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This paper presents an improved version of the well-established k nearest neighbor ( k -NN) and fuzzy NN (FNN), termed the multi-objective genetic-algorithm-modified FNN (MOGA-MFNN). The MFNN design problem is converted into a multi-modal objective maximization problem constrained by four objective functions. Thereafter, the associated parameter set of the MFNN and the feature attributes can be determined optimally and automatically via the non-dominated sorting genetic algorithm II. We introduce two new objective functions termed the Margin-I and Margin-II, which are used to improve the generalization capability of the MFNN for the unknown data, along with two existing performance functions: the geometric mean and the area under the receiver-operated characteristic curve for the training accuracy. Moreover, we proposed a novel data-dependent weight-assignment technique for local class membership functions of the MFNN. The technique enables the MFNN to determine its local neighbors adaptively through the MOGA algorithm. To expedite the classification, the MOGA-MFNN is implemented on a graphical processing unit (GPU), which significantly increases the computation speed. Furthermore, the local class-membership function of the MFNN can be computed in advance, rather than delaying it to the classification stage. This again can improve the classification speed. The MOGA-MFNN is evaluated on 20 datasets obtained from the repository of the University of California, Irvine (UCI). The experiments with rigorous statistical significance tests demonstrate that the proposed method performs competitively with the existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for treating uncertainty in the multi-criteria decision making process by introducing interval rough numbers (IRN). The IRN approach enables decision making using only the internal knowledge incorporated in the data provided by the decision maker. A hybrid multi-criteria model was developed based on IRN, and demonstrated using the example of the bidder selection process in the state administration public procurement procedure. The first segment of the hybrid model deals with the rough interval DEMATEL-ANP (IR'DANP) model, which enables more objective expert evaluation of criteria in a subjective environment than the traditional/crisp approach. In the second segment, the evaluation is enabled by applying the new rough interval MAIRCA method, which introduces mathematical tools and shows high stability concerning changes in the nature and characteristics of the criteria. The results of the hybrid IR'DANP-MAIRCA model were analyzed using 36 scenarios of sensitivity analysis, which showed high stability of the results. The results of the interval rough method were compared with the fuzzy extensions of the TOPSIS, VIKOR, MABAC, TODIM, ELECTRE I and DEMATEL-ANP models. [ABSTRACT FROM AUTHOR]

In this paper, a novel two-archive Multi-Objective Grey Wolf Optimizer (2ArchMGWO) is proposed for solving Multi-Objective Optimal Reactive Power Dispatch (MORPD) problems. The optimizer has been improved from its original Multi-Objective Grey Wolf Optimizer (MGWO) by modifying the reproduction operator and adding the 2-archive concept to the algorithm. It is then implemented on solving MORPD with objective functions being active power loss minimization and voltage profile improvement (voltage deviation minimization). The generator bus voltages, tap setting transformers and shunt reactive power sources or flexible alternating current transmission systems are set as design variables. The proposed algorithm along with other existing multiobjective optimizers are applied to solve three test problems with the standard IEEE 30-bus, IEEE 57-bus, and the IEEE 118-bus power systems. The optimum results obtained from the various optimizers performance are compared based on the hypervolume indicator and they reveal that 2ArchMGWO is clearly superior to the others. [ABSTRACT FROM AUTHOR]

LNG importing strategies, in the literature, are primarily studied under a common single-factor framework. However, LNG importing strategies are affected by a variety of factors. To address this existing gap, this paper proposes a Multi-Objective Programming model, which takes into account the cost, the country risk, the shipping risk, and the impact of extreme events. A pure structural change model is used to determine the risk impact coefficient for extreme events. An enhanced Simulated Annealing Algorithm is then used to solve the LNG-importing optimization problem. An experimental study is further conducted to verify the practicability of the proposed approach in the case of China's LNG-importing data. The software implementation of the proposed model is developed in Python. The proposed model provides a decision support tool for LNG importing companies to find an efficient portfolio strategy for LNG importing. The optimization model can be used for analyzing similar scenarios involving such dimensions as economy, energy security, and especially energy diversification. [ABSTRACT FROM AUTHOR]

Uncertainty is one of the main difficulties that increases the complexity of multi-criteria decision analysis (MCDA) problems, and often uncertainty cannot be managed by probabilistic models. In such cases, the use of fuzzy methods has been successfully applied to multi-criteria decision methods in which the ranking of fuzzy quantities is crucial for the decision analysis. This paper aims to introduce a new approach to MCDA problems defined under fuzzy contexts that implements the concept of acceptability analysis, Fuzzy Multi-Criteria Acceptability Analysis (FMAA), based on the Fuzzy Rank Acceptability Analysis (FRAA), that provides a ranking and a confidence degree about the ranking of fuzzy quantities. Based on the fuzzy extension of MAVT method, the FMAA is implemented and then applied to a case study, and its results are compared with other well-known MCDA methods in order to show its validity, interpretability and consistency. [ABSTRACT FROM AUTHOR]

The problem-solving decision-making process often requires involvement of a group of individuals who have differing interests and conflicting multiple evaluation criteria. Therefore, the greatest concern in multiobjective group decision-making problems is how to arrive at a best decision that is agreeable to all the members of the group. Many previous studies focused on handling this concern based on decision rules, such as the consensus or ranking selection approaches. Although many contributions to the literature were made by past studies on this issue, disagreement remains on finding an effective way to address the subjectivity issue in group decision-making. This paper introduces a new approach called the preference clustering-based mediating group decision-making (PCM-GDM) method for minimizing the subjectivity issue. The PCM-GDM method basically employs two concepts: (1) clustering the preferences of the group members in a decision and (2) utilizing a mediating agent as a final decision-making tool. The new approach was applied to a case study of sample concrete bridge decks in the state of Indiana. The results of this study confirm that the proposed approach can significantly improve the multicriteria group decision-making results by providing a way to exclude biased judgments by decision-makers that can interfere with the development of one best alternative. The proposed approach advanced the reliability of the conventional decision-making knowledge, which is dependent on a consensus or the ranking of approaches by human experts to reach one solution. [ABSTRACT FROM AUTHOR]

In general, traditional decision-making models are based on methods that perform calculations on quantitative measures. These methods are usually applied to assess possible solutions to a problem, resulting in a ranking of alternatives. However, when it comes to making decisions about qualitative measures —such as service quality—, the quantitative assessment is a bit difficult to interpret. Therefore, taking into account the maturity of the linguistic assessment models, this paper puts forth a new solution proposal. It is a decision-making model that uses linguistic labels —represented with the 2-tuple notation— and a variable expressive richness when providing output results. This solution allows expressing results in a manner closer to the human cognitive system. To achieve this goal, a mechanism has been implemented for measuring the distance among the aggregate ratings, providing the decision-maker with a fast and intuitive answer. The proposal is illustrated with an application example based on the TOPSIS model, using linguistic labels throughout the entire process. [ABSTRACT FROM AUTHOR]

Transshipment hub selection becomes increasingly important to the global logistics community. From the perspectives of shippers and freight forwarders, a selection must align with cost control strategy and sustain service reliability across cooperative service providers. This paper assesses the selection with the options of both sea and air transports, and from the influence of country of origin of the company. Critical factors of transshipment hub selection, both qualitative and quantitative, are identified through focus group discussions. Relative importance of these factors is determined based on collective views of logistics stakeholders. The competitiveness of transshipment hubs is then assessed using an AHP approach. Our analysis is based on the historical implementation of direct transportation link policy between Mainland China and Taiwan. With this empirical work, the finding suggests that even the spawn of other nearby ports with shorter transport distance and closer proximity to cargo sources, there remain overriding factors such as customs regulations & government policies and connectivity that a transshipment hub is preferred. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Analysis (MCDA) enables decision makers (DM) and decision analysts (DA) to analyse and understand decision situations in a structured and formalised way. With the increasing complexity of decision support systems (DSSs), it becomes challenging for both expert and novice users to understand and interpret the model results. Natural language generation (NLG) techniques are used in various DSSs to cope with this challenge as they reduce the cognitive effort to achieve understanding of decision situations. However, NLG techniques in MCDA have so far mainly been developed for deterministic decision situations or one-dimensional sensitivity analyses. In this paper, a concept for the generation of textual explanations for a multi-dimensional preferential sensitivity analysis in MCDA is developed. The key contribution is a NLG approach that provides detailed explanations of the implications of preferential uncertainties in Multi-Attribute Value Theory (MAVT). It generates a report that assesses the influences of simultaneous or separate variations of inter-criteria and intra-criteria preferential parameters determined within the decision analysis. We explore the added value of the natural language report in an online survey. Our results show that the NLG approach is particularly beneficial for difficult interpretational tasks. [ABSTRACT FROM AUTHOR]

The literature on supply base segmentation has increasingly adopted multi-criteria decision making (MCDM) techniques into recently proposed models. However, most proposals segment the supply base from the standpoint of the purchased item, which prevents them from providing guidelines that are specific to each supplier. Some authors have attempted to overcome these limitations by putting forward portfolio models based on the relationship with suppliers. These approaches use fuzzy variables and MCDM methods that take qualitative judgements by experts as the only input for decision making. However, many companies have databases with historical data about the performance of past transactions with suppliers that should be considered by expert systems that aim to comprehensively evaluate suppliers’ performance. This paper seeks to address this gap by proposing a segmentation model based on the relationship with suppliers capable of aggregating quantitative and qualitative criteria. Analytic Hierarchy Process (AHP) was used to determine the relative importance of each criteria. Fuzzy 2-tuple, a prominent computing with word (CWW) approach, was used to evaluate suppliers with a mixture of historical quantitative data and qualitative judgements by purchasing experts. An illustrative application of the proposed model was carried out in the pharmaceutical supply center (PSC) of a teaching hospital. The proposed model can be viewed as a decision support system capable of aggregating the qualitative judgements of experts and quantitative historical performance measures, thus providing guidelines to improve the relationship between suppliers and the buyer firm. [ABSTRACT FROM AUTHOR]

• A novel consensus method has been developed for multi-agent decision systems. • The method is specially designed to allow large groups of decision-makers. • Social rules and component division are introduced to improve the method response. • The method allows to manage previous influence. • HIVES can be applied beforehand to obtain group decisions for any MCDM method. Aggregating multiple opinions or assessments in a decision has always been a challenging field topic for researchers. Over the last decade, different approaches, mainly based on weighting data sources or decision-makers (DMs), have been proposed to resolve this issue, although social choice theory, focused on frameworks to combine individual opinions, is generally overlooked. To resolve this situation, a novel methodology is developed in this paper based on social choice theory and statistical mathematics. This method innovates by dividing the assessment into components which provides a multiple assessment analysis, assigning weights to each source regarding their position compared to the group for each considered criteria. This multiple-weighting process maximises individual and group satisfaction. Furthermore, the method makes it possible to manage previously assigned influence. An example is given to illustrate the proposed methodology. Additionally, sensitivity analysis is performed and comparisons with other methods are made. Finally, conclusions are presented. [ABSTRACT FROM AUTHOR]

The core objective of this article is to portray a novel multi-criteria group decision-making (MCGDM) approach that combines the salient features of the 2-tuple linguistic m -polar fuzzy sets (2TL m FSs) with the theoretical background and accuracy of the well-known ELimination and Choice Translating REality (ELECTRE) II approach. The resulting 2TL m F-ELECTRE II technique outlines the framework and explains how to construct the ELECTRE II model for problems having 2TL m F uncertainties. Initially, for constructing ELECTRE-II methodology in the 2TL m F context, the basic steps are designed to evaluate the pairwise comparison of alternatives utilizing the fundamental concepts of concordance, indifferent, and discordance sets and matrices, respectively. After that, the 2TL m F strong and weak outranking relationships are investigated by comparing the 2TL m F-concordance and 2TL m F-discordance indices with the threshold values (three concordance and two discordance levels) provided by the decision experts. Finally, a complete ranking set is obtained using an iterative procedure on strong and weak outranking graphs. A comprehensive flowchart is supplied to elaborate the brief and clear vision for the mathematical procedure of the intended work. Moreover, we utilized the proposed methodology for selecting adequate sports utility vehicles (SUVs) to confirm the applicability and feasibility of the novel approach. Ultimately, the final outcomes are briefly compared with the existing 2TL m F ELECTRE-I approach to ensure the authenticity and reliability of the proposed work. • This paper addresses the case of providing 2-tuple linguistic m-polar fuzzy data. • We propose a new version of the ELECTRE II method based on 2TLmF information. • We consider a problem to show the practicality and efficiency of the proposed model. • We provide a comparative analysis of the proposed method with existing techniques. [ABSTRACT FROM AUTHOR]

In recent years, there have been dramatic changes in manufacturing systems in many industries depending on technological developments. Robotics is one of the essential components of these changes. Today, the usage of robotics in manufacturing processes has become widespread in almost all industries. Also, it has become a very strong desire ever-increasing for even small and medium-sized enterprises at present. Almost all the previous studies emphasized that industrial robot selection is a highly complex decision-making problem as there are many conflicting factors and criteria. Besides, different and advanced specifications of these robotics added by robotic manufacturers have caused to increase the complexities much more. Hence, decision-makers encounter more complicated decision-making problems affected by many uncertainties. Because of that, an integrated fuzzy group MCDM framework can help overcome many ambiguities proposed in the current paper. The proposed fuzzy integrated model consists of the fuzzy SWARA (F-SWARA'B) and the fuzzy CoCoSo (F-CoCoSo'B), which are extended with the help of the Bonferroni function. The model selected the appropriate industrial robotics used in the automotive industry by considering 15 criteria and ten alternatives. According to the result of the study, the three most significant criteria have been determined: Working Accuracy, Reaching Distance, and Performance; and the most suitable option is the A8. The obtained results were validated with the help of a comprehensive sensitivity analysis consisting of different 150 scenarios. The results are also compared with some existing techniques. The sensitivity analysis results approve the validity and applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Abstract: The TOPSIS method is a technique for order preference by similarity to ideal solution. This technique currently is one of the most popular methods for Multiple Criteria Decision Making (MCDM). The TOPSIS method was primary developed for dealing with only real-valued data. In many cases, it is hard to present precisely the exact ratings of alternatives with respect to local criteria and as a result these ratings are considered as intervals. There are some papers devoted to the interval extensions of TOPSIS method, but these extensions are based on different heuristic approaches to definition of positive and negative ideal solutions. These ideal solutions are presented by real values or intervals, which are not attainable in a decision matrix. Since this is in contradiction with basics of classical TOPSIS method, in this paper we propose a new direct approach to interval extension of TOPSIS method which is free of heuristic assumptions and limitations of known methods. Using numerical examples we show that “direct interval extension of TOPSIS method” may provide the final ranking of alternatives which is substantially different from the results obtained using known methods. [Copyright &y& Elsevier]

• Estimate the relevance of contextual information for music recommendation. • Model users' context-sensitive preference for music. • Recommendation quality is improved by treating criteria preferences as contexts. • Experimental results show the advantages of the proposed method. • The method outperforms both traditional baselines and context-aware models. Context-aware recommender systems have received considerable attention from industry and academic areas. In this paper, we pay heed to the growing interest in integrating context-awareness and multi-criteria decision making in recommender systems, to deal with the most pressing challenges in music recommender systems, namely the diversity of the recommended playlist, the scalability of the system, and the cold start problem. This paper introduces a new multi-criteria recommendation approach, named MORec, which generates Top-N music recommendations by bootstrapping the system using beforehand collected data. We usher by gauging the relevance of contextual information from the relation between three elements: user, music genre, and the user's context. Then, we apply an aggregation technique to uncover the relationship between the context and the overall rating. Besides, we apply the K-means algorithm to generate a predictive model that comprises clusters of similar contexts defining the association between contextual dimensions and music genres. Carried out experiments emphasize very promising results of our approach in terms of clustering quality, compared to the Partitioning Around Medoids algorithm in terms of connectivity and stability. The comparison versus pioneering recommendation baselines underscored the effectiveness of MORec in terms of recommendation quality and usefulness. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main developments on sorting approaches. • The categorization of multiple-criteria decision-making sorting methods. • Applications of the multiple-criteria decision-making sorting methods. • Trends and future directions of MCDM Sorting. Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods' trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research. [ABSTRACT FROM AUTHOR]

Abstract: Interactive multiobjective optimization (IMO) is a subfield of multiple criteria decision making. In multiobjective optimization, the optimization problem is formulated with a mathematical model containing several conflicting objectives and constraints depending on decision variables. By using IMO methods, a decision maker progressively provides preference information in order to find the most satisfactory compromise between the conflicting objectives. In this paper, we consider implementation challenges of IMO methods. In particular, we consider what kind of interaction techniques can support the decision making process and information exchange between IMO methods and the decision maker. The implementation of an IMO method called Pareto Navigator is used as an example to demonstrate concrete challenges of interaction design. This paper focuses on describing the incremental development of the user interface for Pareto Navigator including empirical validation by user testing evaluation. [Copyright &y& Elsevier]

Abstract: In the present paper, a novel fuzzy Multiple Attribute Decision Making (MADM) model is proposed for modeling and solving truck selection problem of a land transportation company. As truck selection is a very crucial problem for the land transportation companies, a systematic and scientific approach is necessary for its solution. A systematic methodology is proposed in the present study by integrating “fuzzy DEMATEL” and “fuzzy hierarchical TOPSIS”. The proposed method makes use of fuzzy DEMATEL method for evaluating the weights of the criteria and hierarchical fuzzy TOPSIS method for assessing the alternatives according to criteria. The steps of the method are described first then a case study is presented in the paper. [Copyright &y& Elsevier]

The problem of rank aggregation, also known as group-ranking, arises in many fields such as metasearch engines, information retrieval, recommendation systems and multicriteria decision-making. Given a set of alternatives, the problem is to order the alternatives based on ordinal rankings provided by a group of individual experts. The available information is often limited and uncertain in real-world applications. This paper addresses the general group-ranking problem using interval ordinal data as a flexible way to capture uncertain and incomplete information. We propose a two-stage approach. The first stage learns an aggregate preference matrix as a means of gathering group preferences from uncertain and possibly conflicting information. In the second stage, priority vectors are derived from the aggregate preference matrix based on properties of fuzzy preference relations and graph theory. Our approach provides a theoretical framework for studying the problem that extends some of the methods in the literature, efficient computational methods to solve the problem and some performance measures. It relaxes data certainty and completeness assumptions and overcomes some shortcomings of current group-ranking methods. [ABSTRACT FROM AUTHOR]

Nowadays, passengers in urban public transport systems do not only seek a short-time travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalization of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighborhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GA–VNS represents the best approach in terms of computational time and solutions quality for a real world routing system. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for computing the Pareto frontier in Multi-Objective Markov Chains Problems (MOMCPs) that integrates a regularized penalty method for poly-linear functions. In addition, we present a method that make the Pareto frontier more useful as decision support system: it selects the ideal multi-objective option given certain bounds. We restrict our problem to a class of finite, ergodic and controllable Markov chains. The regularized penalty approach is based on the Tikhonov’s regularization method and it employs a projection-gradient approach to find the strong Pareto policies along the Pareto frontier. Different from previous regularized methods, where the regularizator parameter needs to be large enough and modify (some times significantly) the initial functional, our approach balanced the value of the functional using a penalization term ( μ ) and the regularizator parameter ( δ ) at the same time improving the computation of the strong Pareto policies. The idea is to optimize the parameters μ and δ such that the functional conserves the original shape. We set the initial value and then decrease it until each policy approximate to the strong Pareto policy. In this sense, we define exactly how the parameters μ and δ tend to zero and we prove the convergence of the gradient regularized penalty algorithm. On the other hand, our policy-gradient multi-objective algorithms exploit a gradient-based approach so that the corresponding image in the objective space gets a Pareto frontier of just strong Pareto policies. We experimentally validate the method presenting a numerical example of a real alternative solution of the vehicle routing planning problem to increase security in transportation of cash and valuables. The decision-making process explored in this work correspond to the most frequent computational intelligent models applied in practice within the Artificial Intelligence research area. [ABSTRACT FROM AUTHOR]

Blockchain technologies, also known as Distributed Ledger Technologies (DLT), are increasingly being explored in many applications, especially in the presence of (potential) dis-/mis-/un-trust among organizations and individuals. Today, there exists a plethora of DLT platforms on the market, which makes it challenging for system designers to decide what platform they should adopt and implement. Although a few DLT comparison frameworks have been proposed in the literature, they often fail in covering all performance and functional aspects, adding that they too rarely build upon standardized criteria and recommendations. Given this state of affairs, the present paper considers a recent and exhaustive set of assessment criteria recommended by the ITU (International Telecommunication Union). Those criteria (about fifty) are nonetheless mostly defined in a textual form, which may pose interpretation problems during the implementation process. To avoid this, a systematic literature review regarding each ITU criterion is conducted with a twofold objective: (i) to understand to what extent a given criterion is considered/evaluated by the literature; (ii) to come up with 'formal' metric definition (i.e., on a mathematical or experimental ground) based, whenever possible, on the current literature. Following this formalization stage, a decision support tool called CREDO-DLT, which stands for "multiCRiteria-basEd ranking Of Distributed Ledger Technology platforms" , is developed using AHP and TOPSIS, which is publicly made available to help decision-maker to select the most suitable DLT platform alternative (i.e., that best suits their needs and requirements). A use case scenario in the context of energy communities is proposed to show the practicality of CREDO-DLT. • Blockchain (DLT) standardization initiatives are reviewed. • To what extent ITU's DLT assessment criteria are covered in literature is studied. • A mathematical formalizations of the ITU recommendations are proposed. • A decision support tool (CREDO-DLT) is designed for DLT platform selection. • An energy community use case is developed to show the practicality of CREDO-DLT. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops models for selecting portfolios for conventional and socially responsible investment (SRI) mutual funds according to the preferences of the SRI investor. This involves constructing an investment portfolio that takes into account both financial and social, environmental and ethical (SEE) criteria. The optimal portfolio selection problem is solved when the expected returns of the assets as well as the periodic returns are not precisely known. Instead, incomplete information on the parameters of the model is modeled by fuzzy numbers, which include the ‘true’ values and are consistent with the Decision Maker’s beliefs on assets’ performance. In this paper, the financial criteria taken into account are the expected return and the difference between the returns of the portfolio and a pre-specified benchmark index i.e. a strategy of tracking error (TE) is followed. Moreover, we assume that the investor’s preferences about SEE features of the portfolio are imprecisely known. In order to model these flexible preferences we propose to use fuzzy decision making. The multidimensional nature of the problem leads us to work with techniques of multiple criteria decision making (MCDM), namely goal programming (GP), and the incomplete information is handled by a fuzzy robust approach. The proposed fuzzy goal programming (FGP) model is applied to a database of UK mutual funds. [Copyright &y& Elsevier]

Design concept evaluation is a critical stage in the product development which has significant impact on the downstream process in product development thus on success of new product. Design concept evaluation is widely recognized as a complex multi-criteria decision-making (MCDM) problem involving various decision criteria and large amount of data which are usually imprecise and subjective. This paper proposes a new decision-making method to evaluate product design concepts based on the distance between interval vectors each alternative and positive and negative ideal reference vectors. Rank of design concepts is obtained by calculating interval-based relative closeness index for each alternative. In this method, to deal with uncertainty and vagueness of data in the primary phases of product design, performance of design concepts with respect to quantitative and qualitative criteria are concurrently evaluated using rough set and fuzzy set. The weights of criteria used in the evaluation are obtained using the extent analysis method on fuzzy AHP. The efficacy of the method is demonstrated with a numerical example and the results are compared to TOPSIS method. In final, the conclusions of our method are represented and some future directions are proposed to improve the model. [ABSTRACT FROM AUTHOR]

Today’s largest and fastest growing companies’ assets are no longer physical, but rather digital (software, algorithms...). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises’ daily maintenance data reporting (e.g., maintenance planners, plant managers...), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a maintenance reporting quality assessment (MRQA) dashboard that enables any company stakeholder to easily – and in real-time – assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a new method for solving multiple criteria decision making (MCDM) problems in fuzzy environment to overcome all the deficiencies observed in the existing methods. For this purpose a weighted geometric aggregation operator (WGAO) and a new score function based on interval valued intuitionistic fuzzy soft set of root type (IVIFSSRT) are defined and some interesting theoretical properties of these tools are established. It is shown that interval valued intuitionistic fuzzy set of root type is a generalization of interval valued intuitionistic fuzzy set. A new method for ranking the alternatives of the MCDM problem based on WGAO and the new score function is presented and an algorithm is developed for this purpose. The working of the algorithm is explained with an example and the efficiency and superiority of the tools and new method are established with the help of a critical comparison study. It is shown that the proposed method works efficiently in solving the MCDM problem in fuzzy environment. [ABSTRACT FROM AUTHOR]

Tender analysis processes are everyday processes in any company, but they become even more important when the financial resources are limited. Achieving optimal and transparent tender analysis processes requires hard work from everyone involved. This paper proposes a linguistic multi-criteria decision-making model which helps decision-makers in this task by automating it. To do that, the proposal requires diverse experts to assess different criteria and establish their corresponding weights. The processing of all these input data will determine a minimum value, called profile , to consider an alternative as valid. Additionally, assessments of a similar (probably external) process will be taken into account, becoming a reference valuation. Unlike other decision-making models, the use of this last value gives an idea of the goodness of the result depending on whether the solution obtained is close to the reference value. To the best of our knowledge, there is no expert or intelligent system specifically designed to fully meet the needs of tender analysis processes. For both input and output process stages carried out in our proposal, 2-tuple linguistic labels have been used. These linguistic labels were chosen to facilitate decision-making for the staff involved in the process, as well as for being the most suitable communication way used by human beings. To validate the model, we apply it to a case study in the ICT (Information and Communications Technology) sector. In addition, we include a literature review related to applications using the 2-tuple representation, as well as a comparison of our proposal with related methods, including the results obtained by these methods for the case study presented. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

In the last decades, a considerable number of studies have been conducted to find the optimal locations for renewable energy facilities. The reviewed literature demonstrates how the combination of spatial representation computer tools, such as geographic information systems (GIS), with multi-criteria decision making (MCDM) methodologies, has successfully solved the problem of identifying optimal locations. Furthermore, since the appearance of fuzzy logic, the combination approaches have extended to the field of fuzzy sets to consider the imprecision and vagueness that some criteria may involve. In this paper, we propose a comparative analysis among fuzzy versions of MCDM methodologies, including GIS technologies, for the optimal site selection of offshore wind power plants. With this aim, we combined a classical pair-wise comparison method (AHP) with two distance-based approaches (TOPSIS and VIKOR), applying GIS software and comparing the two most extended fuzzy membership functions: triangular and linear. As a case study, this optimal location problem was applied to offshore wind site selection in the Gulf of Maine (USA). Initially, 56 alternatives for potential locations were identified from 22,331 km 2 study area. After applying the AHP methodology, the weights of the criteria were obtained, turning out to be the wind speed and bathymetry the most important criteria. The results demonstrate the robustness of the fuzzy TOPSIS methodology against potential variations in the criteria weights, since the best alternatives (optimal locations) and almost 90% of the 25 top-ranked alternatives were matching. Likewise, the rankings of alternatives illustrate that the use of triangular or linear fuzzy membership functions does not cause significant differences after applying the fuzzy VIKOR methodology and ArcGIS software. In fact, the most appropriate alternative is the same for both cases, and there is only an exchange of positions among the top-ranked alternatives. The proposed solutions can be applied to other locations and both onshore and offshore installations. • A comparative analysis among fuzzy versions of MCDM methodologies. • GIS and MCDM fuzzy methodologies combined for optimal site wind locations. • Assessment of site alternatives through TOPSIS–VIKOR MCDM comparative analysis. • Case study based on the offshore wind site selection in the Gulf of Maine, USA. [ABSTRACT FROM AUTHOR]

• Considering ABSA at three levels simultaneously based on customer reviews. • Integrating ABSA and BWM in designing an end-to-end ranking system. • Ranking the quality of services, facilities and amenities based on real case study. • Supporting customers decision by addressing the issue of "choice overload" • Designing an end-to-end ranking method as a service for web users. Considering customer reviews is one of the challenges of real-world decision models. These reviews can be on different platforms and include a large amount of information and may also include incomprehensible and unrelated phrases. The main advantage of customer reviews is the realistic view that it provides a realistic view of the product or service. Therefore, converting unstructured and incoherent customer-based reviews into machine learning language and ultimately turning it into a decision model is very important. In this paper, we propose an end-to-end ranking method for integrating mechanisms such as text processing, sentiment analysis and the multi-criteria decision-making technique. The proposed ranking method relies on the integration of three methods, namely, the aspect-based sentiment analysis (ABSA) method, the Dawid-Skene algorithm and the Best Worst Method (BWM). In other words, the proposed work encompasses four major steps: i) crawling customer reviews, ii) preprocessing, iii) aspect term extraction, aspect category detection and polarity detection, and iv) designing a decision-making model. The main contribution of this study is to consider ABSA at three levels simultaneously and integrate ABSA and BWM in designing an end-to-end ranking method for ranking the quality of hotel services, facilities and amenities based on customer reviews. The ability of the proposed end-to-end ranking framework is evaluated using a real data set of user reviews of Sydney hotels. [ABSTRACT FROM AUTHOR]

This paper introduces and investigates ranked hesitant fuzzy sets, a novel extension of hesitant fuzzy sets that is less demanding than both probabilistic and proportional hesitant fuzzy sets. This new extension incorporates hierarchical knowledge about the various evaluations submitted for each alternative. These evaluations are ranked (for example by their plausibility, acceptability, or credibility), but their position does not necessarily derive from supplementary numerical information (as in probabilistic and proportional hesitant fuzzy sets). In particular, strictly ranked hesitant fuzzy sets arise when no ties exist, i.e., when for any fixed alternative, each submitted evaluation is either strictly more plausible or strictly less plausible than any other submitted evaluation. A detailed comparison with similar models from the literature is performed. Then in order to produce a natural strategy for multi-criteria multi-agent decisions with ranked hesitant fuzzy sets, canonical representations, scores and aggregation operators are designed in the framework of ranked hesitant fuzzy sets. In order to help implementation of this model, Mathematica code is provided for the computation of both scores and aggregators. The decision-making technique that is prescribed is tested with a comparative analysis with four methodologies based on probabilistic hesitant fuzzy information. A conclusion of this numerical exercise is that this methodology is reliable, applicable and robust. All these evidences show that ranked hesitant fuzzy sets are an intuitive extension of the hesitant fuzzy set model designed by V. Torra, that can be implemented in practice with the aid of computationally assisted algorithms. • We introduce a new model called ranked hesitant fuzzy sets. • We develop scores in this framework. • An algorithm produces information fusion of ranked information under hesitancy. • Mathematica codes facilitate reproducibility of these computations. • We apply these elements to multi-criteria multi-agent decisions. [ABSTRACT FROM AUTHOR]

• A new Multi-Criteria Decision-Making Method, ABAC, is proposed. • ABAC is simple, more scalable and free from rank reversal problem. • ABAC is illustrated and validated using cloud service selection. • Various case studies using ABAC in different domains are provided. 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. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%–95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ − ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers. [ABSTRACT FROM AUTHOR]

• Performance measurement complexities during relief operations are key motivations. • Strategies aimed to improve the performance measurement in HSC were proposed. • Proposed strategies were modelled using the grey-DEMATEL and m-TISM approach. • The proposed framework may help managers improve performance measurement in HSC. Performance measurement activities (PMA) in the humanitarian supply chain (HSC) face several issues. Many of these issues are avoidable and inter-related, subsequently producing undesirable cascading effects. HSC Stakeholders are actively looking for means to reduce the complexities associated with PMA and improve its efficiency. In this paper, we identify and model the eleven strategies that can improve both PMA and the performance measurement system (PMS). Strategies were identified using detailed literature reviews and discussions with the experts and were modelled using the grey DEMATEL and m-TISM methodology. The results indicate that the most effective strategies are fundraising for PMA, Digital supply chain adoption, Top management support, Public awareness activities and Relief worker training. Four strategies can be grouped into the cause side and seven strategies on the effect side. The key recommendation is to rapidly adopt digital supply chain applications in the HSC. HSC stakeholders also need to pay increased attentions towards fundraising activities targeting PMS. This study advances the understanding of PMS in the context of HSC. The decision-maker can adopt the study's findings to reduce the complexities that occurred during PMA. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new decision making method for selection problems under uncertainty is proposed. • The method considers both the objective weights and the subjective preferences. • The method is applied to the selection of renewable energy technologies. • The best option varies depending on the coefficient factor of the subjective weight. • The method is applicable to other selection problems in different areas. The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives' scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations. [ABSTRACT FROM AUTHOR]

Purulia district is a drought-prone area in West Bengal, India. Naturally, the farmers of the Purulia district could not plant the crops due to the absence of water resources. Water resource management (WRM) has the potential mechanism to address the available water resources. This paper developed a multi-criterion water resource management technique for defining the available water resources in the agriculture field. We have introduced a novel ranking method of generalized single-valued non-linear bipolar neutrosophic numbers based on possibility measures. Additionally, we have first defined the possibilistic mean of generalized single-valued non-linear bipolar neutrosophic number. Using the possibilistic mean, we have invented the positive and negative rank expositors ( S μ + , S μ − ) for authenticity, hesitate, and falsity membership function. In the present situation, water resource management in agriculture has become a big problem worldwide. Multi-criteria decision-making technique is necessary for developing this situation. Using the proposed multi-criteria decision-making method, we have solved a real water resource management problem in the Purulia district under GSVnTbN-environment. In this decision-making problem, we have considered the GSVnTbN-environment. Because during the summertime, water demand is very high, and at the end of summer, when monsoon season comes, water demand is deficient in the agriculture field of Purulia district. Sometimes the monsoon season, rainwater does not occur entirely. At that time, monsoon crop planting was tough in the agriculture field. So, this district's water scarcity nature is a non-linear and uncertain type of nature. Therefore, we proposed an MCDM method for water resource management problems under GSVnTbN-environment. • MCDM technique of water resource management (WRM) problem. • Possibilistic mean of GSVnbN number. • Positive and negative rank expositor ( S μ + , S μ − ) of GSVnbN number. • WRM problem of agriculture field in the Purulia district. • WRM problem solved under GSVnbN environment. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, because of the high potential for insurability in Turkey and the rapid improvement of insurance sector, foreign investors are interested in Turkish insurance market. Since insurability rate has reached saturation point in their countries, foreign investors tended to make investments in Turkey and start to purchase local insurance companies in Turkey. In this paper we analyze five Turkish insurance companies for a foreign investor who wants to purchase a local insurance company. For selecting the most appropriate alternative we used the extended VIKOR method which applied to determine the best feasible solution according to the selected criteria. This method was developed to solve multiple criteria decision making problems with conflicting and non-commensurable criteria, assuming that compromising is acceptable for conflict resolution. In this paper, the alternatives are evaluated according to all established criteria with the VIKOR method completely under fuzzy environment with fuzzy sets. [Copyright &y& Elsevier]

Abstract: In this paper we present a new approach to solve multi-attribute decision making problems in intuitionistic fuzzy environment. This approach is based on a new ranking method of intuitionistic fuzzy sets, in which the evaluated values (in the form of intervals) of the same alternative with different attributes are considered as one unified entity. According to people’s intuition, the ranking method proposed in this paper is mainly grounded on a revised score function and a revised accuracy function of intuitionistic fuzzy sets. Different from the traditional methods, in this new approach, the degree of membership, the degree of nonmembership and the degree of hesitation are considered with various importance in reflecting the true image of the respective alternative. Furthermore, an optimization model is established to estimate the relative degree of importance of each quantity. Finally, two practical examples are provided to illustrate our approach. [Copyright &y& Elsevier]

Abstract: Micropayment is common in retail outlets and public transport systems. This paper considers the situation where smart-card-based e-micropayment scheme starts off as single-purpose scheme and is progressing to be a multi-purpose scheme. User deciding whether to use such a scheme is a multiple criteria decision-making (MCDM) problem and depends on factors such as transaction cost, availability of discount, ease of use, convenience, reliability, and participation by transport-based companies, retail outlets, large organizations, as well as whether others are using it. Through a case study of successful smart-card-based single-purpose e-micropayment schemes in Taiwan and from the user’s perspective, the dynamic picture of the progression is obtained to analyze how different factors interact and collectively contribute to the successful transformation and diffusion of the scheme. Since this is a MCDM problem, Decision Making Trial and Evaluation Laboratory, a MCDM method, is used. Hence, this paper contributes to extending practical applications of DEMATEL to the field of electronic payment system. It is found that the dynamic picture of the progression of single-purpose smart-card-based e-micropayment scheme to multi-purpose scheme is indeed complex. The factors are definitely not independent of one another and they interact with one another. Ease of use, discount and participation by retail outlets are net causer factors. Top-up locations, reliability of the scheme, participation by transport companies and large organizers are net receiver factors. Factors related to participants of the scheme (such as user base, retail-outlet and transport companies) also exhibit the presence of network effect. The scheme operator faces a chicken and egg situation but the operator has to start somewhere to kick-start the process, end the waiting game, and hopefully to achieve the bandwagon effect. A possible starting point is to focus a lot of the energy on getting more retail outlets onboard. To further accelerate the progression to multi-purpose scheme, a multi-pronged approach to manage the transformation is also advised. To attract more users and to push them to use the scheme to make payment at the participating retail outlets, discount can be offered. [Copyright &y& Elsevier]

Abstract: This paper develops an evaluation approach based on the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). When the input for a decision process is linguistic, it can be understood that the output should also be linguistic. For that reason, in this paper we propose a modification of the TOPSIS algorithm which develops the above idea and which can also be used as a linguistic classifier. In this new development, modifications to the classic algorithm have been considered which enable linguistic outputs and which can be checked through the inclusion of an applied example to demonstrate the goodness of the new model proposed. [Copyright &y& Elsevier]

Abstract: In this paper the authors review the developments of the analytic hierarchy process (AHP) since its inception. The focus of this paper is a neutral review on the methodological developments rather than reporting its applications that have appeared since its introduction. In particular, we discuss problem modelling, pair-wise comparisons, judgement scales, derivation methods, consistency indices, incomplete matrix, synthesis of the weights, sensitivity analysis and group decisions. All have been important areas of research in AHP. [Copyright &y& Elsevier]

Abstract: Supply planning of two-level assembly system is considered in this paper in which lead times are stochastic at both levels. Finding optimal order release dates of components is our aim which optimizes problem’s performance metrics. There is a determined due date that if demands are not prepared for delivery in that day, backlogging cost would happen. The other noticeable cost that should be taken in to consideration is holding cost, which may occur if components arrive before their time of assembling. In contrast to most of studies in this field that consider sum of these cost, this paper attempts to make a tradeoff between these objectives’ affects using multi-objective approach. This approach hybridizes an evolutionary optimizer, genetic algorithm, with an electromagnetism-like mechanism (EM). The algorithm’s effectiveness is compared against an adapted non-dominated sorting genetic algorithm II (NSGA-II) which has been presented for this problem. [Copyright &y& Elsevier]

Abstract: Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts’ subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique. [Copyright &y& Elsevier]

Abstract: In a recent paper by Toloo et al. [Toloo, M., Sohrabi, B., & Nalchigar, S. (2009). A new method for ranking discovered rules from data mining by DEA. Expert Systems with Applications, 36, 8503–8508], they proposed a new integrated data envelopment analysis model to find most efficient association rule in data mining. Then, utilizing this model, an algorithm is developed for ranking association rules by considering multiple criteria. In this paper, we show that their model only selects one efficient association rule by chance and is totally depended on the solution method or software is used for solving the problem. In addition, it is shown that their proposed algorithm can only rank efficient rules randomly and will fail to rank inefficient DMUs. We also refer to some other drawbacks in that paper and propose another approach to set up a full ranking of the association rules. A numerical example illustrates some contents of the paper. [Copyright &y& Elsevier]

Abstract: Since the second half of 2007, the global economy has felt the effects of the credit crunch, and the sub-prime mortgage crisis in the US has resulted in a loss of consumer confidence. Small and medium enterprises (SMEs) have been the foundation for economic development and have provided important contributions to job creation. Economic policy makers worldwide are trying to implement actions that will enhance the competitive vitality of SMEs and foster entrepreneurial drive through effective entrepreneurship policy. Surprisingly, prior research has given little attention to the evaluation of entrepreneurship policy. The paper proposes an integrated model that combines the Decision Making Trial and Evaluation Laboratory (DEMATEL), analytic network process (ANP), and zero-one goal programming (ZOGP) methods. This model considers the interdependent relationship among complex policy evaluation criteria and alternatives, and addresses the constraints of itemized annual budgets. Then, the paper applies the model to an empirical case study of entrepreneurship policy for SMEs in Taiwan. The evaluation results show that three alternatives (incubator center, financial assistance, and knowledge-sharing platforms) are selected under budget constraints and the model provides an effective solution to help policy makers evaluate and select feasible entrepreneurship policy mix. [Copyright &y& Elsevier]

Abstract: This paper is aimed to present a fuzzy decision making approach to deal with the performance measurement in supply chain systems. In the manufacturing environment, performance measurement is based on different quantitative and qualitative factors. Some of these factors may have a larger effect on the performance measure than others. Units of measure of the quantitative factors are different such as time, money, percentage, ratio, and counts. Thus, this paper presents a performance measurement approach based on fuzzy set theory and the pair-wise comparison of Analytical Hierarchy Process (AHP), which ensures the consistency of the designer’s assignments of importance of one factor over another to find the weight of each of the manufacturing activity in the departmental organization. In the proposed model, various input factors have been selected, and treated as a linear membership function of fuzzy type. It is tested on a numerical example. The approach provides an effective decision tool for the performance measurement of a supply chain in manufacturing environment. [Copyright &y& Elsevier]

Abstract: The selection of a location for an international distribution center (IDC) is a most important decision for international logistics managers owing to the need to consider various criteria that involve a complex decision process in which multiple requirements and uncertain conditions have to be taken into consideration simultaneously. Moreover, the criteria often exist simultaneously as independent and dependent characteristics when the problems of location selection have become very complex. A new hybrid method combining the concepts of fuzzy DEMATEL and a new method of fuzzy multiple criteria decision-making (MCDM) in a fuzzy environment is proposed to solve the problems of IDC location selection. In this paper, the fuzzy DEMATEL is proposed to arrange a suitable structure between criteria, and the analytic hierarchy/network process (AHP/ANP) is used to construct weights of all criteria. The linguistic terms characterized by triangular fuzzy numbers are used to denote the evaluation values of all alternatives versus various criteria. Finally, the aggregation fuzzy assessments of different alternatives are ranked to determine the best selection. Furthermore, this paper uses an empirical case for optimal location selection for an IDC in Pacific Asia to illustrate the proposed method, and the results show that the method is an effective means for tackling fuzzy MCDM problems. [Copyright &y& Elsevier]

Abstract: This paper focuses on the product diffusion in a competitive automobile market. Since purchasing a car is costly, the consumers in the market tend to behave like rational decision makers. They naturally compare the attributes of cars (e.g., brand preference, fuel economy, safety, comfort) and make overall decisions. In this paper, we propose an agent-based (AB) diffusion model consisting of tens of thousands of interacting agents. In the model, an agent represents a consumer and bases its multi-attribute decision-making on fuzzy TOPSIS. The decision-making process integrates three purchasing forces: expert’s product information provided by mass media, subjective weights on product attributes assigned by individual consumers, and social influence (i.e., information delivered from a consumer’s neighbors who have already adopted products). The AB model executes the agents and observes the collective behavior. In this sense, the model can assist in the analysis of the complex market dynamics. We conducted an empirical study to verify the performance of the AB model. [Copyright &y& Elsevier]

Abstract: In international trade offset practices (in Taiwan known as industrial cooperation program, ICP) have received increased attention over the past 20 years. In the coming 10 years, the Taiwanese government may expend roughly US$16 billion for purchasing Patriot-III missiles, P-3 long-range anti-submarine planes, and diesel-engine submarines from the United States through foreign military sale, and can achieve US$8 billion ICP credit the largest in Taiwanese history. Offsets or ICP can be regarded as fuzzy multiple criteria decision-making (MCDM) problems, therefore, the fuzziness and uncertainty of subjective perception should be considered. This paper provides an alternative approach, the non-additive fuzzy integral, to deal with the fuzzy MCDM problems especially when there is dependence among considered criteria. The main purpose of this paper is to discuss with Taiwan’s ICP Optimal Offset Transaction Policy and propose a framework of drawing on ICP credit in future. This paper considers the four aspects of policy, ability, economy, and environment, to establish a set of fuzzy AHP multiple criteria decision mode to identify the evaluative criteria variables and project item’s order for ICP project. This decision mode was identified as a workable method. [ABSTRACT FROM AUTHOR]

Abstract: Data Envelopment Analysis (DEA) is a managerial powerful tool to evaluate the relative efficiency of each decision making unit (DMU). Nowadays, multi-objective DEA models in static environment are an attractive technique for evaluation quantity and quality aspects of performance analysis because there is some weakness in single objective DEA such as one-dimensional performance analysis and also it is important to consider the decision maker(s) preference over the potential adjustments of various inputs and outputs when DEA is employed. In this paper, a fuzzy dynamic multi-objective DEA model is presented in which data are changing sequentially. This paper assesses the performance of the railways using presented model as a numerical example to evaluate the results of the model. Results indicate that the multiple objective program model improves discriminating power of classical DEA models with just one time calculation of the efficiency achievement for all DUMs. [Copyright &y& Elsevier]