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In this paper, we consider multi-criteria decision-making problems of selecting the best decision alternative in which the preferences of the decision maker are given in the form of quasi-orders. If the decision maker's preferences are modeled by a family of complete quasi-orders (in particular, by a parametric family of additive value functions) then a common approach to solving such problems is based on the notion of potential optimality. In this paper, we consider the case in which the decision maker's preferences are modeled by a family of incomplete quasi-orders. We suggest a new approach to selecting decision alternatives by introducing the notion of potential nondominated alternatives. Such alternatives can be regarded as candidates for the best alternative. We investigate properties of the sets of potential nondominated alternatives and, for the case of a parametric family of partial quasi-orders, we develop a method of their identification. Our development is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: "How is it done?". The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities. [ABSTRACT FROM AUTHOR]

Analytical Hierarchy Process (AHP) has evolved since its inception and has contributed mainly to multi-criteria decision-making (MCDM) to solve complex problems. It is a widespread technique that uses group decision-making approach to solve problems in business operations, mathematics, and numerous other scientific fields. While its applications are numerous, a handful of researchers have studied the growth trajectory of AHP. Whilst these seminal works have discussed the evolution of AHP in different contexts and using contemporary techniques, we complement them by presenting and reflecting on the unchartered and pressing need of charting the managerial and theoretical evolution of AHP, over the last decade. We do this through a systematic and thematic review of the extant literature. This study uses the Scopus database to extract papers published between 2012 and 2021 in decision science with an overlap with operations research and information systems. The study follows Tranfield et al. (2003)'s guidelines to understand the past trends of AHP along three key dimensions and offers future directions. The study also showcases key trends using historiography and uses data visualization techniques to understand the use of AHP with other MCDM techniques. [ABSTRACT FROM AUTHOR]

Multicriteria decision making (MCDM) is a field that helps decision makers evaluate alternatives based on multiple criteria and encompasses scoring, distance-based, pairwise comparison, and outranking methods. Recent developments have aimed to solve specific problems and overcoming the limitations of previous methods. This paper proposes a new axial-distance-based aggregated measurement (ADAM) method, which is used in combination with the best-worst method (BWM) to evaluate agri-food circular economy (CE)-based business models (BMs) to create a more sustainable and efficient system for producing and consuming food. This paper proposes nine BMs, which were evaluated against eight criteria. The BWM method was used to obtain the criteria weights, while the ADAM method was used to obtain a final ranking of the BMs. The results indicate that a sustainable circular agri-food supply chain is a BM that can bring companies the most significant progress in business and strengthen their position in the market. We concluded that the ADAM method is effective for solving MCDM problems and that, overall, the model is an effective tool for solving the problem defined in this study. The main contributions are the development of a new MCDM method and a hybrid model, the establishment of the framework for evaluation and selection of CE-based BMs, and the identification of the most important ones. [ABSTRACT FROM AUTHOR]

The goal of city logistics (CL) is to reconcile the interests of all stakeholders and find compromise solutions for problems associated with the urban logistics flows. A great number of CL initiatives have been the subject of research, analysis and practical application. Some of them indicated significant positive effects, but their implementation brought only partial effects. The non-systematic approach in problem-solving and neglect of other stakeholders' interests by the local government caused the failure of many applied CL initiatives. The purpose of this paper is to evaluate the CL initiatives categories sustainability, taking into consideration the goals of all stakeholder groups. This is the first paper analyzing various CL initiatives categories simultaneously, from the aspect of providers, shippers/receivers, local authorities, and residents, which is the main contribution of the paper. Twelve basic CL initiative categories were considered, while their assessment and ranking were performed with a novel hybrid multi-criteria decision-making (MCDM) model based on fuzzy Analytic Hierarchy Process and fuzzy Measurement Alternatives and Ranking according to Compromise Solution, which is another contribution of the paper. The results indicate that the development and implementation of logistics centers emerge as the best CL initiative category for solving CL problems in the city of Belgrade. The theoretical implications of this article are the creation of a framework for the evaluation of the sustainability of CL initiatives, as well as the novel MCDM model that could be used for problem-solving in different areas. The practical implication of the article is the provision of a framework for decision-makers, policy-creators and practitioners for supporting the process of plans development and initiative implementation in the field of CL. [ABSTRACT FROM AUTHOR]

In literature, several models which can handle uncertainty in datasets have been introduced. Fuzzy set introduced by Zadeh in 1965, is one of the earliest such models and Atanassov generalised it by introducing the notion of Intuitionistic fuzzy sets(IFS) in 1986. However, these models are handicaped due to their inadequacy as parameterization tools. The notion of Soft sets (SS) was introduced by Molodtsov in 1999 to solve this problem. Almost at the same time, Neutrosophic set (NS) model was introduced by Smarandache, which is a huge generalisation of IFS. As has been the practice, the hybrid model of SS and NS was proposed to frame the notion of Neutrosophic Soft Set (NSS) by Ali and Smaranche in 2015 and studied their properties. Since its inception, one of the major areas of application of Soft Sets has been that of Multi-criterian Decision Making (MCDM). Many problems in MCDM were solved by using hybrid models of SS. Following this trend, in this paper, we develop an algorithm basing upon NSS to handle the problem of MCDM in the selection of faculty through an interview prcess. For this, we had to introduce an improved score function which is used to rank the candidates basing upon several of their characteristics including interview perfromances. This application is better handled by the NSS model as is evident from the results. We illustrated the superiority of our proposed algorithm by providing a comparative analysis with many exieting algorithms in the literature. [ABSTRACT FROM AUTHOR]

Commentary is presented for the article "Remarks on the Analytic Hierarchy Process," by James S. Dyer, published in the March 1, 1990 issue of "Management Science." The author discusses the merits of various theories of decision making. He notes one in particular, the analytic hierarchy process (AHP), which is a theory of measurement designed to aid decision makers in deconstructing complex problems into multiple levels of subproblems that have a hierarchical structure. According to the author, as a theory, AHP exists independently of utility theory.

Operational research is a scientific discipline related to the decision theory that allows determining solutions for specific problems related to, for example, widely understood transport. Increasingly popular in this field are issues related to the domain of the green urban transport. In order to support the decision-making process in this area, methods of multi-criteria decision analysis (MCDA) are used more and more often. However, if we solve a specific problem using different MCDA methods, we get different rankings, as each method has a different methodological basis. Therefore, the challenge is how to make a reliable decision. This paper presents a numerical example from the green urban transport domain, which is solved by six different MCDA methods that return a complete ranking. We measure the similarity of these rankings using coefficients rw and WS, and then we propose a simple way of determining a compromise solution. The obtained compromise ranking is guaranteed to be the best match to the selected MCDA methods' rankings, which is proved in the paper. Finally, possible directions for further development work are identified. [ABSTRACT FROM AUTHOR]

According to a new study by the International Labor Organization (ILO), the COVID-19 pandemic has had a strong impact on the garment industry in the Asia-Pacific region. A sharp drop in retail sales in key export markets has affected workers and businesses across supply chains. To ensure the effectiveness and efficiency of garment supply chain, choosing a sustainable supplier should be a main concern of all businesses. The supplier selection problem in garment industry involves multiple quantitative and qualitative criteria. There have been many research and literatures about the development and application of Multicriteria Decision Making (MCDM) models in solving decision-making problems in different industry sectors such as supplier selection or investment assessment. Many different MCDM models have been introduced over the years, and each model is uniquely dedicated into solving a particular problem. There is very little MCDM models incorporated with fuzzy set theory to support decision makers with decision-making problem in uncertain environments. This paper introduces a Fuzzy MCDM-based approach to the problem by utilizing Fuzzy-Analytic Hierarchical Process (FAHP) and Weighted Aggregated Sum Product Assessment (WASPAS) methods to support the decision makers. The aim of the paper is developing a decision-making tool that supports the decision maker in deciding the suitable supplier in garment industry under fuzzy environment. The proposed MCDM model is applied to a real-world case study to demonstrate the application steps of the model as well as its feasibility. The model assisted in successfully its proposed goals that resulted in an optimal supplier in garment industry. [ABSTRACT FROM AUTHOR]

Multiple Criteria Decision Analysis (MCDA) sorting models are highly relevant for solving real-world problems. Thus, in the literature, the great majority of MCDM methods tackled the choice or ranking problems unlike the sorting approaches although assigning alternatives to predefined homogeneous categories (classes) presents a complex problem. Thus, in this paper, we tackled the sorting problematic using the EDAS "Evaluation based on Distance from Average Solution" method. It is used for ranking alternatives, in a decreasing order, according to their Appraisal Scores (AS). Nevertheless, the current version of EDAS method cannot deal with sorting problems. Since a great majority of real-world decision-making problems are modeled as sorting ones, we proposed a new sorting MCDM method called EDAS-Sort to cope with decision problems requiring assigning alternatives to predefined and ordered classes. Given that we dealt with classes defined by their boundary profiles, the proposed method is called EDASSort-B. To demonstrate and underline it, we presented a case study on a bank agency located in Sfax, Tunisia which aims to assign clients requesting loans to three predefined and ordered categories: very solvent, solvent, and doubtful according to various criteria. Therefore, the head of the bank agency (decision maker) will gain insight on the client's profile and whether he is trustful or not to repay the loan. Thus, the EDAS-Sort-B is effective for solving problems requiring assigning alternatives to predefined and ordered categories. Thereupon, the main advantage of EDAS-Sort-B is to help the DM "Decision Maker" to take a real-time decision related to alternatives' assignment. [ABSTRACT FROM AUTHOR]

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are composed of a set of q-rung orthopair fuzzy numbers (q-ROFNs) with membership and nonmembership degrees of linguistic variables, which can be regarded as an extension of linguistic intuitionistic fuzzy numbers (LIFNs) and linguistic Pythagorean fuzzy numbers (LPFNs). Compared with LIFNs and LPFNs, Lq-ROFNs have a wider range of applicability because the value of q can have a wider range, thus allowing to handle more multiattribute group decision making (MAGDM) problems. Based on Lq-ROFS, this paper first proposes the Chebyshev distance metric, then develops the Chebyshev distance entropy for deriving objective solution of decision makers' (DMs') weight vector and attribute weight vector by combining the metric and entropy measure. The TODIM method has been widely valued by the society and has achieved good results in many MAGDM problems. Subsequently, the TODIM decision method in Lq-ROFS is presented and combined with the Chebyshev distance entropy model as a new decision method (CDE-TODIM) to solve the MAGDM problems. This method process is objective and direct and takes into account the decision maker's preferences, and the decision results are more persuasive. Finally, the effectiveness and rationality of this MAGDM method are illustrated by a case as well as comparisons. [ABSTRACT FROM AUTHOR]

The ranking of intuitionistic fuzzy numbers is paramount in the decision making process in a fuzzy and uncertain environment. In this paper, a new ranking function is defined, which is based on Robust's ranking index of the membership function and the non-membership function of trapezoidal intuitionistic fuzzy numbers. The mentioned function also incorporates a parameter for the attitude of the decision factors. The given method is illustrated through several numerical examples and is studied in comparison to other already-existent methods. Starting from the new classification method, an algorithm for solving fuzzy multi-criteria decision-making (MCDM) problems is proposed. The application of said algorithm implies accepting the subjectivity of the deciding factors, and offers a clear perspective on the way in which the subjective attitude influences the decision-making process. Finally, a MCDM problem is solved to outline the advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Multiple criteria decision making (MCDM) problems in practice may simultaneously contain both redundant and incomplete information and are difficult to solve. This paper proposes a new decision-making approach based on soft set theory to solve MCDM problems with redundant and incomplete information. Firstly, we give an incomplete soft set a precise definition. After that, the binary relationships of objects in an incomplete soft set are analyzed and some operations on it are provided. Next, some definitions regarding the incomplete soft decision system are also given. Based on that, an algorithm to solve MCDM problems with redundant and incomplete information based on an incomplete soft set is presented and illustrated with a numerical example. The results show that our newly developed method can be directly used on the original redundant and incomplete data set. There is no need to transform an incomplete information system into a complete one, which may lead to bad decision-making due to information loss or some unreliable assumptions about the data generating mechanism. To demonstrate its practical applications, the proposed method is applied to a problem of regional food safety evaluation in Chongqing, China. [ABSTRACT FROM AUTHOR]

Reliable and effective selection of logistic handling operator is a particularly demanding process due to the short reaction time or high level of accompanying stress. Moreover, diversification of transported cargo makes use of classical indicators and methods of carrier selection highly unsatisfactory for decision-makers. To solve this problem, managers are seeking multi-criteria decision methods that improve the decision-making process related to the selection of the carrier and reduce the risk indicator related to the incorrect implementation of the transport order. Thus, in this paper, we present a Multi-Criteria Decision-Making (MCDM) approach for selecting logistic handling operators under partial or incomplete information (uncertainty) and taking into account the different type of transported cargo. The proposed approach comprises 2 main steps. In the 1st step, we identify the input parameters, mainly connected with criteria for carrier selection depending on the type of transported cargo. In the 2nd step, experts provide linguistic ratings to the potential alternatives against the selected criteria and the best alternative is chosen. At this stage, the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach is used. Later, the applicability of the developed method is presented based on the chosen case company. The comparison of classical and fuzzy approaches to decision-making process is also given. [ABSTRACT FROM AUTHOR]

Today, most of the issues and challenges faced by managers and decision makers are complex and multifaceted. More clearly, due to the developments of technologies, emerging trends in various industries, competitive markets, and rapid and transformative changes in the business environment, managers and decision makers have faced an uncertain environments and issues that cannot be resolved definitively. The use of multi-criteria decision-making (MCDM) methods as a practical and decision-supporting tool allows managers to examine decision-making issues in various organizations and industries based on various criteria, alternatives, and objectives and make decisions with greater reliability. The use of fuzzy techniques and concepts in MCDM methods and their mathematical relationships makes it possible to consider complexities and uncertainties in decisions related to various issues and it can lead to better and more realistic decisions. In this paper, the simplified best-worst method (SBWM), which is one of the methods based on pairwise comparisons, has been developed using triangular fuzzy numbers (TFNs) to propose a fuzzy extension of SBWM (F-SBWM). Triangular fuzzy numbers in different symmetric and asymmetric forms have widely been used in MCDM approaches and pairwise comparisons. It is noteworthy that symmetric numbers are used when we are using equal division of the domain due to an increased ambiguity and lack of information. The proposed approach as a simplified fuzzy MCDM method helps managers and decision makers in various industries to solve decision-making problems under uncertainty without the need for complex calculations, specialized skills, and software packages. To check the feasibility and applicability of the proposed approach, two numerical examples and a computational experiment with real data are presented, and the results are analyzed and discussed. Furthermore, to check the robustness of the results obtained from the proposed approach, sensitivity analysis and comparison of methods have been performed. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool. [ABSTRACT FROM AUTHOR]

One of the most common tools for achieving optimization and adequate production process management is linear programming (LP) in various forms. However, there are specific cases of the application of linear programming when production optimization implies several potential solutions instead of one. Exactly such a problem is solved in this paper, which integrates linear programming and a Multi-Criteria Decision-Making (MCDM) model. First, linear programming was applied to optimize production and several potential solutions lying on the line segment AB were obtained. A list of criteria was created and evaluated using the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA). To obtain the final solution, a novel Rough compromise ranking of alternatives from distance to ideal solution (R-CRADIS) method was developed and verified through comparative analysis. The results show that the integration of linear programming and a Fuzzy-Rough MCDM model can be an exceptional solution for solving specific optimization problems. [ABSTRACT FROM AUTHOR]

This study considers the problem of selecting an anti-tank missile system (ATMS). The mentioned problem is solved by applying a hybrid multi-criteria decision-making model (MCDM) based on two methods: the DIBR (Defining Interrelationships Between Ranked criteria) and the MABAC (Multi-Attributive Border Approximation area Comparison) methods. The methods are modified by applying rough numbers, which present a very suitable area for considering uncertainty following decision-making processes. The DIBR method is a young method with a simple mathematical apparatus which is based on defining the relation between ranked criteria, that is, adjacent criteria, reducing the number of comparisons. This method defines weight coefficients of criteria, based on the opinion of experts. The MABAC method is used to select the best alternative from the set of the offered ones, based on the distance of the criteria function of every observed alternative from the border approximate area. The paper has two main innovations. With the presented decision-making support model, the ATMS selection problem is raised to a higher level, which is based on a proven mathematical apparatus. In terms of methodology, the main innovation is successful application of the rough DIBR method, which has not been treated in this way in the literature so far. Additionally, an analysis of the literature related to the research problem as well as to the methods used is carried out. After the application of the model, the sensitivity analysis of the output results of the presented model to the change of the weight coefficients of criteria is performed, as well as the comparison of the results of the presented model with other methods. Finally, the proposed model is concluded to be stable and multi-criteria decision-making methods can be a reliable tool to help decision makers in the selection process. The presented model has the potential of being applied in other case studies as it has proven to be a good means for considering uncertainty. [ABSTRACT FROM AUTHOR]

Multicriteria decision-making (MCDM) is one of the most common methods used to select the best alternative from a set of available alternatives. Many methods in MCDM are presented in the academic literature, with the latest being the Fuzzy Decision by Opinion Score Method (FDOSM). The FDOSM can solve many challenges that are present in other MCDM methods. However, several problems still exist in the FDOSM and its extensions, such as uncertainty. One of the most significant problems in the use of the FDOSM is the loss of information during the conversion of a decision matrix into an opinion decision matrix. In this paper, the authors expanded the FDOSM into the 2-tuple-FDOSM to solve this problem. The methodology behind the development of the 2-tuple-FDOSM was presented. Within the methodology, definitions of the 2-tuple linguistic fuzzy method, which was used to solve the loss-of-information problem that is present in the FDSOM method, are presented. A network case study was used in the application of the 2-tuple-FDOSM. The final results show that the 2-tuple-FDOSM can be used to address the problem of loss of information. Finally, a comparison between the basic FDOSM, TOPSIS, and 2-tuple-FDOSM was presented. [ABSTRACT FROM AUTHOR]

In the current hotel sales trend due to COVID-19 pandemic, few empirical studies have discussed hotel appraisal determinants and prioritization in terms of operational efficiency. This paper presents an innovative approach for appraisal practice efficiency based on hotel appraisal approach and the multiple criteria decision making (MCDM). The DANP-mV model is used to identify the determinants related to actual hotel appraisal practices, including the techniques of the decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based ANP (DANP), and modified VIKOR. The result of influential network relationship map (INRM) and the gaps of determinants to the aspiration level may contribute to improving hotel appraisal efficacy. In practice, the "discounted cash flow" becomes the most influential determinant (dimension) and the "market survey" is the most manageable one. More findings together with an action plan are presented and useful in the real world. Therefore, this innovative approach could help hotel appraisers and related parties, such as hospitality managers, investors, lenders, and decision makers, better manage the evaluation determinants of hotel appraisal efficacy. [ABSTRACT FROM AUTHOR]

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With the emergence of outsourcing logistics and the rapid development of the e-commerce business, Third Party Logistics (TPL) plays an indispensable role in modern business. In the TPL provider selection process, uncertain information brings more challenges to decision makers. This paper uses probabilistic linguistic term sets (PLTSs) to describe uncertain decision making information. Firstly, we propose an improved Decision Making Trial and Evaluation Laboratory method, which allows a certain relationship between decision criteria and calculates criteria weights in multi-criteria decision making (MCDM) problems. Then, in order to make full use of uncertain TPL provider information and maximize the values of data, the probabilistic linguistic complex proportional assessment method is proposed and applied to solve the MCDM problems under probabilistic linguistic environment, which needs much less computation than other MCDM methods. Finally, an application example of TPL provider selection is presented to demonstrate the proposed method. A comparative analysis is further conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background: This paper introduces the use of the 5Ws & H technique, which is the creative problem solving technique based on who, what, when, where, why and how questions, for the establishing of the criteria weights in multi-criteria decision-making (MCDM). Objectives: The main goal of this paper is to adapt and complete the steps of the 5Ws & H technique, usually used in the problem definition phase, to establish the importance of criteria by the methods based on an interval scale. It also aims to verify the applicability of the proposed approach in the selection of the most appropriate blade. Methods/Approach: In terms of prescriptive approach, the creative 5Ws & H technique was used in the weighting step of the frame procedure for MCDM. During synthesis, the additive model was used, whereas interactions among criteria were considered by using the discrete Choquet integral. Results: The first result is a theoretical statement of the weighting scheme for a new decision mechanism. The second result is the application of this scheme in a real-world case-study. Considering interactions among criteria strengthened the decision-making basis in the selection of the most appropriate blade. Conclusion: The creative 5Ws & H technique proved useful in criteria weighting. [ABSTRACT FROM AUTHOR]

When dealing with multi-criteria decision making problems, the concept of Pareto-optimality and Pareto-dominance may be inefficient (e.g. generally multiple solutions exist), especially when there is a large number of criteria. Our paper considers the fuzzy multi-criteria decision making problem based on Zadeh's linguistic approach to P-optimality and P-dominance. The construction, analysis and application of a model of multi-criteria decision making using a fuzzy preference relation are considered. The paper is dedicated to the problem of modeling preferences in terms of fuzzy binary relations and provides an introduction to the important problem of forming fuzzy preference relations to analyze models of multi-attribute decision making. The key features of the multi-criteria evaluation, comparison, choice and ordering of alternatives in a fuzzy environment using fuzzy preference relations have been introduced. [ABSTRACT FROM AUTHOR]

This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields. [ABSTRACT FROM AUTHOR]

Fault tree analysis is often used in elevator fault diagnosis because of its simplicity and reliability. However, the traditional fault tree method has the problems of low efficiency due to ignoring location change of bottom events during troubleshooting. This paper proposes a rapid diagnosis method based on multiattribute decision making to solve the problem. The fault tree of the elevator system is constructed based on expert knowledge and multisource data, and the location-related matrix is constructed according to the complex vertical structure of the elevator. Then, the attributes of bottom events such as the failure probability, search cost, location time cost, and location-related attributes are comprehensively analyzed in this paper. Finally, the TOPSIS method for dynamic attributes is used based on the work above to achieve the optimal troubleshooting sequence of elevator vibration fault. The results show that the proposed method is more efficient when compared to the optimal troubleshooting sequence obtained by the traditional method. [ABSTRACT FROM AUTHOR]

Introduction: Anti Tank Ground Missiles (ATGMs) are one of the most efficient weapon systems to counter armored and mechanized units. Procurement of these weapons is imperative for armed forces of any country. Adequate evaluation and choice of an efficient ATGM system is a very important factor which affects operational capabilities of armed forces. The purpose of this paper is to present that multi criteria methods can be a useful tool for optimal procurement of ATGMs for armed forces. Methods: The implementation of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and the MABAC (Multi Attributive Border Approximation Area Comparison) multi criteria methods will be proposed for decision makers to solve the problem of ATGM procurement. In this paper, four models of ATGMs will be proposed as possible solutions. These ATGMs will be presented as alternatives A1, A2, A3 and A4. Results: The implementation of the methods in this paper has led to the conclusion that the alternative A2 has the highest value and therefore the best course of action for decision makers is to chose procurement of this model of ATGMs. Conclusion: The conclusion is that the mathematical models of multi-criteria decision making presented in this paper unequivocally point to the alternative with the best characteristics, thus presenting extremely useful tools for decision makers. [ABSTRACT FROM AUTHOR]

During the multi-objective optimization process, numerous efficient solutions may be generated to form the Pareto frontier. Due to the complexity of formulating and solving mathematical problems, choosing the best point to be implemented becomes a non-trivial task. Thus, this paper introduces a weighting strategy named robust optimal point selection, based on ratio diversification/error, to choose the most preferred Pareto optimal point in multi-objective optimization problems using response surface methodology. Furthermore, this paper proposes to explore a theoretical gap—the prediction variance behavior related to the weighting. The ratios Shannon's entropy/error and diversity/error and the unscaled prediction variance are experimentally modeled using mixture design and the optimal weights for the multi-objective optimization process are defined by the maximization of the proposed measures. The study could demonstrate that the weights used in the multi-objective optimization process influence the prediction variance. Furthermore, the use of diversification measures, such as entropy and diversity, associated with measures of error, such as mean absolute percent error, was determined to be useful in mapping regions of minimum variance within the Pareto optimal responses obtained in the optimization process. [ABSTRACT FROM AUTHOR]

In modern society, people concern more about the evaluation of medical service quality. Evaluation of medical service quality is helpful for medical service providers to supervise and improve their service quality. Also, it will help the public to understand the situation of different medical providers. As a multi-criteria decision-making (MCDM) problem, evaluation of medical service quality can be effectively solved by aggregation operators in interval-valued q-rung dual hesitant fuzzy (IVq-RDHF) environment. Thus, this paper proposes interval-valued q-rung dual hesitant Maclaurin symmetric mean (IVq-RDHFMSM) operator and interval-valued q-rung dual hesitant weighted Maclaurin symmetric mean (IVq-RDHFWMSM) operator. Based on the proposed IVq-RDHFWMSM operator, this paper builds a novel approach to solve the evaluation problem of medical service quality including a criteria framework for the evaluation of medical service quality and a novel MCDM method. What's more, aiming at eliminating the discordance between decision information and weight vector of criteria determined by decisionmakers (DMs), this paper proposes the concept of cross-entropy and knowledge measure in IVq-RDHF environment to extract weight vector from DMs' decision information. Finally, this paper presents a numerical example of the evaluation of medical service for hospitals to illustrate the availability of the novel method and compares our method with other MCDM methods to demonstrate the superiority of our method. According to the comparison result, our method has more advantages than other methods. [ABSTRACT FROM AUTHOR]

In this paper, hybridization of invasive weed optimization (IWO) and space transformation search (STS) are presented to solve, by applying multiple populations for multiple objectives individually, multiobjective optimization. This whole process is addressed as hybrid cooperative multiobjective optimization IWO (HCMOIWO). We carried out an application to solve system of nonlinear equations. In HCMOIWO, M single objectives are optimized simultaneously using the hybrid IWO with STS and all the nondominated solutions that are extracted from the group of parent weeds and offspring are stored in an archive, A . This archive is used not only to store nondominated solutions, but also to exchange information among subpopulations to explore the new search areas along the Pareto front. To exploit the nondominated solutions, a local search technique is adopted in HCMOIWO. The performance of HCMOIWO is evaluated with different sets of benchmark problems having different characteristics. Empirical results reveal the supremacy of HCMOIWO over state-of-the-art algorithms reported in recent literature. [ABSTRACT FROM AUTHOR]

Decision-making processes increasingly use models based on various methods to ensure professional analysis and evaluation of the considered alternatives. However, the abundance of these methods makes it difficult to choose the proper method to solve a given problem. Also, it is worth noting whether different results can be obtained using different methods within a single decision problem. In this paper, we used three selected Multi-Criteria Decision Analysis (MCDA) methods called COMET, TOPSIS, and SPOTIS in order to examine how the obtained rankings vary. The selection of material suppliers was taken into consideration. The equal weights, entropy and standard deviation methods were used to determine the weights for criteria. Final preferences values were then compared with the WS similarity coefficient and weighted Spearman correlation coefficient to check the similarity of the received rankings. It was noticed that in the given problem, all of the methods provide highly correlated results, and the obtained positional rankings are not significantly different. However, practical conclusions indicate the need to look for improved solutions in the correct and accurate assessment of suppliers in a given period. [ABSTRACT FROM AUTHOR]

Recent research is recognizing that multiple criteria analysis should take account of the concepts of uncertainty and risk. In some cases, precise determination of the exact value of alternatives and weights of criteria is difficult. Consequently, to deal with these potential problems, their values are regarded as fuzzy and intervals. This paper proposes an interval-valued fuzzy multiple criteria complex proportional assessment (IVF-COPRAS) method that can reflect both a subjective judgment and objective information in real-life situations. In this method, the performance rating values versus selected criteria as well as the weights of conflicting criteria are linguistic variables represented by interval-valued triangular fuzzy numbers. Moreover, performances of alternatives against subjective criteria are described via linguistic variables and then transformed into interval-valued triangular fuzzy numbers. Finally, an application example for the robot selection problem is given in this paper to show this decision-making steps and the effectiveness of the proposed method in manufacturing companies. [ABSTRACT FROM AUTHOR]

The objective of the paper is to introduce a novel approach using the multi-attribute border approximation area comparison (MABAC) approach under intuitionistic fuzzy sets (IFSs) to solve the smartphone selection problem with incompleteweights or completely unknownweights. A novel discrimination measure of IFSs is proposed to calculate criteria weights. In view of the fact that the ambiguity is an unavoidable feature of multiple-criteria decision-making (MCDM) problems, the proposed approach is an innovative process in the decision-making under uncertain settings. To express the utility and strength of the developed approach for solving problems in the area of MCDM, a smartphone selection problem is demonstrated. To validate the IF-MABAC approach, a comparative discussion is made between the outcomes of the developed and those of the existing methods. The outcomes of analysis demonstrate that the introduced method is well-ordered and effective with the existing ones. [ABSTRACT FROM AUTHOR]

Background: Release planning (RP) is one of the most complex and relevant activities in the iterative and incremental software development, because it addresses all decisions associated with the selection and assignment of requirements to releases. There are many approaches in which RP is formalized as an optimization problem. In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. Since RP is a wicked problem with a large focus on human intuition, the decision maker's (DM) opinion is a relevant issue to be considered when solving release planning problem. Thus, we emphasize the importance in gathering the DM's preferences to guide the optimization process through search space area of his/her interests. Methods: Typically, RP is modelled as a multi-objective problem by considering to maximize overall clients satisfaction and minimize project risk. In this paper, we extend this notion and consider DM's preferences as an additional objective. The DM defines a set of preferences about the requirements allocation which is stored in a preference base responsible for influencing the search process. The approach was validated through an empirical study, which consists of two different experiments, respectively identified as (a) automatic experiment and (b) participant-based experiment. Basically, the former aims to analyze the approach using different search-based algorithms (NSGA-II, MOCell, IBEA, and SPEA-II), over artificial and real-world instances, whereas the latter aims at evaluating the use of the proposal in a real scenario composed of human evaluations. Results: The automatic experiment points out that NSGA-II obtained overall superiority in two of the three datasets investigated, positioning itself as a superior search technique for scenarios with few number of requirements and preferences, while IBEA showed to be better for larger ones (with more requirements and preferences). Regarding the participant-based experiment, it was found that two thirds of the participants evaluated the preference-based solution better than the non-preference-based one. Conclusions: The results suggest that it is feasible to investigate the approach in a real-world scenario. In addition, we made available a prototype tool in order to incorporate the human's preferences about the requirements allocation into the solution of release planning. [ABSTRACT FROM AUTHOR]

In order to solve the mechanical multi-objective optimal design problems, the basic idea and flow chart of collaborative optimization method are introduced in this paper. In view of the shortcomings that exist in standard collaborative optimization method, this method has been improved by applying the dynamic slack factor method. Taking a mechanical multi-objective optimal design of spring as an example, the multi-objective optimal design problem has been solved by the improved collaborative optimization method. The process and result show that the improved collaborative optimization method has higher accuracy and efficiency. This paper has provided an efficient method to solve the complicated mechanical multi-objective optimal design problems. [ABSTRACT FROM AUTHOR]

A traveling salesman problem (TSP) is an NP-hard optimization problem. So it is necessary to use intelligent and heuristic methods to solve such a hard problem in a less computational time. This paper proposes a novel hybrid approach, which is a data mining (DM) based on multi-objective particle swarm optimization (MOPSO), called intelligent MOPSO (IMOPSO). The first step of the proposed IMOPSO is to find efficient solutions by applying the MOPSO approach. Then, the GRI (Generalized Rule Induction) algorithm, which is a powerful association rule mining, is used for extracting rules from efficient solutions of the MOPSO approach. Afterwards, the extracted rules are applied to improve solutions of the MOPSO for large-sized problems. Our proposed approach (IMOPSP) conforms to a standard data mining framework is called CRISP-DM and is performed on five standard problems with bi-objectives. The associated results of this approach are compared with the results obtained by the MOPSO approach. The results show the superiority of the proposed IMOPSO to obtain more and better solutions in comparison to the MOPSO approach. [ABSTRACT FROM PUBLISHER]

In some cases of using multi-criteria decision making methods for solving real-world problems ratings of alternatives cannot be determined precisely, and that is why they are expressed in the form of intervals. Therefore, the aim of this paper is to extend the MOORA method for solving decision making problems with interval data. By extending the ratio system part of MOORA method, an algorithm to determine the most preferable alternative among all possible alternatives, when performance ratings are given as intervals, is presented. Finally, an example is shown to highlight the proposed procedure, at the end of this paper. [ABSTRACT FROM AUTHOR]

In the past, decision making within the energy sector, especially in Iran, was limited to economic analysis. Lately, multiple criteria decision making has gained great popularity. However, this is not enough to make a right decision by considering sustainability. This paper deals with designing an appropriate multiple criteria decision making method to address the multifaceted nature of such problems. This task is the second precondition to make a decision which meets the sustainability criterion. This is done by revealing new facts about quantitative and qualitative data and the degree of compensation between the criteria. Moreover, this paper illustrates the capability of different methods with regard to sustainable energy planning and management in two steps: 1) comparison of two main approaches in the strategic energy planning context; 2) evaluation of multi attribute decision making methods and combining them. Combining four methods including Analytical hierarchy process, Preference ranking organization method for enrichment evaluation II, geometric mean and weighted sum seems to result in designing an appropriate method which meets the sustainability criterion. These contributions are proposed for comparing the renewable energy technologies with non-renewable ones. Nevertheless, these seem to be applicable in any comparison between discrete alternatives in the energy sector. [ABSTRACT FROM AUTHOR]

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Recent research is recognizing that multicriteria decision making should take into account the concepts of uncertainty, risk, and confidence. In some cases, precise determination of the exact value of the attributes is difficult. Consequently, to surmount this potential problem, their values are considered as fuzzy and intervals. The elimination and choice translating reality (ELECTRE) is one of the most widely used methods to rank a set of alternatives versus a set of criteria to reflect the decision maker’s preference. What is customary in almost all recent papers regarding ELECTRE is the utilization of fuzzy concepts in decision-making process. In this paper, we propose a novel algorithmic ELECTRE method which assimilates the concepts of interval weights and data for the facilitation of evaluation of a set of alternatives against a set of criteria in this situation. Finally, the executive procedure of our proposed ELECTRE method is illustrated by applying it to the problem of supplier selection. [ABSTRACT FROM AUTHOR]

Due to the unavailability of time series data for newly listed stocks or products, it is a challenge for investors to make rational portfolio selection under uncertain circumstances. To solve the problem, a new approach is put forward in this paper. Firstly, the problem is considered as a multi-criteria decision making (MCDM) problem based on the assumption that the assessments are given in intuitionistic fuzzy set (IFS) form, which can better describe the uncertainty. Then, the TOPSIS method, which is widely used in MCDM problem, has been modified in two aspects. On the one hand, the new definitions of Absolute Positive Ideal Solution (APIS) and Absolute Negative Ideal Solution (ANIS) are proposed to represent the best returns and the greatest cost/risk in portfolio selection. They are more meaningful than the definitions of Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS), which could only express the best and the worst case of portfolio but cannot achieve a balance between returns and risk. On the other hand, the weighted closeness coefficient is refined to offer the more appropriate results that are consistent with investors' demands or preferences. In addition, based on distance measure of IFSs, several novel linear programming models with different constraints are proposed to allocate the investment ratios according to investors' demands. The models can make up for the disadvantage of TOPSIS method which only considers the ranking of investments but neglects the investment ratios. Finally, compared with the conventional TOPSIS method and the IF ELECTRE Method in a numerical example, our new approach is demonstrated to be more effective and more flexible. Particularly, it can provide a more appropriate strategy in accordance with investors' preferences and demands. [ABSTRACT FROM AUTHOR]

In multi-criteria decision-making (MCDM) problems, with the increasing number of decision makers and candidate objects, some traditional decision-making approaches can no longer be applied to such scenarios, and how to fully ensure the participation of each decision maker as well as reasonably integrate the decision makers' assessment information on the objects becomes a problem. As a risk-averse decision-making method, three-way decision can be applied to MCDM to greatly reduce the decision-making risk. In this paper, combining the decision makers' criterion preferences and the three-way decision model, we propose three novel three-way MCDM models incorporating multiple criterion preferences to solve this problem. Firstly, a novel criterion preference-based decision risk loss measure function is proposed, and a new way of fusing multiple decision risk loss functions is established. Next, the description of the objects is obtained by the affinity propagation clustering algorithm, and three criterion preferences aggregation strategies (optimistic, compromise and pessimistic) are proposed to cope with the demand for group in three scenarios (two extremes and a moderate), respectively, then three conditional probability estimation methods are established. Subsequently, three three-way MCDM models are built to obtain the ranking results of all candidate objects in different group demand scenarios. In addition, three ranking performance testing indices are introduced to evaluate the reasonableness of the ranking results. Finally, a case application, comparative experiments, data set experimental analysis, statistical test analysis and comprehensive discussion are presented to verify the rationality, effectiveness and superiority of the proposed methods. • A criterion preference-based decision risk loss measure method is proposed. • An AP clustering algorithm is utilized to obtain the description of the objects. • Three conditional probability estimation approaches are established. • Three three-way MCDM models fusing multiple criterion preferences are developed. [ABSTRACT FROM AUTHOR]

In material handling, warehousing, manufacturing and construction applications, forklifts are vital equipment, which are used to engage, lift and move palletized items. So, selection of the most appropriate forklift is an essential task for transportation of materials in warehouses for optimal use of the equipment. The present treatise introduces a wellknown multi-criteria decision making (MCDM) technique, namely fully consistent method (FUCOM) under neutrosophic environment (NE) to model and solve the problem of selecting the best forklift for warehouse. In this regard, the linguistic assessments of the criteria have been represented in terms of single valued triangular neutrosophic numbers (SVTNNs). A novel triangular neutrosphic score function and ranking function are also proposed. To calculate criteria weights, a novel SVTN linear programming problem (SVTNLPP) has been developed. The alternatives have been ranked through multi-objective optimization on the basis of ratio analysis (MOORA). The robustness, consistency and reliability of the proposed integrated method have been checked through comparative and sensitivity analyses. This study makes a significant contribution by developing an original integrated model which provides warehousing system managers a quantifiable analysis, based on which they may make future decisions in order to improve the overall efficiency of the organization in transport handling. [ABSTRACT FROM AUTHOR]

In a typical project portfolio optimization problem, the decision makers would choose the optimal project portfolio from the candidates under the constraints of certain resources. However, in lots of real world examples, traditional approaches cannot perfectly solve the project portfolio optimization problems with unknown information about the importance of attributes. An innovative feature of this paper is to propose a stochastic multiattribute acceptability analysis (SMAA) to solve the multiattribute project portfolio optimization problems. SMAA includes a series of methods, which provide full ranks for a set of alternatives by exploring the weight space to make each alternative the most preferred one. Our proposed method provides feasible procedures to deal with the project portfolio optimization problems and, as an extension of traditional SMAA methods, expands the SMAA applications from the comparison of single alternatives to the comparison of sets of alternatives. [ABSTRACT FROM AUTHOR]

Uncertain random variables are used to describe the phenomenon of simultaneous appearance of both uncertainty and randomness in a complex system. For modeling multi-objective decision-making problems with uncertain random parameters, a class of uncertain random optimization is suggested for decision systems in this paper, called the uncertain random multi-objective programming. For solving the uncertain random programming, some notions of the Pareto solutions and the compromise solutions as well as two compromise models are defined. Subsequently, some properties of these models are investigated, and then two equivalent deterministic mathematical programming models under some particular conditions are presented. Some numerical examples are also given for illustration. [ABSTRACT FROM AUTHOR]

This paper throws doubt on the direct relationship between the difficulty of a decision problem and decision time. A formal theory is proposed, hypothesizing that decision makers take longer to choose from four alternatives when two of the alternatives are easily rejected than when all four alternatives are equal. The results of laboratory experiments support this hypothesis, and suggest that decision behavior is related to personality factors. [ABSTRACT FROM AUTHOR]

This paper proposes a new Multiple-criteria decision-making (MCDM) method called MUltiple-TRIangles ScenarioS (MUTRISS) with two scenarios respecting different levels of access to complete information for material selection problems. MUTRISS calculates the areas occupied by alternatives in n-dimensional space, employing analytic geometry and converting each alternative into n-edges forms. The paper applies MUTRISS to three material selection case studies, with Ti-6Al-4V, Material 4, and AISI 4140 Steel- UNS G41400 emerging as the best materials for the three examples with the highest overall scores of 0.036, 4.540 and 0.427 respectively. The results are compared with various MCDM methods through four statistical measures, including relative closeness ratio, robustness analysis, compromise ranking coefficient, and similarity degree. The measures focus on different aspects of MCDM methods in solving problems and their results. The paper concludes that MUTRISS offers a more robust and reliable approach for material selection problems compared to other MCDM methods, with the first scenario of MUTRISS being more reliable than the second scenario. The paper also emphasizes the importance of validating results in material selection problems due to the potential irreversible consequences of selecting the wrong material. [ABSTRACT FROM AUTHOR]

The article presents an assessment of the productibility of hybrid nodes. The hybrid node is a new structural element the implementation of which causes numerous (especially technological) problems that need to be solved. The most important element of the hybrid node is the so-called connector, the choice of which is a complex and difficult problem. It involves taking into account many aspects (constructional, strength-related, technological, economic) in order to make sure that the choice is as objective as possible. Therefore, an attempt to acquire a comprehensive view at the problem requires that a set of accurate criteria be used for assessment. In this paper, the author undertakes such an attempt. The expert method presented herein allows for choosing the right connector. [ABSTRACT FROM AUTHOR]