Multiple Criteria Decision-making Problem Research Articles

A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria

The best–worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating ≤a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers’ (DMs’) confidence in their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs’ confidence in the best-to-others preferences and the DMs’ confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs.

Property valuation based on Choquet integral

The property valuation is classically challenged by the weighting of attributes and observed prices of properties. Modern approaches, using statistical potential, optimization, and rich data increased expectations for better results. Although the practitioners recognize that, they prefer accessible estimations with the convenience of interpreting them like in the sales comparison adjustment-grid. The weighting process is thus either weak in practice or complex in modeling culture. The challenge mainly originates from the incoherencies, sometimes significant, between the negotiated prices and the utility reward (contribution) of attributes. Economic agents take decisions under uncertainty, essentially influenced by their subjective expectations, capacity of evaluation and ability of using reliable information. Therefore, existing approaches suffer from properly addressing these issues of valuation. In this paper, we are motivated to improve these issues based on Choquet integral (CI) and fuzzy logic, well-known in Multiple-criteria decision-making (MCDM) problems. In comparison to the available classic (Adjustment-grid) and statistical approaches (like OLS regressions), the method we develop objectively and accurately weights the contribution of property attributes and the adjusted prices in market value estimations. Based on empirical experiments in case-by-case and mass valuation of single-family properties in Montreal (Canada), the results are encouraging and support its reliability.

SUBCONTRACTOR SELECTION IN A TEXTILE COMPANY USING AN INTEGRATED FUZZY MCDM BASED APPROACH

Several studies have addressed subcontractor selection problem to date. However, most of them considered subcontractor selection in construction industry and other fields have been disregarded. In order to fill this gap, this study focuses on selecting the best subcontractor under fuzzy environment for a Turkish textile company. The case problem is considered as a multiple criteria decision making (MCDM) problem and a robust hybrid methodology combining Fuzzy Preferences Programming (FPP) Method and Graph Theoretical Matrix Approach (GTMA) is proposed for the solution. In the application, the FPP is used to model the interrelations and interdependences among attributes and the GTMA is utilized to choose the best subcontractor for the company under consideration. The obtained results reveal that the devised methodology can successfully tackle with any type of MCDM problems as well as subcontractor selection.

A novel decision-making method using R-Norm concept and VIKOR approach under picture fuzzy environment

The Picture Fuzzy Sets (PFS) are well suitable to capture inconsistent, imprecise and uncertain information in multiple-criteria decision-making problems. This communication is intended to introduce one such information measure defined on PFSs called R-norm picture fuzzy information measure. Moreover, a new set of axioms is proposed as a criteria for picture fuzzy entropy. Besides establishing the validity of proposed R-norm picture fuzzy information measure, some of its major properties are also discussed. In application part, the proposed information measure is applied in predicting the outcome of elections in a poll bound country through opinion polls and to solve an investment problem.

Multiple criteria decision making based on weighted Archimedean power partitioned Bonferroni aggregation operators of generalised orthopair membership grades

In this paper, a multiple criteria decision making (MCDM) method based on weighted Archimedean power partitioned Bonferroni aggregation operators of generalised orthopair membership grades (GOMGs) is proposed. Bonferroni mean operator, geometric Bonferroni mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are introduced into generalised orthopair fuzzy sets to develop the Bonferroni aggregation operators. Their formal definitions are provided, and generalised and specific expressions are constructed. On the basis of the specific operators, a method for solving the MCDM problems based on GOMGs is designed. The working process, characteristics, and feasibility of the method are, respectively, demonstrated via a numerical example, a qualitative comparison at the aspect of characteristics, and a quantitative comparison using the example as benchmark. The demonstration results show that the proposed method is feasible that has desirable generality and flexibility in the aggregation of criterion values and concurrently has the capabilities to deal with the heterogeneous interrelationships of criteria, reduce the negative influence of biased criterion values, and capture the risk attitudes of decision makers.

Election Decision Support System Based on the Best Teacher Performance Assessment in State Smp 2 One Roof SAW Method Using STM Hilir

The process of teacher performance assessment Effectively, precisely, Objectively, and transparently is the important point in selecting the best teacher assessment based on their performance. This assessment was Carried out in SMP Negeri 2 One Roof STM Hilir to avoid the accusations of Injustice and family relationship with the assessor in the school, both the Headmaster and the teacher selection committee. Therefore, a decision system is needed in selecting the decision. One method is the which can be used in solving the problem of Multiple Criteria Decision Making (MADM) is by using Simple Additive weighting (SAW) method, Because The method concept is so easy to understand, simple, effective computationally and has ability in measuring the relative performance of alternative decisions in mathematical form. In addition, Also the design application used UML (Unified Modeling Language) is implemented a system roomates in the software by using Web Programming and MySQL as its database. Finally, based on the design of this application, it can be seen that the decision system by using SAW method is very effective, fast, computerized and transparent accurately in Determining the teacher performance in SMPN 2 One Roof STM Hilir.

Comparative analysis of evolutionary algorithms for multiple criteria decision making with interval-valued belief distributions

In multiple criteria decision making (MCDM) with interval-valued belief distributions (IVBDs), individual IVBDs on multiple criteria are combined explicitly or implicitly to generate the expected utilities of alternatives, which can be used to make decisions with the aid of decision rules. To analyze an MCDM problem with a large number of criteria and grades used to profile IVBDs, effective algorithms are required to find the solutions to the optimization models within a large feasible region. An important issue is to identify an algorithm suitable for finding accurate solutions within a limited or acceptable time. To address this issue, four representative evolutionary algorithms, including genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, and gravitational search algorithm, are selected to combine individual IVBDs of alternatives and generate the minimum and maximum expected utilities of alternatives. By performing experiments with different numbers of criteria and grades, a comparative analysis of the four algorithms is provided with the aid of two indicators: accuracy and efficiency. Experimental results indicate that particle swarm optimization algorithm is the best among the four algorithms for combining individual IVBDs and generating the minimum and maximum expected utilities of alternatives.

A priority-based intuitionistic multiplicative UTASTAR method and its application in low-carbon tourism destination selection

The intuitionistic multiplicative preference relation extends Saaty’s multiplicative preference relation by membership and non-membership functions to describe decision-makers’ preferences on objects (alternatives or criteria), and the product of pairwise membership and non-membership degrees is less than or equal to one. In recent years, the intuitionistic multiplicative preference relation has attracted increasing attention and many methods have been proposed to handle multiple criteria decision making (MCDM) problems. However, few of the existing methods can be used to tackle the MCDM problems with both quantitative and qualitative criteria. To overcome this challenge, in this paper, a novel MCDM method called the priority-based intuitionistic multiplicative UTASTAR method is proposed, which combines a new intuitionistic multiplicative prioritization method and the UTASTAR method within the context of intuitionistic multiplicative sets. The consistency checking and improving processes are considered in the weight-determining method. A case study regarding the low-carbon tourism destination selection is adopted to illustrate the applicability of the proposed method. The effectiveness and superiority of the proposed method are further verified by comparative analyses and discussions.

Generalized Geometric Aggregation Operators Based on T-Norm Operations for Complex Intuitionistic Fuzzy Sets and Their Application to Decision-making

Complex intuitionistic fuzzy set (CIFS) is a special intuitionistic fuzzy set where the membership and non-membership degrees are expressed by a complex-valued membership degree and can more easily describe the vagueness and uncertainty in the real world. Archimedean t-conorm and t-norm (ATT), as an important class of the t-norm (TN) and t-conorm (TC), have greater flexibility in the information fusion process. In this paper, we extend the ATT to CIFSs and present some generalized geometric aggregation operators, which can be used to handle the multiple criteria decision-making (MCDM) problems. For it, we firstly define some new operational laws of the CIFSs based on ATT, then some weighted geometric aggregation operators based on proposed operations are proposed. Further, some desirable properties and special cases of them are studied. Finally, a decision-making approach is developed for the MCDM problem with complex intuitionistic fuzzy information. A practical example is given to show the availability and advantages of the proposed method by comparison with some existing methods. The proposed aggregation operators are more generalized than the existing ones to utilize the uncertain and imprecise information. Several existing operators are considered as special cases of the proposed one. Finally, the proposed method will offer various choices to the decision-maker to access the finest alternatives.

Synergetic Application of Multi-Criteria Decision-Making Models to Credit Granting Decision Problems

Although various algorithms have widely been studied for bankruptcy and credit risk prediction, conclusions regarding the best performing method are divergent when using different performance assessment metrics. As a solution to this problem, the present paper suggests the employment of two well-known multiple-criteria decision-making (MCDM) techniques by integrating their preference scores, which can constitute a valuable tool for decision-makers and analysts to choose the prediction model(s) more properly. Thus, selection of the most suitable algorithm will be designed as an MCDM problem that consists of a finite number of performance metrics (criteria) and a finite number of classifiers (alternatives). An experimental study will be performed to provide a more comprehensive assessment regarding the behavior of ten classifiers over credit data evaluated with seven different measures, whereas the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) techniques will be applied to rank the classifiers. The results demonstrate that evaluating the performance with a unique measure may lead to wrong conclusions, while the MCDM methods may give rise to a more consistent analysis. Furthermore, the use of MCDM methods allows the analysts to weight the significance of each performance metric based on the intrinsic characteristics of a given credit granting decision problem.

A Decision Support System Using Text Mining Based Grey Relational Method for the Evaluation of Written Exams

Grey relational analysis (GRA) is a part of the Grey system theory (GST). It is appropriate for solving problems with complicated interrelationships between multiple factors/parameters and variables. It solves multiple-criteria decision-making problems by combining the entire range of performance attribute values being considered for every alternative into one single value. Thus, the main problem is reduced to a single-objective decision-making problem. In this study, we developed a decision support system for the evaluation of written exams with the help of GRA using contextual text mining techniques. The answers obtained from the written exam with the participation of 50 students in a computer laboratory and the answer key prepared by the instructor constituted the data set of the study. A symmetrical perspective allows us to perform relational analysis between the students’ answers and the instructor’s answer key in order to contribute to the measurement and evaluation. Text mining methods and GRA were applied to the data set through the decision support system employing the SQL Server database management system, C#, and Java programming languages. According to the results, we demonstrated that the exam papers are successfully ranked and graded based on the word similarities in the answer key.

Maintenance strategy selection in a cement industry using analytic network process

PurposeThe purpose of this paper is to demonstrate the application of analytic network process (ANP) as a methodology to make multiple criteria decision in selecting the most appropriate maintenance strategy for organizations with critical manufacturing requirements.Design/methodology/approachMaintenance strategy selection problems are multiple criteria decision making (MCDM) problems which consist of many qualitative and quantitative characteristics. For solving MCDM problems, the ANP is highly recommended as it considers the interdependent influences among and between the various levels of decision attributes. In this research paper, the ANP method is used to select the optimum maintenance strategy in a cement industry in India.FindingsThe ANP method can be used as an effective tool for the evaluation of possible alternatives in maintenance strategy decision problems by considering the dependency among the strategic factors.Research limitations/implicationsAs illustrated in this paper, ANP method can also be used in other industries for adopting the optimum maintenance strategy to enhance the business performance by decreasing the losses associated with equipment effectiveness.Practical implicationsThe major contribution of this research is the successful development of the comprehensive ANP model for the cement plant. ANP model incorporates diversified variables of the cement plant supply chain and includes their interdependencies. The proposed ANP model in this paper, not only guides the decision makers in the selection of the best services but also enables them to visualize the impact of various criteria in the arrival of the final solution.Social implicationsThe model can be extended to certain other manufacturing sectors as the future scope of research and may assist in obtaining a clear idea regarding the status of current maintenance strategies. It should be carried out with a larger number of firms in India focusing on small and medium firms to confirm these results and reinforce their applicability to these kinds of firms. Studies of such a nature would help in identifying successful organizational factors or successful maintenance practices that lead to superior performance.Originality/valueThis paper explores the value of implementing ANP as a decision making method in maintenance strategy, which is currently not a prevalent method.

A new combination rule for Spatial Decision Support Systems for epidemiology

BackgroundDecision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS).MethodsMakes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map.ResultsAn example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases.ConclusionThe new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

Importance‐based multicriteria decision making with interval valued criteria satisfactions

Multiple-criteria decision problems involve selecting an alternative that best satisfies a collection of criteria as quantified by a scalar corresponding to an aggregation of the alternatives satisfaction to the individual criteria. A fundamental issue is the formulation of decision maker's aggregation function based upon the decision maker's perceived relationship between the criteria. Here, we allow the decision maker to express their perceived relationship between the criteria in terms of information about the criteria importances by providing a fuzzy measure over the criteria such that the measure of any subset of criteria is its importance. With the aid of the Choquet integral, we use this fuzzy measure of importances to construct an aggregation function. As the Choquet integral requires an ordering of an alternatives individual criteria satisfactions, special handling is required in the case when criteria satisfactions are interval valued rather then scalar. Here we use the golden rule representative value in the case of interval values.

An Interval Based Score Method for Multiple Criteria Decision Making Problems

In this paper, a linear optimization approach to solve multiple criteria decision making (MCDM) problems is presented. For this purpose, two linear programming problems are proposed in the most favorable and least favorable senses. Then, an overall score as an interval number for each alternative is obtained. The lower bound is the score in the most favorable sense and its upper bound is the performance in the least favorable sense. The order of all alternatives is ranked in descending order in accordance with these interval numbers using the concept of degree of possibility. This study makes three major contributions. First, the proposed method employs linear programming (LP) technique to solve MCDM problems. Second, the common set of weights are utilized to solve the proposed LP models. Finally, the presented approach incorporates the decision maker preferences in decision making process.

An Integrated Method with PROMETHEE and Conflict Analysis for Qualitative and Quantitative Decision-Making: Case Study of Site Selection for Wind Power Plants

Multiple-criteria decision-making is common in our daily life. The probabilistic linguistic term set is an effective tool to represent both simple and cognitive complex linguistic expressions given by individuals and groups completely. In this paper, the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) is enhanced by integrating with a conflict analysis to solve general multiple-criteria decision-making problems with both quantitative and qualitative criteria. Firstly, to capture the inherent uncertainty of evaluations, interval numbers are used to expresses the values of quantitative criteria while probability linguistic term sets are used to scale the qualitative criteria. Then, a preference function for both quantitative and qualitative criteria is proposed. In addition, a conflict analysis is presented and added to the PROMETHEE, which can derive the preference, indifference, and incomparability (PIR) relations of alternatives. A reference point is given to select the thresholds for the PIR relations. Finally, the improved PROMETHEE is highlighted by a case study concerning site selection of the wind power plant.

A weight-consistent model for fuzzy supplier selection and order allocation problem

Decision support for Supplier Selection and Order Allocation (SSOA) is an important application area of multiple criteria decision making (MCDM) problems. In Amid et al. (Int J Prod Econ 131(1):139–145, 2011) proposed and developed a weighted maximin model to ensure the weight-consistent solution for SSOA in an MCDM problem under an uncertain environment. Essentially, this model is based on a weight-consistent constraint and a maximin aggregation operator. This paper reanalyzes the weighted maximin model in terms of the weight-consistent constraint, and then proposes a general weight-consistent model for SSOA in MCDM problems under uncertainty. In this paper, two existing models are reviewed and compared with the proposed model. Three datasets with different ranges of fuzzy demand and full factorial patterns of criteria weights are used to test the performances of the related models. The results showed that the proposed model always generates a weight-consistent Pareto-optimal solution in all cases, while the other existing models do not.

Multi-criteria decision making via multivariate quantiles

A novel approach for solving a multiple judge, multiple criteria decision making (MCDM) problem is proposed. The presence of multiple criteria leads to a non-total order relation. The ranking of the alternatives in such a framework is done by reinterpreting the MCDM problem as a multivariate statistics one and by applying the concepts in Hamel and Kostner (J Multivar Anal 167:97–113, 2018). A function that ranks alternatives as well as additional functions that categorize alternatives into sets of “good” and “bad” choices are presented. The paper shows that the properties of these functions ensure a reasonable decision making process.

VIKOR method for ranking concrete bridge repair projects with target-based criteria

Abstract With the development of intelligent and automated information system in the information era, the need for more efficient decision making method is growing. An effective method used to rank concrete bridge projects affected by reinforcement corrosion damage will help decision makers of bridge management agencies to better understand the different degrees of urgency for each project. The priority ranking process of different bridge repair projects is complex, since it is hard for decision makers to directly decide which bridge has higher priority when considering many criteria at the same time. The ranking of the different bridge repairing projects can be similar to solving multiple criteria decision making (MCDM) problems. The VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian) method is a distance-to-target method which is developed and used to solve MCDM problems in material selection research. Since weights play a significant role in the priority ranking problems, objective weights based on Entropy concept and Target-based standard deviation method and dependent weights are also applied in this study. Several important criteria, which have not been considered in previous literatures, are proposed and the influence of these criteria to the bridge condition is checked and verified by using data information in NBI databased. A numerical example is proposed to illustrate the application of proposed method. Therefore, in this research a new ranking system methodology based on weighted VIKOR method with proposed criteria for ranking bridge projects is presented.

A framework for the evaluation of routing protocols in opportunistic networks

The evaluation of routing protocols for opportunistic networks can be seen as a multidimensional problem because it involves several performance aspects. To capture these aspects various evaluation metrics are used, such as the number of delivered packets, the delivery delay and the number of transmissions. Unfortunately, in the context of opportunistic networks, these metrics are often highly correlated and usually conflicting. To make things worse, the characteristics of the network affect the importance of each metric as well as the levels of its correlation with other metrics. In this work, we first propose a set of performance evaluation metrics that are normalized with respect to the optimal performance. This approach tackles several of the above-mentioned shortcomings of traditional evaluation metrics. We then formulate the evaluation of routing protocols as a Multiple-Criteria Decision-Making (MCDM) problem where each routing protocol is an alternative and the performance metrics correspond to a set of criteria. We use this formulation to develop an evaluation framework that objectively ranks the performance of opportunistic routing protocols. To this end, we reshape well-known concepts and algorithms from the MCDM field to address the special requirements that are specific to the opportunistic context. We present detailed simulation results of well-known routing protocols in various opportunistic environments and rank their performance according to the proposed framework. In conclusion, no algorithm was able to achieve the best performance in all or the majority of the network topologies that we studied. This demonstrates the diversity of challenges that routing mechanisms face in such networks.