assumptions. Therefore, there will be different solutions to the ranking. In this case, one or more methods may be selected as the best method. But it cannot be sure that the superior method is chosen correctly. In this paper, decision making methods are compared with each other for an appropriate method to be chosen. Case examples from the related literature have been compared with the proposed method (three-dimensional integrated mean method). To evaluate the validity of the proposed method, the results were sent to experts in various industries along with the proposed method. For analysis the accuracy of experts’ opinions, One-Sample Kolmogorov-Smirnov, Frequencies and Explore tests have been used in SPSS software with 95% confidence level. The obtained results show high reliability of the proposed method for determining an appropriate decision-making method in supply chain risk assessment. However, the proposed method is used for ranking of supplier selection in this paper, but it can be used for many decision-making procedures.

Multi-criteria decision making in robotic agri-farming with q-rung orthopair m-polar fuzzy sets.

q-Rung orthopair fuzzy set (qROFS) and m-polar fuzzy set (mPFS) are rudimentary concepts in the computational intelligence, which have diverse applications in fuzzy modeling and decision making under uncertainty. The aim of this paper is to introduce the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) as a hybrid model of q-rung orthopair fuzzy set and m-polar fuzzy set. A qROmPFS has the ability to deal with real life situations when decision experts are interested to deal with multi-polarity as well as membership and non-membership grades to the alternatives in an extended domain with q-ROF environment. Certain operations on qROmPFSs and several new notions like support, core, height, concentration, dilation, α-cut and (α, β)-cut of qROmPFS are defined. Additionally, grey relational analysis (GRA) and choice value method (CVM) are presented under qROmPFSs for multi-criteria decision making (MCDM) in robotic agri-farming. The proposed methods are suitable to find out an appropriate mode of farming among several kinds of agri-farming. The applications of proposed MCDM approaches are illustrated by respective numerical examples. To justify the feasibility, superiority and reliability of proposed techniques, the comparison analysis of the final ranking in the robotic agri-farming computed by the proposed techniques with some existing MCDM methods is also given.

Q ‐Rung orthopair fuzzy decision‐making framework for integrating mobile edge caching scheme preferences

Mobile edge caching scheme (MECS) can determine where, how, and what to cache on user equipment by employing its own storage. When considering the performance of MECS, it is often full of uncertainty. The q-rung orthopair fuzzy set (q-ROFS), characterized by membership and nonmembership degrees with adjustable parameter q, is quite a high-efficiency way to capture uncertainty. In this paper, first, information measure (entropy, distance measure, and similarity measure)-based area difference under the q-rung orthopair fuzzy (q-ROF) circumstance is studied along with their detailed proofs. Then, we present a comprehensive weight-determination method by combining objective weights (determining by entropy) and subjective weights (given by experts) as combined weights, which can effectually alleviate the unconscionable influence of extreme data on evaluation results and simultaneously reflect objective data and subjective emotion. Moreover, q-ROF score function-based distance measure is presented for dealing with a value comparison problem. Later, q-ROF multicriteria decision-making (MCDM) method called total area based on orthogonal vector (TAOV) is introduced. Moreover, its feasibility is illustrated by MECS selection problem. Finally, a comparison of some existing MCDM methods and the proposed method is constructed for displaying their effectiveness. This proposed method can effectively avoid counterintuitive phenomena, eliminate antilogarithm by negative and zero issue, and has no division by zero issue.

Fuzzy decision by opinion score method

Multicriteria decision making (MCDM) methods have been utilised to solve discrete problems in broad domains. Existing MCDM methods have been developed on the basis of two main contexts, namely, human and mathematical approaches. Each approach faces different challenges, such as inconsistency, time consumption in a pairwise comparison, unnatural comparison, vagueness, normalisation, distance measurement and the issue of criteria weighting in mathematical approach. To solve these challenges, this work presents fuzzy decision by opinion score method (FDOSM), which is a novel MCDM method under fuzzy environment. FDOSM consists of three stages, namely, data input, data transformation and data processing units. Two decision-making platforms, namely, single and group decision-making contexts, are performed on FDOSM. Two case studies (i.e. communication and sport) with multicriteria decision problems are applied to illustrate the significance of FDOSM. The behaviour of FDOSM works on the idea of ideal solutions and allows experts to select the best value and compare the best and other values under the same criterion. Subsequently, mathematical operations are performed to reach a final rank and select the best alternative from a set of available alternatives. In conclusion, FDOSM is debated on and compared with different MCDM methods from ranking and weighting perspectives. Results of FDOSM are more logical than those of other existing MCDM methods.

A Multi-Criteria Decision-Making Method Based on the Improved Single-Valued Neutrosophic Weighted Geometric Operator

The aggregation operator is one of the most common techniques to solve multi-criteria decision-making (MCDM) problems. The aim of this paper is to propose an MCDM method based on the improved single-valued neutrosophic weighted geometric (ISVNWG) operator. First, the defects of several existing single-valued neutrosophic weighted geometric aggregation operators in terms of producing uncertain results in some special cases are analyzed. Second, an ISVNWG operator is proposed to avoid the defects of existing operators. Further, the properties of the proposed ISVNWG operator, including idempotency, boundedness, monotonicity, and commutativity, are discussed. Finally, a single-valued neutrosophic MCDM method based on the developed ISVNWG operator is proposed to overcome the defects of existing MCDM methods based on existing operators. Application examples demonstrate that our proposed operator and corresponding MCDM method are effective and rational for avoiding uncertain results in some special cases.

A Novel Hybrid Fuzzy Grey TOPSIS Method: Supplier Evaluation of a Collaborative Manufacturing Enterprise

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria.

An adaptive model for resource selection and allocation in fog computing environment

Fog computing is a computing infrastructure that supports data distribution between cloud and source in an efficient manner. Building an effective and generalized model for efficient resource selection and allocation mechanism seems to be complex in fog computing. Allocation and selection mechanisms in such environment fall into a class of multi-criteria decision-making (MCDM) problems. The existing MCDM methods may not be suitable for fog computing environment as these problems are distributed, scalable and dynamic. We propose an adaptive multi-criteria decision-making (A-MCDM) model to obtain an optimal ranking of alternatives in dynamic and scalable environments. The time complexity of the proposed A-MCDM is O(nm) in general, and it takes only O(m) time to assign a rank to an alternative where n is the number of criteria and m is the number of alternatives. The performance of the proposed model has been analyzed and found to be better than other MCDM methods.

Optimal location selection for installation of surface water treatment plant by Gini coefficient-based analytical hierarchy process

The present study tries to utilize the advantage of multi-criteria decision-making (MCDM) methods in the selection of an ideal location for installation or relocation of surface water treatment plant (SWTP). But among the existing MCDM methods, the selection of features and their importance is determined by the decision-maker. Also a decision from the method varies with a change in the decision-maker or change in the methods. In this aspect, the present study tries to propose a new MCDM which introduced Gini coefficient method in analytical hierarchy process to make the resultant priority value clearly differentiated and easily separable from the other alternatives. This new method was applied to find a suitable location for SWTP installation or relocation, and according to the results, the decision of the method is coherent with the existing installations where locations with active SWTP receive higher rating compared to the areas where SWTP is not installed. Result displays that the labour cost (LC) and construction cost were found to be most significant compared to others. Both X-chart and analytic hierarchy process have identified water quality as the most significant parameter whereby the Gini method located the LC parameter to be the most vital.

Improved Symmetry Measures of Simplified Neutrosophic Sets and Their Decision-Making Method Based on a Sine Entropy Weight Model

This work indicates the insufficiency of existing symmetry measures (SMs) between asymmetry measures of simplified neutrosophic sets (SNSs) and proposes the improved normalized SMs of SNSs, including the improved SMs and weighted SMs in single-valued and interval neutrosophic settings. Then, the sine entropy measures of SNSs are presented to establish a sine entropy weight model for solving the criteria weights in decision-making. Based on the improved weighted SMs of SNSs and the sine entropy weight model, a multi-criteria decision-making (MCDM) method with unknown criteria weights (an improved MCDM method) is established in the SNS setting. In the MCDM process, corresponding to the criteria weights obtained by the sine entropy model, the ranking order of all alternatives and the best one are given by means of the improved weighted SMs between the ideal solution and each alternative. Lastly, the improved MCDM method is applied to an actual decision example in single-valued and interval neutrosophic settings to indicate the feasibility of the improved MCDM method. By comparative analysis with existing MCDM methods, the improved SMs and the sine entropy weight model not only provide a simpler and more effective method for MCDM problems with unknown criteria weights in the SNS setting, but can also overcome the insufficiency of the existing SMs and MCDM method.