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Abstract: Multi objective processing can be leveraged for mining the association rules. This paper discusses the application of multi objective genetic algorithm to association rule mining. We focus our attention especially on association rule mining. This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account. In order to improve algorithm efficiency, we apply the FP-tree algorithm. Our method extracts the best rules that have best correlation between support and confidence. The operators of our method are flexible for changing the fitness. Unlike the Apriori-based algorithm, it does not depend on support. Experimental study shows that our technique outperforms the traditional methods. [Copyright &y& Elsevier]

Abstract: Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool. [ABSTRACT FROM AUTHOR]

Abstract: Multi-objective (MO) optimization is a challenging research topic because it involves the simultaneous optimization of several complex and conflicting objectives that requires researchers to address many issues which are unique to MO problems. However multi-objectivity is only one aspect of real-world applications and there is a growing interest in the optimization of solutions that are insensitive to parametric variations as well. In order to evaluate the capability of MO evolutionary algorithms (MOEAs) to find robust solutions, it is important to employ suitable test functions. In this paper, empirical studies are conducted to examine the suitability of existing robust test functions. Results suggest that these test functions have a bias towards the region where the robust solutions lie, rendering it difficult to assess the true capability of MOEAs. Motivated by such a finding, we present a framework for the construction of robust continuous MO test functions characterized by different noise-induced features. These noise-induced features can pose different difficulties to the optimization algorithms. A fitness-inheritance scheme is also presented and incorporated into two well-known MOEAs. Empirical analysis of the proposed robust MO test functions reveals that some noise-induced features present greater challenges to robust MOEAs as compared to existing robust test functions. In addition, the vehicle routing problem with stochastic demand (VRPSD) is presented as a practical example of robust combinatorial MO optimization problems. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization. [Copyright &y& Elsevier]

Abstract: Fuzziness is inherent in decision data and decision making process. In this paper, interval-valued intuitionistic fuzzy (IVIF) sets are used to capture fuzziness in multiattribute decision making (MADM) problems. The purpose of this paper is to develop a methodology for solving MADM problems with both ratings of alternatives on attributes and weights being expressed with IVIF sets. In this methodology, a weighted absolute distance between IF sets is defined using weights of IF sets. Based on the concept of the relative closeness coefficients, we construct a pair of nonlinear fractional programming models which can be transformed into two simpler auxiliary linear programming models being used to calculate the relative closeness coefficient intervals of alternatives to the IVIF positive ideal solution, which can be employed to generate ranking order of alternatives based on the concept of likelihood of interval numbers. The proposed method is illustrated with a real example. [Copyright &y& Elsevier]

Abstract: Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study. [Copyright &y& Elsevier]

Abstract: The Nickel reduction process is a complex task where many dynamic optimization problems arises that, nowadays, requires a human operator to take decisions based on his experience and intuition. In order to help the operator to optimize the reduction process in terms of maximum amount of mineral extracted and minimum energy consumption, a control system integrated by several modules is being designed. One of the modules has the task of predicting how much petroleum will be burned in the ovens where the raw material is processed. This paper proposes an algorithm to design Radial Basis Function Neural Networks that will be able to predict accurately the amount of petroleum given a set of input parameters. The algorithm is also able of identifying the most relevant input parameters for the network so the dimensionality reduction problem is ameliorated. Hence, this paper, as it will be shown in the experiments section is able to apply the synergy of different Soft Computing techniques to the industrial process obtaining satisfactory results. [Copyright &y& Elsevier]

Abstract: The location of a warehouse is generally one of the most important and strategic decision in the optimization of logistic systems. Warehouse location is a long-term decision and is influenced by many quantitative and qualitative factors. Among the main criteria taken into account in this paper, some are costs, labor characteristics, infrastructure, and markets. This paper also includes some sub-criteria because of the hierarchical structure of the problem, like tax incentives and tax structures, availability of labor force, quality and reliability of modes of transportation, and proximity to customers. The conventional approaches to warehouse location selection problem tend to be less effective in dealing with the imprecise or vague nature of the linguistic assessment. Under many situations, the values of the qualitative criteria are often imprecisely defined for the decision-makers. Choquet integral is a suitable multi-criteria method to capture this imprecise or vague nature. This paper shows a successful application of multi-criteria Choquet integral to a real warehouse location selection problem of a big Turkish logistic firm. [Copyright &y& Elsevier]

Abstract: This paper presents a method for designing autonomous classifiers via multi-objective genetic algorithms. The paper also proposes a novel objective measure to quantify the understandability of the classifiers. The other objectives of the classifiers are classification accuracy and average support value. We experimentally evaluate our approach on five different medical dataset and demonstrate that our algorithm encourages us to improve and apply this strategy in many real-world applications. [Copyright &y& Elsevier]

Abstract: This paper focuses on the application of the analytical hierarchy process (AHP) technique in the context of production and distribution of anti cancer drugs within the pharmacy department of a French hospital. This is achieved by evaluating how AHP can support the drug production planning process which aims at minimizing the cost associated with the drug preparation process while satisfying patients. The approach proposed has been applied to a case study which is the pharmaceutical chemotherapy compounding unit of the Georges Pompidou European Hospital (HEGP, AP-HP, Paris). Quantitative weightings from the AHP model are used to identify drugs that the pharmacy can produce in advance, i.e. on a MTS (make to stock) basis. Because of its ease of implementation and results it enabled to reach, the approach developed at Georges Pompidou has been extended to other pharmacies in France. More generally, the work carried out in this paper is an example of illustration of how AHP can be used (with its strengths and weaknesses) as a decision-support tool in health care management. [Copyright &y& Elsevier]

Abstract: This paper developed an optimized mathematical model to address the modification of spare parts. In addition, it can evaluate the execution of modified product plans; it also evaluates the suppliers and distributes spare parts supplied by the suppliers. In this mathematical model, analytic hierarchy process (AHP) was proposed for the formulation of factor weights. In addition, an improved particle swarm optimization (PSO) algorithm was also developed for solving the mathematical model; with the introduction of memory and inhibition mechanisms, it could eliminate worse results and excessive searches. Finally, this paper combines the improved mechanisms and four PSO speed-updating criterions. The case study shows that, this improved PSO has good solving capabilities. [Copyright &y& Elsevier]

Abstract: Design concept is an important wealth-creating activity in companies and infrastructure. However, the process of designing is very complex. Besides, the information required during the conceptual stage is incomplete, imprecise, and fuzzy. Hence, fuzzy set theory should be used to handle linguistic problem at this stage. This paper presents a fuzzy integrated approach to assess the performance of design concepts. And those criteria rating, relative weights and performance levels are captured by fuzzy numbers, and the overall performance of each alternative is calculated through an enhanced fuzzy weighted average (FWA) approach. A practical numerical example is provided to demonstrate the usefulness of this study. In addition, this paper, in order to make computing and ranking results easier to increase the recruiting productivity, develops a computer-based decision support system to help make decisions more efficiently. [Copyright &y& Elsevier]

Abstract: In this paper, we present a hybrid multi-criteria decision-making (MCDM) model to evaluate the competence of the firms. According to the competence-based theory reveals that firm competencies are recognized from exclusive and unique capabilities that each firm enjoy in marketplace and are tightly intertwined within different business functions throughout the company. Therefore, competence in the firm is a composite of various attributes. Among them many intangible and tangible attributes are difficult to measure. In order to overcome the issue, we invite fuzzy set theory into the measurement of performance. In this paper first we calculate the weight of each criterion through adaptive analytic hierarchy process (AHP) approach (A 3) method, and then we appraise the performance of firms via linguistic variables which are expressed as trapezoidal fuzzy numbers. In the next step we transform these fuzzy numbers into interval data by means of α-cut. Then considering different values for α we rank the firms through TOPSIS method with interval data. Since there are different ranks for different α values, we apply linear assignment method to obtain final rank for alternatives. [Copyright &y& Elsevier]

The problem of electroplating selection has been of concern to users for many years. The selection process is becoming more and more difficult due to the availability of large variety of electroplating configurations and manufacturing plants/products. The methods proposed so far consider only physical parameters (thickness, hardness, adhesion, etc.). In order to have precise information about the selection process, the performance of electroplating plays a vital role. The objective of this paper is to propose a methodology by which selection of electroplating product/plant can be made easy. This selection procedure will help the user to select the system most suited for his operational needs. Moreover, the paper discusses how the electroplating suppliers, designers and maintenance personnel will also be benefited. The identification and codification of attributes based on n-digit alpha numeric code is presented here. The 3 stage selection procedure allows rapid convergence from a very large number of options to manageable shortlist of potentially suitable electroplating option using ¿elimination search¿ based on a few pertinent attributes. Then the selection procedure ranks them by employing a multiple attributes decision making (MADM) method using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. It helps the decision maker(s) to organize the problem to be solved, and carry out analysis, comparisons and ranking of the alternatives. Magnitudes of different attributes are used to generate parameters of the hypothetical ideal electroplating, with all the candidate electroplating compared and ranked. This ranking gives the best available electroplating for particular application. The methodology is presented with the help of illustrated example of gold plating, which shows that it can benefit the designers, users or manufacturers. [Copyright &y& Elsevier]

Abstract: This paper proposes an approach of measuring a technology university’s knowledge management (KM) performance from competitive perspective. The approach integrates analytical network process (ANP), which is a theory of multiple criteria decision-making and is good at dealing with tangible and intangible information, with balanced scorecard (BSC) that contains four perspectives, including customer perspective, internal business perspective, innovation and learning perspective, and financial perspective, being adopted as the indicators of KM performance measurement (KMPM). This paper makes three important contributions: (1) it propose a methodology of comparing an organization’s knowledge management performance with its major rivals to offer effective information for improving KM, increasing decision-making quality, and obtaining clear effort direction of attaining competitive advantage; (2) it explores the case involving a lot of findings that present the positions of the case organization against it major rivals and imply that the technology university has to reinforce knowledge creation and accumulation to catch up with its competitive rivals; and (3) it is generic in nature and applicable to benefit an organization. The results prove the proposed method can act as a measurement tool for the entire KM of an organization. [Copyright &y& Elsevier]

Abstract: This paper explores the use of the optimization procedures in SAS/OR software with application to the contemporary logistics distribution network design using an integrated multiple criteria decision making approach. Unlike the traditional optimization techniques, the proposed approach, combining analytic hierarchy process (AHP) and goal programming (GP), considers both quantitative and qualitative factors. In the integrated approach, AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, a GP model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. To facilitate the use of integrated multiple criteria decision making approach by SAS users, an ORMCDM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear programming models based on the selected GP model. An example is given to illustrate how one could use the code to design the logistics distribution network. [Copyright &y& Elsevier]

Abstract: This paper presents a new unsupervised hybrid classifier that combines several base classifiers through a fuzzy multicriteria decision making (MCDM) approach. The base classifiers are: fuzzy clustering, parametric and non-parametric Bayesian approaches, self-organizing feature maps and two versions of learning vector quantization. During the learning phase different partitions are established until a valid partition is found. The partitioning and validation are two automatic processes based on validation measurements. These measures allow computing the competences of each base classifier which are mapped as the weights to be used during the decision process through the MCDM. The design of the unsupervised classifier from supervised base classifiers and the automatic computation of the competences make the main contributions of this paper. Although the method is designed for six classifiers it can be extended for a greater number of classifiers. The method is applied for classifying textures in natural images. The analysis of the results shows that the performance of the proposed method is superior to other hybrid methods and the single usage of existing classification methods. [Copyright &y& Elsevier]

• The interval type 2 fuzzy Bonferroni mean Ordered Weighted Averaging is developed. • Seven Bonferroni mean quantifier Ordered Weighted Averaging models are formulated. • The optimal weights are solved from the mixed integer linear programming models. • The models are applied to various linguistic decision making aggregation schemes. In this paper, the new aggregation models for the interrelated multi-criteria decision making (MCDM) problems based on the quantifier guided ordered weighted averaging (QGOWA) and Bonferroni mean (BM) operator with interval type 2 fuzzy sets (IT2FS) are developed. In most MCDM problems, the decision criteria might not be totally independent. For some MCDM methodologies that are not considering the interrelationship of the criteria, the decision results suggested are meaningless. The BM operator can express the interrelationship of the input arguments, which serves as a mean type aggregator. Yager introduced the OWA operator which is associated with the orness level (attitudinal character) by means of the quantifiers. Different quantifier functions are associated with the respective different orness levels. This is referred to as the QGOWA operator. Besides, the real world MCDM problems are mostly under uncertain environments. To address such MCDM problems, the linguistic criteria weights and the alternative rates are better characterized by IT2FS. The major contributions of this paper are to propose the interrelation MCDM aggregation models with various extensions, to construct the mixed integer linear programming models for obtaining the optimal QGOWA BM IT2FS weights, and to formulate a new interrelation MCDM paradigm. The developed aggregation models are: (1). Ordinary MCDM aggregation; (2). BM with OWA weights; (3). BM with OWA weights and personal importance; (4). BM with QGOWA weights; (5). BM with QGOWA weights and personal importance; (6). BM with QGOWA weights and attitudinal characters; (7). BM with QGOWA weights, attitudinal characters and personal importance. A new MCDM aggregation methodology with application based on the developed models is introduced. The application results from the MCDM aggregation methodology demonstrate that the final decision prioritization are actually affected by the various orness levels predetermined by the decision experts. [ABSTRACT FROM AUTHOR]

• D numbers are introduced to deal with the vagueness in decision-making. • A novel MCDM model based on D numbers and linguistic fuzzy variables is proposed. • A hybrid BWM-MABAC-D multi-criteria based decision model is proposed. • D numbers methodology is very flexible to deal with the vagueness in HCW problem. • Multi-criteria techniques were compared based on D numbers and fuzzy approaches. Healthcare waste (HCW) management is a complex and challenging problem. It is one of the priorities in health. An increase in the number of the health services provided leads to an increase in the amount of HCW, which has particularly been noticeable in recent years. Since this is the waste that may pose a risk to humans and the environment, it is necessary to ensure an adequate treatment of the same. HCW management is particularly important in developing countries, due to inappropriate disposal methods, underfunding and a lack of the infrastructure. In order to achieve the cost-effectiveness and sustainability of this area, HCW should be minimized through an adequate treatment of the same. The Public Enterprise Zdravstvo Brčko (Brčko Health System) has intensively been addressing the HCW management issue. They have decided to upgrade the HCW system by purchasing a new infectious waste treatment facility. The paper is aimed at creating a new original integrated multicriteria decision-making model based on D numbers for processing fuzzy linguistic information. This model will serve to support management in the procurement of the mentioned facility. The model integrates the benefits of different approaches and theories. An initial model was formed, consisting of the six potential solutions evaluated based on the 18 criteria classified into the following four groups: social, environmental, economic and technological. Four experts in this field evaluated the criteria and potential solutions. Then, a new Best-Worst Method based on D numbers (BWM-D) was applied in order to determine the significance of the criteria. After that, a Multi-Attributive Border Approximation Area Comparison Based on D numbers (MABAC-D) was developed and applied so as to evaluate and select an infectious waste treatment facility. The results have shown that the alternative A1 gives the best results, whereas the alternative A5 shows the worst results. Finally, a sensitivity analysis was performed to validate the obtained results. In this part of the paper, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS-D) and VIšekriterijumska Optimizacija Kompromisnog Rešenja (VIKOR-D) were developed in order to validate the results. When procuring a new Contagious Waste Treatment System, the characteristics of the available devices need be perceived and all the criteria need be taken into account in order to provide a device which will solve the HCW problem in the best way. This paper has shown how D numbers can be used when making a selection of an HCW management device, and also how all the characteristics of such a device can be perceived and how the device demonstrating the best characteristics can be selected. [ABSTRACT FROM AUTHOR]

Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development. • A comprehensive literature review dedicated to selecting consensus protocols. • A new open-source data collection reflecting 18 state-of-the-art consensus protocols. • The first MCDM-based framework to identify preferable consensus protocols. • Demonstration on three main types of blockchains and bike renting application. [ABSTRACT FROM AUTHOR]

• An auxiliary algorithm is proposed to construct initial stage of the MCDM methods. • A multi-objective fuzzy optimization model is formulated. • An extensive comparative analysis is conducted in terms of the addressed methods. • The stability and validity of the results are tested and discussed. • Key findings and managerial insights are provided. This paper examines the performances of the multi-criteria decision-making (MCDM) methods and optimization model in solving multi-attribute shortest path problems such as the safest shortest path under a fuzzy environment. To the best of the knowledge of the authors, this is the first study performing comparative analysis on finding the multi-attribute shortest path by employing well-known techniques in terms of computational effort and results in a fuzzy environment. To this end, the safest shortest path problem, where the risk and distance values concerning arcs on a directed network are defined as triangular fuzzy numbers, is handled. The solution process is carried out under two main headings: (i) To start the solution with MCDM methods, an auxiliary algorithm that constructs a fuzzy decision matrix is proposed. Then, Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Simple Additive Weighting (F-SAW), and Fuzzy Evaluation Based on Distance from Average Solution (F-EDAS), that are fuzzy-based MCDM methods, are employed to rank the alternative paths. (ii) A multi-objective fuzzy optimization model is formulated, and the most reasonable paths are obtained considering different α-cut levels. Following that, comparative analysis is performed through a set of scenarios considering the different weights of the criteria to see the variability in the rankings. Besides, the addressed fuzzy-based MCDM methods are compared in terms of computational complexity. Overall, the main findings and managerial insights regarding the effectiveness and performance of the methods discussed in the solution process are provided. [ABSTRACT FROM AUTHOR]

The application of Data Envelopment Analysis (DEA) as a tool for efficiency evaluation has become widespread in public and private sector organizations. Since decision makers are often interested in a complete ranking of the evaluated units according to their performance, procedures that effectively discriminate the units are of key importance for designing intelligent decision support systems to measure and evaluate different alternatives for a better allocation of resources. This paper proposes a new method for ranking alternatives that uses common-weight DEA under a multiobjective optimization approach. The concept of distance to an ideal is thereby used as a means of selecting a set of weights that puts all the decision units in a favorable position in a simultaneous sense. Some numerical examples and a thorough computational experiment show that the approach followed here provides sound results for ranking alternatives and outperforms other known methods in discriminating the alternatives, therefore encouraging its use as a valuable decision tool for managers and policy makers. [ABSTRACT FROM AUTHOR]

Supply Chains are complex networks that demand for decision supporting tools that can help the involved decision making process. Following this need the present paper studies the supply chain design and planning problem and proposes an optimization model to support the associated decisions. The proposed model is a Mixed Integer Linear Multi-objective Programming model, which is solved through a Simulated Annealing based multi-objective meta-heuristics algorithm – MBSA. The proposed algorithm defines the location and capacities of the supply chain entities (factories, warehouses and distribution centers) chooses the technologies to be installed in each production facility and defines the inventory profiles and material flows during the planning time horizon. Profit maximization and environmental impacts minimization are considered. The algorithm, MBSA, explores the feasible solution space using a new Local Search strategy with a Multi-Start mechanism. The performance of the proposed methodology is compared with an exact approach supported by a Pareto Frontier and as main conclusions it can be stated that the proposed algorithm proves to be very efficient when solving this type of complex problems. Several Key Performance Indicators are developed to validate the algorithm robustiveness and, in addition, the proposed approach is validated through the solution of several instances. [ABSTRACT FROM AUTHOR]

In this paper, an imprecise Multi-Objective Genetic Algorithm (iMOGA) is developed to solve Constrained Multi-Objective Solid Travelling Salesman Problems (CMOSTSPs) in crisp, random, random-fuzzy, fuzzy-random and bi-random environments. In the proposed iMOGA, ‘3- and 5-level linguistic based age oriented selection’, ‘probabilistic selection’ and an ‘adaptive crossover’ are used along with a new generation dependent mutation. In each environment, some sensitivity studies due to different risk/discomfort factors and other system parameters are presented. To test the efficiency, combining same size single objective problems from standard TSPLIB, the results of such multi-objective problems are obtained by the proposed algorithm, simple MOGA (Roulette wheel selection, cyclic crossover and random mutation), NSGA-II, MOEA-D/ACO and compared. Moreover, a statistical analysis (Analysis of Variance) is carried out to show the supremacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Majority of the Multiple-Attribute Decision Making (MADM) methods assume that the criteria are independent of each other, which is not a realistic assumption in many real world problems. Several forms of interactions among criteria might occur in real life situations so that more sophisticated/intelligent techniques are required to deal with particular needs of the problem under consideration. Unfortunately, criteria interaction concept is very little issued in the literature. It is still a very important and critical research subject for intelligent decision making within MADM. The present paper aims to put a step forward to fill this gap by depicting the general picture, which provides a classification of methods related to criteria interaction phenomenon, and discuss/review the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) hybridizations first time in the literature. DEMATEL and ANP hybridizations grab remarkable attention of decision analysis community in recent years and seem as one of the most promising approaches to handle criteria interactions in a MADM setting. [ABSTRACT FROM AUTHOR]

Partnership is one of the strategies that could help companies increase their competiveness in a global market. Previous studies reported that a high percentage of partnerships fail to achieve their drivers of entering into partnership. The lack of a comprehensive partnership evaluation has been identified as one of the main reasons for partnership failure. In this paper, a multi-criteria decision support model is developed to evaluate the performance of an ongoing partnership in different periods based on the measures associated with the drivers for entering into the partnership. Interpretive Structural Modeling (ISM), Analytical Network Process (ANP) and Fuzzy Logic (FL) are used in order to address the interdependency, the importance of, and the uncertainty in performance measures, respectively. The outputs of the model are the importance of each performance measure and a single number for the overall partnership performance in each period, named as Partnership Performance Index (PPI) here. PPI is different from either mere financial or operational performance measures. PPI is a multi-dimensional measure which includes multiple performance measures associated with the partnership drivers and accounts for their importance and interdependencies. The model is applied to a partnership between a logging company and a sawmill in British Columbia, Canada. PPI is used to evaluate this partnership in three different periods. PPI values are compared to conventional measures for partnership evaluation and the managers confirmed that PPI values better represent the performance of their partnership. The sensitivity of the PPIs is investigated based on the changes in the importance as well as the value of the measures. The rankings from the model are compared to the ones estimated by the managers, and the results showed that the rankings are compatible. This model contributes to the literature by developing an index for partnership performance which captures partnership drivers and performance measures as well as their importance and interdependencies. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple criteria group decision making (MCGDM) problems in which decision makers (DMs)’ preferences on alternatives (criteria) are depicted by triangular fuzzy numbers and take the form of incomplete reciprocal comparison matrices. We aim to develop integrated methodologies for the MCGDM problems. First of all, we develop a triangular fuzzy power geometric (TFPG) operator and a triangular fuzzy weighted power geometric (TFWPG) operator for aggregating the DMs’ preferences into the group preferences. Furthermore, we construct a consistent recovery method and a δ -consistent recovery method for estimating the missing preferences. Next, we propose two integrated approaches to the aforementioned MCGDM problems by utilizing triangular fuzzy analytic hierarchy process (TFAHP) to combine the TFPG (TFWPG) operator, the recovery methods and extent analysis method (EAM) effectively. Finally, an illustrative example of small hydropower (SHP) investment projects selection is given to show our approaches. [ABSTRACT FROM AUTHOR]

While the auxiliary services required for the operation of power plants are not the main components of the plant, their energy consumption is often significant, and it can be reduced by implementing a series of improvement strategies. However, the cost of implementing these changes can be very high, and has to be evaluated. Indeed, a further economic analysis should be considered in order to maximize the profitability of the investment. In this paper, we propose a multiobjective optimization problem to determine the most suitable strategies to maximize the energy saving, to minimize the economic investment and to maximize the Internal Rate of Return of the investment. Solving this real-life multiobjective optimization problem with a decision maker presents several challenges and difficulties and we have developed a novel interactive procedure which combines three different approaches in order to make use of the main advantages of each method. The idea is to start with the approximation of the Pareto optimal set, in order to gain a global understanding of the trade-offs among the objectives, using evolutionary multiobjective optimization; next step is aiding the decision maker to explore the efficient set and to identify the subset of solutions which fits her/his preferences, for which interactive multiple criteria decision making methodologies are used; and finally we concentrate the search for new solutions into the most interesting part of the efficient set with the help of a preference-based evolutionary algorithm. This allows us to build a flexible scheme that is progressively adapted to the decision maker’s reactions until (s)he finds the most preferred solution. The interactive combined procedure proposed is applied in practice for solving the problem of the auxiliary services with a real decision maker, extracting interesting insights about the efficiency improvement of the auxiliary services. With this practical application, we show the usefulness of the interactive procedure proposed, and we highlight the importance of an understandable feedback and an adaptive process. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is a well-known approach for correlating the failure modes of a system to their effects, with the objective of assessing their criticality. The criticality of a failure mode is traditionally established by its risk priority number (RPN), which is the product of the scores assigned to the three risk factors, which are likeness of occurrence, the chance of being undetected and the severity of the effects. Taking a simple “unweighted” product has major shortcomings. One of them is to provide just a number, which does not sort failures modes into priority classes. Moreover, to make the decision more robust, the FMEA is better tackled by multiple decision-makers. Unfortunately, the literature lacks group decision support systems (GDSS) for sorting failures in the field of the FMEA. In this paper, a novel multi-criteria decision making (MCDM) method named FlowSort-GDSS is proposed to sort the failure modes into priority classes by involving multiple decision-makers. The essence of this method lies in the pair-wise comparison between the failure modes and the reference profiles established by the decision-makers on the risk factors. Finally a case study is presented to illustrate the advantages of this new robust method in sorting failures. [ABSTRACT FROM AUTHOR]

Selecting the most sustainable biomass crop type for biofuel production is a multi-criteria decision-making (MCDM) problem involving various conflicting criteria. In this paper, we propose a unique stochastic analytical hierarchy process (AHP) that can handle uncertain information and identify weights of criteria in the MCDM problem. By utilizing the beta distribution and approximating its median, we convert various types of expert evaluations including imprecise values into crisp values. We ensure consistency in each evaluation matrix before aggregating expert judgments. We then demonstrate use of the model by applying it to sustainable biomass crop selection. In order to define a comprehensive list of the selection criteria, we utilize the existing literature and opinions of experts including farmers, government specialists from the U.S. Department of Agriculture (USDA), and faculty members in the areas of biomass and bioenergy. The evaluation model includes three main sustainability criteria defined as economic, environmental, and social aspects associated with a total of 16 sub-criteria. We apply the proposed model to biomass alternatives including switchgrass, Miscanthus, sugarcane, corn, and wheat in Kansas. Results show the weights of economic, environmental, and social aspects to be 0.59, 0.26, and 0.15, respectively. The sensitivity analysis indicates that the score of switchgrass increases if environmental criteria are emphasized. On the other hand, wheat and corn become more favorable than other alternatives if priority is given to economic factors. The most sustainable biomass sources in different regions can be determined by applying the presented selection hierarchy. The proposed stochastic AHP methodology can also be utilized for other complex multi-criteria decision-making problems with uncertain information and multiple stakeholders. [ABSTRACT FROM AUTHOR]

The Artificial Neural Network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. Various approaches have been successfully applied as a learning algorithm for ANN training. This paper performs an experimental study that investigates the performance of different metaheuristics as learning algorithms to train the ANN for medical data classification tasks. The experiments are carried out on 15 well-known medical datasets. A comparative study is conducted with the classical Levenberg–Marquardt (LM) and other thirteen recent and relevant metaheuristics. Different evaluation criteria such as accuracy, sensitivity, specificity, precision, Geometric Mean, F-Measure, false-positive rate (FPR) are considered for performance estimation. The classification results are analyzed using Multi-Criteria Decision Making (MCDM) method, and the results with analysis establish that the Equilibrium Optimizer algorithm outperforms all the other algorithms included in the comparative study. • Artificial Neural Network training using metaheuristic algorithms. • An experimental study in medical data classification. • Performance analysis using multi-criteria decision making. • Equilibrium Optimizer shows superior performance over the competitive algorithms. [ABSTRACT FROM AUTHOR]

The social relationships between development agencies, non-governmental organizations, private companies, and other groups working on development projects play an important role in the overall success of projects. However, traditional project selection and prioritization processes ignore the organizational relationships. This paper proposes to integrate social network analysis into multi-criteria decision-making processes to enhance the effectiveness of project selection. A set of topological metrics of social network are used to quantitatively measure the organizational relationships and integrated into the analytic network process (ANP) to form a multi-criteria ANP project selection model. Utilizing empirical social network data of a water and food security research for development network in the Mekong River Basin, we investigate the effectiveness of the proposed model. The results show that it will offer companies, government agencies, and other donor organizations the opportunity to prioritize strategic network goals simultaneously with research and development priorities, and help companies and research organizations to increase their impact and reach within networks. [ABSTRACT FROM AUTHOR]

Qualitative and hesitant information is common in practical decision making process. In such complicated decision making problem, it is flexible for experts to use comparative linguistic expressions to express their opinions since the linguistic expressions are much closer than single or simple linguistic term to human way of thinking and cognition. The hesitant fuzzy linguistic term set (HFLTS) turns out to be a powerful tool in representing and eliciting the comparative linguistic expressions. In order to develop some approaches to decision making with hesitant fuzzy linguistic information, in this paper, we firstly introduce a family of novel distance and similarity measures for HFLTSs, such as the cosine distance and similarity measures, the weighted cosine distance and similarity measures, the order weighted cosine distance and similarity measures, and the continuous cosine distance and similarity measures. All these distance and similarity measures are proposed from the geometric point of view while the existing distance and similarity measures over HFLTSs are based on the different forms of algebra distance measures. Afterwards, based on the hesitant fuzzy linguistic cosine distance measures between hesitant fuzzy linguistic elements, the cosine-distance-based HFL-TOPSIS method and the cosine-distance-based HFL-VIKOR method are developed to dealing with hesitant fuzzy linguistic multiple criteria decision making problems. The step by step algorithms of these two methods are given for the convenience of applications. Finally, a numerical example concerning the selection of ERP systems is given to illustrate the validation and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performance is compared with two well-known methods from literature. Computational experiments show that the PDIS method performs better than the other methods. [ABSTRACT FROM AUTHOR]

Decision on alternative-fuel vehicles is one of the most important problems for fleet operations. In this paper we propose a hierarchical hesitant fuzzy linguistic model that captures hesitant linguistic evaluations of multiple experts on multiple criteria for alternative-fuel vehicles. We apply the proposed model on the alternative-fuel vehicle selection problem of a home health care service provider in the USA. The results show that an electric vehicle is the best fit for the application in today’s conditions. We also show robustness of the decision through a sensitivity analysis as well as analyze three scenarios representing possible changes in conditions. [ABSTRACT FROM AUTHOR]

This paper illustrates a method to deal with the decision making problems in case of uncertainty. This methodology, devoted to brain informatics, is based on an abstraction and a simulation of some brain’s emotional processing mechanisms using the fuzzy sets theory. To prove the performance of the proposed method, we have studied a problem of websites ranking which represents an important research theme in the World Wide Wisdom Web (W4). Based on the results, we have realized that underestimating the fuzziness of information by dealing with them as only as abstract elements, could significantly affect the final conclusion and automatically the final decision. [ABSTRACT FROM AUTHOR]

Group decision making is an important part of multiple criteria decision making and the analytic hierarchy process (AHP). The aim of this paper was to compare group AHP methods. Seven simple group AHP aggregation techniques that could be attractive for applications selected from the vast array of group AHP models proposed in the literature were selected for evaluation. We developed three new measures of evaluation: group Euclidean distance, group minimum violations, and distance between weights for the purpose of evaluation. The results of seven group AHP methods of the theoretical example were evaluated by three new evaluation measures, satisfactory index and fitting performance index. Furthermore, a case study of a decision making problem from the construction engineering field was performed and nine group AHP aggregation techniques, seven of them formerly presented and two new two stage group approaches were applied. Finally, the case study was evaluated using all five measures for each of the nine group decision making methods. The results showed that not all group AHP methods are equally convenient and that the selection of the method depended on the specific application. [ABSTRACT FROM AUTHOR]

How to select the suitable suppliers in the supply chain is critical for an organization’s success and has attracted much attention of both researchers and practitioners. Supplier selection can be regarded as a complex group multiple criteria decision making problem requiring consideration of a number of alternative suppliers and quantitative and qualitative criteria. Additionally, decision makers cannot easily express their judgments on the alternatives with exact numerical values in many practical situations, and there usually exists uncertain and incomplete assessments. In response, this paper proposes an extended VIKOR method for group multi-criteria supplier selection with interval 2-tuple linguistic information. The feasibility and practicability of the proposed interval 2-tuple linguistic VIKOR (ITL-VIKOR) method are demonstrated through three realistic supplier selection examples and comparisons with the existing approaches. Results show that the ITL-VIKOR method being proposed is more suitable and effective to handle the supplier selection problem under vague, uncertain and incomplete information environment. [ABSTRACT FROM AUTHOR]

QUALIFLEX (QUALItative FLEXible multiple criteria method) is a very useful outranking method to deal simultaneously with the cardinal and ordinal information in decision making process. The purpose of this paper is to develop a hesitant fuzzy QUALIFLEX with a signed distance-based comparison method for handling multi-criteria decision-making problems in which both the assessments of alternatives on criteria and the weights of criteria are expressed by hesitant fuzzy elements (HFEs). We propose a novel concept of hesitancy index for the HFE to measure the degree of hesitancy of the decision-maker or the decision organization. By taking their hesitancy indices into account, we present a signed distance-based method to compare the magnitude of HFEs. Using the signed distance-based comparison approach, we define the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index. By investigating all possible permutations of alternatives with respect to the level of concordance/discordance of the complete preference order, the optimal ranking orders of alternatives can be obtained. An application study of the proposed method on green supplier selection is conducted. The study indicates that the proposed method does not require the complicated computation procedures but still yields a reasonable and credible solution. [ABSTRACT FROM AUTHOR]

Supply chain environment is more dynamic and unpredictable than the past; therefore, it needs to be highly flexible in order to reconfigure in response to changes in their environment on the spur of the moment. This study presents a framework for supplier selection based on product-related and organization-related characteristics of the suppliers to be more competitive in the market and flexible to overcome probable changes in demands, supplies etc. Product-related and organization-related characteristics are those which are named in this study as lean and agile criteria respectively. Comprehensively digging up the literature, we extract the best criteria representing both leanness and agility of an organization. The aim of this paper is to select an appropriate supplier portfolio based on two aforementioned concepts. Supplier selection problem is solved using a combination of multi-criteria decision making (MCDM) methods. Due to the interaction between the criteria, analytical network process (ANP) is applied for determining the weight of each criterion for each alternative (supplier), and then data envelopment analysis (DEA) is used to rank them. The reason that DEA is used in this study is that when the number of suppliers increases, ANP approach tends to work inefficiently. Moreover, for determining the accurate interdependencies between the proposed criteria, fuzzy decision making trial and evaluation laboratory (DEMATEL) is applied. The framework is applied on a real case to demonstrate its applicability and feasibility. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making methods (MCDM) have been widely used throughout the last years to assist project contractors in selection processes related to the construction field. Sustainable urban drainage systems (SUDS) are an especially suitable discipline to implement these techniques, since they involve important impacts on each branch of sustainability: economy, environment and society. Considering that pervious pavements constitute an efficient solution to manage urban stormwater runoff as a source control system, this paper presents a multi-criteria approach based on the Integrated Value Model for Sustainable Assessments (MIVES) method to facilitate their proper selection. Given the lack of accurate information to shape the behavior of the alternatives regarding some of the criteria defining the decision-making environment, a series of variables are modeled by executing stochastic simulations based on the Monte Carlo methods. Additionally, a group of ten experts from various sectors related to water management was requested to provide their opinions about the importance of the set of selected criteria, according to the comparison levels of the Analytic Hierarchy Process (AHP). These judgments are converted into triangular fuzzy numbers, in order to capture the vagueness that human attitude entails when making judgments. A case of study in which the three major types of pervious pavements (porous asphalt, porous concrete and interlocking concrete pavers) are evaluated is presented to demonstrate the potential of the model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposes a novel quantification of two key concepts in expert/intelligent systems. • These concepts are the knowledge and ignorance levels. • Some theoretical results are developed to provide a holistic and in-depth treatment. • Some illustrative examples demonstrate key computational issues. • These results can become an integral foundation for more important developments. People use knowledge on several cognitive tasks such as when they recognize objects, rank entities such as the alternatives in multi-criteria decision making, or for classification tasks of decision making / expert / intelligent systems. When people have sufficient relevant knowledge, they can make well-distinctive assessments among entities. Otherwise, they may exhibit some uncertainty. This paper establishes two differential equations, of which one is for the interaction between the knowledge level and the uncertainty level, and the other is for the interaction between the ignorance level and the uncertainty level. By solving these two differential equations under certain boundary conditions, one can derive that the proposed knowledge level indicator is equivalent to Wierman's knowledge granularity measure up to a constant (exactly, ln2). Moreover, the knowledge level indicator and the ignorance level indicator are found to be in a complementary relationship with each other. That is, more knowledge implies less ignorance, and vice-versa. The results of this study bridge a critical gap that exists in the understanding of the concepts of knowledge and ignorance. [ABSTRACT FROM AUTHOR]

• A new form of the ranking based on optimal points MCDM method is developed. • A new weighting method is developed called Win, Loss, Draw method. • RBOP imitates the decision maker's behavioral pattern to find the best alternative. Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting method s called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process. [ABSTRACT FROM AUTHOR]

• Define Choquet integral based intuitionistic fuzzy arithmetic aggregation operator. • Combine the defined operator with ordered weighted aggregation operator. • Propose a decision-making method based on the combined operator. Intuitionistic fuzzy set (IFS), a classical extension of fuzzy set, is a powerful tool to describe fuzzy information in multi-criteria decision-making (MCDM) because of its membership and non-membership functions. Intuitionistic fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to criteria into one unique output. The traditional intuitionistic fuzzy aggregation operator-based methods have been criticized because of reasons that include disregard for comprehensive correlative relationships of criteria and ignorance for combination of fuzzy measures of criteria and weights of positions. In this paper, the effectiveness of traditional aggregation operator-based MCDM techniques is improved by a novel method to select the best alternative(s) under the intuitionistic fuzzy environment. We begin by defining the Choquet integral-based intuitionistic fuzzy arithmetic aggregation (CIIFAA) operator, the remarkable properties of which are proved in details. Further, the Choquet integral based intuitionistic fuzzy hybrid arithmetic aggregation (CIIFHAA) operator is defined as well as the proofs of its properties. The CIIFHAA operator can not only capture the comprehensive correlative relationships of criteria in a simpler manner, but also combine the weights of positions. Then, we put forward a MCDM method based on CIIFHAA operator with intuitionistic fuzzy evaluations. An illustrative example is solved, the best alternative and the ranking of alternatives are obtained, which can verify the feasibility of the proposed method. Finally, two comparisons are conducted to illustrate the stability and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

• PHFLTSs are adopted to express epistemic uncertainty of group members for risk assessment. • MCDM methods by PHFLTSs namely SNA, MCM, BWM, MDM, and TOPSIS are used in the proposed FMEA model. • The subjective, objective and integrated weights of group members and risk factors are considered. • A case study with sensitive and comparative analyses is used to verify the proposed FMEA model. Failure mode and effects analysis (FMEA) usually requires multi-domain specialists to implement the group risk assessment for identifying and eliminating system failures. Therefore, this paper combines several multi-criteria decision making (MCDM) techniques with probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) to implement risk assessment of failure modes by a panel of specialists. It aims at overcoming some defects existing in the conventional FMEA, such as without epistemic uncertainty and group risk assessment, as well as with some questions incurring from the risk priority number (RPN). Consequently, group members utilize PHFLTSs to express their subjective uncertain risk assessments on failure modes, in which the social network analysis (SNA) and maximizing consensus method (MCM) are exploited to derive the subjective and objective weights of group members respectively, afterwards their integrated weights are employed to aggregate individual risk assessments into the collective risk assessment. Additionally, the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure modes. Finally, an example with sensitive and comparative analyses is presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• Strong correlation between an algorithm ranking results and PCA. • PCA is a valid quantitative approach to criteria weight assignment in group MCDM. • PCA is an excellent tool for personnel selection or hiring processes. Assignment of criteria weights during group multi-criteria decision-making (MCDM) processes is a challenging and time-consuming process often rife with subjectivity. This is particularly evident in hiring or personnel selection processes. Both the military and corporate aviation communities have required a large influx of pilots recently due to an increasing pilot shortage. This paper outlines a study conducted to evaluate the effectiveness of principal component analysis (PCA) as an objective weight assignment method to establish a rank order of United States Army pilots based upon their emotional intelligence, safety attitude, and safety citizenship scores. PCA results were compared to an algorithm tracking the rank order of pilots using every possible criteria weighting combination to test PCA's validity as a criteria weight assignment method. Regression analysis demonstrated a strong correlation (p < 0.000) between the algorithm ranking results and PCA's ranking results, reinforcing PCA's validity as a quantitative approach to criteria weight assignment in group MCDM applications. Implications of this study include increased efficiency and reduced subjectivity in group-based MCDM pilot selection processes. [ABSTRACT FROM AUTHOR]

Abstract: The material plays an important role in an engineering design process. The suitable material selection for a particular product is one of the vital tasks for the designers. In order to fulfil the product’s end requirements, designers need to analyze the performance of various materials and spot suitable materials with precise functionalities. Due to the presence of large number of materials with diverse properties, the material selection process is complicated and time consuming task. There is a necessity of systematic and efficient approach towards material selection to choose best alternative material for a product. The aim of this paper is to describe the application of four Multi Criteria Decision Making methods for solving pipes material selection problem in sugar industry. FAHP-TOPSIS, FAHP-VIKOR, FAHP-ELECTRE, FAHP-PROMTHEE are the four methods used to choose the best alternative among the various materials. The ranking performance of various MCDM methods is also compared with each other and exploring the effectiveness and flexibility of VIKOR method. Five stainless steel grades such as J4, JSLAUS, J204Cu, 409M, 304 and seven evaluation criteria such as yield strength, ultimate tensile strength, percentage of elongation, hardness, cost, corrosion rate and wear rate are focussed in this study to choose the suitable material. [Copyright &y& Elsevier]

Highlights: [•] The paper proposes a peer-based modification to intuitionistic fuzzy multi-criteria group decision making with TOPSIS. [•] A peer voting procedure supports the achievement of a wider consensus. [•] IFWA operator is adopted to aggregate the opinions on the relevance of decision makers. [•] IFWA is also used to aggregate the opinions on the importance of criteria and alternatives. [•] The method is applied to the selection of a packaging machine in food industry. [ABSTRACT FROM AUTHOR]

Abstract: Supplier selection is an important issue in supply chain management (SCM), and essentially is a multi-criteria decision-making problem. Supplier selection highly depends on experts’ assessments. In the process of that, it inevitably involves various types of uncertainty such as imprecision, fuzziness and incompleteness due to the inability of human being’s subjective judgment. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method. Within the proposed method, D numbers extended fuzzy preference relation has been involved to represent the decision matrix of pairwise comparisons given by experts. An illustrative example is presented to demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: We consider the issue of supplier selection by using rule-based methodology. Supplier Selection (SS) is an important activity in Logistics and Supply Chain Management in today’s global market. It is one of major applications of Multiple Criteria Decision Analysis (MCDA) that concerns about preference-related decision information. The rule-based methodology is proven of its effectiveness in handling preference information and performs well in sorting or ranking alternatives. However, how to utilize them in SS still remains open for more studies. In this paper, we propose a novel Believable Rough Set Approach (BRSA). This approach performs the complete problem-solving procedures including (1) criteria analysis, (2) rough approximation, (3) decision rule induction, and (4) a scheme for rule application. Unlike other rule-based solutions that just extract certain information, the proposed solution additionally extracts valuable uncertain information for rule induction. Due to such mechanism, BRSA outperforms other solutions in evaluation of suppliers. A detailed empirical study is provided for demonstration of decision-making procedures and multiple comparisons with other proposals. [Copyright &y& Elsevier]

Abstract: Due to green legislations, industries track the used products through reverse logistics contractors. A reverse logistics programme offers significant cost savings in procurement, transportation, disposal and inventory carrying. Since reverse logistics operations and the supply chains they support are considerably more complex than traditional manufacturing supply chains, it can be offered to third party contractors. But availability of more number of contractors make evaluating and selecting the most efficient Reverse Logistics Contractor (RLC) a challenging task and treated as a multi-criteria decision making problem. In this paper, a hybrid method using Analytical Hierarchy Process (AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is proposed. AHP is used to obtain the initial weights and Fuzzy TOPSIS is used to get the final ranking. A case study demonstrates the application of the proposed method. Finally sensitivity analysis is carried out to confirm the robustness. [Copyright &y& Elsevier]