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To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals.

Although Multi Criteria Decision Making (MCDM) methods have been applied in numerous case studies, many companies still avoid employing these methods in making their decisions and prefer to decide intuitively. There are studies claiming that MCDM methods provide better rankings for companies than intuitive approaches. This study argues that this claim may have low validity from a company’s perspective. For this purpose, it focuses on one of the MCDM methods referred to as the Analytic Hierarchy Process (AHP) and shows that AHP is very likely to provide a ranking of options that would not be acceptable by a rational person. The main reason that many companies do not rely on current MCDM methods can be due to the fact that managers intuitively notice ranking errors. Future studies should end the promotion of outdated approaches, pay closer attention to the deficiencies of the current MCDM processes, and develop more useful methods.

The complex nature of the realistic decision-making process requires the use of Pythagorean fuzzy (PF) sets which have been shown to be a highly promising tool capable of solving highly vague and imprecise problems. Multiple criteria decision analysis (MCDA) methods within the PF environment are very attractive approaches for today's intricate decision environments. With this study, an effective compromise model named as the PF technique for order preference by similarity to ideal solutions (TOPSIS) is proposed based on some novel PF correlation-based concepts to overcome the complexities and ambiguities involved in real-life decision situations. In contrast to the existing distance-based definitions, this paper develops new closeness indices based on an extended concept of PF correlations. This paper employs the proposed PF correlation coefficients to construct two types of closeness measures. A comprehensive concept of PF correlation-based closeness indices can then be established to balance the consequences yielded by the two closeness measures. Based on these useful concepts, an effective PF TOPSIS method is proposed to address MCDA problems involving PF information and determine the ultimate priority orders among competing alternatives. Feasibility and practicability of the developed approach are illustrated by a medical decision-making problem of inpatient stroke rehabilitation. Finally, the proposed methodology is compared with other current methods to further explain its effectiveness.

This study examines the impact of corporate social responsibility (CSR) news reports on corporate operating performance forecasting using a large database of publicly-listed electronics firms in Taiwan. Applying text mining techniques and latent topic modelling, we construct and measure the intensity of the CSR-corpus index (ICSRI), which can compress tremendous amounts of CSR textual information content into synthesized meaningful dimensions. By doing so, we are able to break down CSR news reports into multiple dimensions and then examine which dimension(s) affects operating performance. To offer decision-makers with a comprehensive, overarching view of the corporate’s operations, this study incorporates balanced scorecards (BSC) and multiple criteria decision analysis (MCDA) to form a final performance rank. The proposed approach, supported by real samples, can assist both internal and external stakeholders in allocating scarce resources to specific CSR dimensions to enhance a corporate’s growth potential as well as to achieve a win-win situation.

Although Multi Criteria Decision Making (MCDM) methods have been applied in numerous case studies, many companies still avoid employing these methods in making their decisions and prefer to decide intuitively. There are studies claiming that MCDM methods provide better rankings for companies than intuitive approaches. This study argues that this claim may have low validity from a company's perspective. For this purpose, it focuses on one of the MCDM methods referred to as the Analytic Hierarchy Process (AHP) and shows that AHP is very likely to provide a ranking of options that would not be acceptable by a rational person. The main reason that many companies do not rely on current MCDM methods can be due to the fact that managers intuitively notice ranking errors. Future studies should end the promotion of outdated approaches, pay closer attention to the deficiencies of the current MCDM processes, and develop more useful methods. [ABSTRACT FROM AUTHOR]

Introduction: Multiple Criteria Decision Analysis (MCDA) is increasingly used in health care mainly because it moves decision-making from ad hoc to an evidence-based and comprehensive process. Developing countries with more restricted financial and human research capacities, however, should consider their own methods of MCDA development and implementation. Areas covered: An MCDA framework to improve procurement decisions of off-patent pharmaceuticals was developed for developing countries and adapted to Indonesia, Kazakhstan and Vietnam during three policy workshops. Based on the experience of these workshops and one joint workshop with international experts and decision makers from multiple developing countries, general recommendations were formulated on how to implement MCDA specifically in developing countries. We provide 17 practical MCDA implementation recommendations in four major areas, including (1) MCDA objectives; (2) technical considerations of MCDA tool; (3) development and customization of MCDA tool and (4) policy implementation of MCDA in decision-making. Expert commentary: These practical MCDA recommendations for developing countries contribute to feasible, transparent, stepwise, iterative and standardized decision-making in health care. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a novel correlation-based compromise approach for addressing multiple criteria decision analysis problems under complex uncertainty based on Pythagorean fuzzy sets. Based on the concepts of information energy and correlations for Pythagorean fuzzy characteristics, this paper proposes useful correlation-based compromise indices and investigates their desirable properties. These compromise indices can be employed to underlie anchored judgments and to reflect a certain balance between the correlations with positive-ideal and negative-ideal points of reference. Moreover, they can fully take into consideration the amount of information associated with higher degrees of uncertainty and can effectively fuse subjective assessments conveyed by Pythagorean fuzziness. From two different perspectives of displaced and fixed ideals, this paper provides two algorithmic procedures of the proposed correlation-based compromise approach for conducting multiple criteria evaluation tasks within Pythagorean fuzzy environments. A real-world problem concerning a financing decision on working capital policies is investigated to show the feasibility and applicability of the developed techniques. The application results, along with a comparative analysis, demonstrate the practicality and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Given the increasing number of brownfields in China, the Chinese government is now facing a huge financing gap in brownfield remediation projects. Accordingly, the authors present a framework that can assist a provincial government in classifying its brownfields so that the funding can be appropriately allocated. More specifically, the framework starts from constructing a criteria system based on the existing literature and expert opinions. Then, in consonance with the criteria system, a sample set of brownfields is measured by experts and then trained using a Dominance-Based Rough Set Approach to derive judging rules. After the validation, those rules are employed to generate an ordinal classification of the complete set of brownfields. The resulting ordinal classification provides the government with a sequence of brownfields from the highest priority to the lowest, which can support the government in budgeting and planning. Furthermore, as the number and conditions of the brownfields change over time, the criteria system and corresponding categories can be updated and reapplied at the beginning of each fiscal year (or each planning horizon) to facilitate the government’s decision making. The proposed decision process can also be applied to assist the investment decisions of any brownfield stakeholders in other counties with limited funding based on their specific preference. [ABSTRACT FROM AUTHOR]

In this paper, we present IMPROMPTU, a distributed resource consolidation manager for larger-scale commodity computing clouds. The main contribution of this work is two-fold. First, IMPROMPTU fully distributes the responsibility of resource consolidation management among autonomous node agents that have a one-to-one mapping with the physical machines in the cloud. Second, autonomous node agents manage virtual to physical machine resource consolidation using multiple criteria decision analysis (MCDA) through PROMETHEE II method. MCDA has been previously used within the context of computing systems, particularly in fields such as multi-agent systems, data mining, and wireless communications. However, to the best of our knowledge, IMPROMPTU represents the first fully distributed MCDA approach applied to the problem of autonomous resource consolidation management for commodity computing clouds. Moreover, IMPROMPTU improves on our previous studies by introducing key extensions to enhance the granularity of the MCDA model. Simulation results show that the proposed solution provides a strong alternative to prior resource consolidation management approaches for the key industry problem of mitigating SLA violations. This establishes solid groundwork for further applications and extensions of MCDA to this important problem domain. [ABSTRACT FROM AUTHOR]

Foot-and-mouth disease (FMD) prevention and control is a challenge worldwide but the situation in southern Africa is particularly complex because the virus is endemic in wild African buffalo ( Syncerus caffer) . The objective of this study was to compare stakeholder perceptions of the FMD control methods employed to restrict FMD virus to the infected zone of South Africa. Data collection was performed using an online questionnaire distributed to FMD experts, government veterinarians, private livestock veterinarians, people involved within the wildlife sector, and “other” occupation groups including the general public. Data were also collected using semi-structured participatory group discussions with government animal health technicians (AHT) and communal cattle owners directly affected by FMD control measures. Evaluated control methods were the disease control fence bordering the western boundary of the Greater Limpopo Transfrontier Conservation Area, clinical surveillance of livestock, movement control of cloven-hoofed animals and products, and routine FMD vaccination of cattle. These management procedures were scored according to a set of technical, economic, and ethical criteria by stakeholders, who also weighted the criteria according to their perceived importance. Scores and weights were aggregated using an additive linear model to rank control methods. Sensitivity analysis was performed using a stochastic model to explore the effects of varying inputs and the exclusion of scores from randomly selected respondent groups on the ranking of control methods. The deterministic analysis assigned the highest ranking to the disease control fence and the lowest to routine vaccination of cattle. The fence had the highest ranking in 40% of the stochastic iterations, and second, third and fourth in 26%, 20% and 14% of iterations, respectively. The inputs from the AHT and people involved in the wildlife sector were the most influential for ranking the fence as the preferred control option. The most influential criteria were the feasibility of the fence as a control option and its influence on the economics of the communal cattle owners, livestock industry in the FMD free zone, and the government. The disease control fence was the highest ranking control option but further investigations are necessary to understand the reasons for stakeholder perceptions. [ABSTRACT FROM AUTHOR]

Many multiple criteria decision analysis (MCDA) methods have been proposed over the last decades. Some of the most known methods share some similarities in the way they are used and configured. However, we live in a time of change and nowadays the decision-making process (especially when done in group) is even more demanding and dynamic. In this work, we propose a MCDA method that includes cognitive aspects (cognitive analytic process, CAP). By taking advantage of aspects such as expertise level, credibility and behaviour style of the decision-makers, we propose a method that relates these aspects with problem configurations (alternatives and criteria preferences) done by each decision-maker. In this work, we evaluated the CAP in terms of configuration costs and the capability to enhance the quality of the decision. We have used the satisfaction level as a metric to compare our method with other known MCDA methods in literature (utility function, AHP and TOPSIS). Our method proved to be capable to achieve higher satisfaction levels compared to other MCDA methods, especially when the decision suggested by CAP is different from the one proposed by those methods. [ABSTRACT FROM AUTHOR]

Performance measurement has become an increasingly important issue in recent years. In spite of the remarkable progress that has been achieved in this area of research, many performance measurement initiatives fall short of their potential in supporting decision-making. This paper argues that adopting a multi-method approach to assessing performance has the potential to result in more comprehensive and effective performance measurement systems. To support this assertion, the paper discusses the development of a performance measurement system for a Business Tax Department, which combined the use of several operational research (OR) techniques including qualitative system dynamics, data envelopment analysis and multiple criteria decision analysis. The use of these OR techniques was influential in developing and implementing the performance measurement system and has the potential to be transferred to other contexts. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop novel VIKOR-based methods for multiple criteria decision analysis involving Pythagorean fuzzy information. The concept of Pythagorean fuzzy sets possesses definite advantages in handling vagueness and complex uncertainty over well-known nonstandard fuzzy sets, such as intuitionistic fuzzy sets and interval-valued fuzzy sets. Taking the powerfulness of Pythagorean fuzzy sets into account when tackling imprecise and ambiguous information in multiple criteria decision-making problems, this paper proposes remoteness index-based Pythagorean fuzzy VIKOR methods, which are significantly different from the existing VIKOR techniques. The uniqueness of the proposed VIKOR methodology is the consideration of uncertain information represented by Pythagorean fuzzy values and the construction of certain valuable concepts of a generalized distance measure and displaced and fixed remoteness indices. A flexible and multipurpose definition of a distance measure for Pythagorean fuzzy information is presented based on the Minkowski distance model. The generalized Pythagorean fuzzy distance measure presented in this paper is then applied to establish useful concepts of remoteness indices, consisting of displaced positive- and negative-ideal remoteness indices as well as fixed positive- and negative-ideal remoteness indices. Unlike the canonical VIKOR ranking procedure, this paper provides a new way to rank candidate alternatives and determine the compromise solution depending on distinct preference structures for adapting to the particularities within the Pythagorean fuzzy environment. Several useful multiple criteria ranking indices, consisting of remoteness-based group utility, individual regret, and compromise indices, are developed to facilitate compromise ranking among alternatives. Four algorithmic procedures of the presented remoteness index-based Pythagorean fuzzy VIKOR methods are also provided in detail. Furthermore, some real-world applications and comparative analyses concerning a criteria satisfaction problem, two service quality and Internet stock performance evaluation problems, and two Internet stock and R&D project investment problems are conducted to demonstrate the effectiveness and advantages of the proposed methods in practice. [ABSTRACT FROM AUTHOR]

Abstract: Recently, a novel approach (to be referred to as CEU) was introduced for the frequently arising problem of combining the conflicting criteria of equity and utilitarianism. This paper provides additional insights into CEU and assesses its added value for practice by comparing it with a commonly used extended goal programming (EGP) approach. The comparison comprises the way of balancing equity and utilitarianism, the number and spacing of solutions, discrete versus continuous nature, method‐specific parameters, distance to the Pareto front, and computational effort. CEU balances between equity and utilitarianism in a way that is basically different from using a convex combination of these two criteria. Moreover, CEU's parameter has an intuitive interpretation. The set of solutions generated by CEU is smaller and more widely spaced than EGP's set of solutions, which can be an advantage for the decision maker. CEU generates solutions on the Pareto front of the decision maker's n‐criteria problem. However, CEU's way of balancing equity and utilitarianism causes a (small) distance to the Pareto front of the associated bicriteria problem on the aggregate criteria. Reporting this distance will support the decision maker to assess whether the achieved balance is worth its price. Using CEU may require a larger computational effort than using EGP. [ABSTRACT FROM AUTHOR]

The theory of Pythagorean fuzzy sets possesses significant advantages in handling vagueness and complex uncertainty. Additionally, Pythagorean fuzzy information is useful to simulate the ambiguous nature of subjective judgments and measure the fuzziness and imprecision more flexibly. The aim of this research is to develop an effective assignment-based method using a novel concept of correlation-based precedence indices for conducting multiple criteria decision analysis within the Pythagorean fuzzy uncertain environment. Based on the ideas of information energy and correlations, this paper defines a novel concept of correlation-based precedence indices in the Pythagorean fuzzy context and discusses their desirable properties. Next, this paper presents some useful concepts of discordance indicators, weighted discordance indicators, comprehensive discordance indicators, and comprehensive discordance indices to construct a novel assignment model for acquiring a comprehensive ranking of candidate alternatives. As an application of the proposed assignment-based method, a practical example concerning a financing decision of working capital policies is provided to demonstrate its practicality and effectiveness.

In alternative generation, reusing past experience is a potential methodology and case retrieval is a primary step. In order to improve the performance of case retrieval process, many applications have used different similarity measurements and the selection method for the most suitable historical case to solve problems. Many investigations have shown that human beings are usually bounded rational and their psychological behavior has certain influence on decision making. However, such behavior is neglected in similarity measurements and the selection method can only deal with the evaluation given by one decision maker (DM). This paper proposes a new case retrieval method that combines similarity measurement and data envelopment analysis (DEA) model. A similarity measurement based on cumulative prospect theory is proposed to consider the DM’s psychological behavior. A hybridization of four similarity measurements is used to generate a set of similar historical cases. The DM evaluates the similar historical case set by a pairwise comparison matrix. A DEA model is constructed to get the priority vector. The most suitable historical case can then be picked out through the case similarity and the case priority. A case study is finally introduced to illustrate the use of the proposed method.

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.

This study demonstrates an analysis of executives based on their management competences. The aim of the study is to identify those executives that show a greater strategic alignment with the guidelines set by an organization. The analysis is performed using self-evaluation and corporate evaluation by considering each management competence as a criterion. It makes use of the Dominance principle (i.e., the Dominance based Rough Set Approach, denoted here as DRSA). The use of DRSA permits the inference of decision rules and identifies the revisions that violate that principle. The main advantage for an organization from using this procedure (i.e., DRSA and RST) is offering the organizational decision makers a comprehensive analysis of their executives. As a major result, training programs aligned to the expectations and needs of the organization can be developed and implemented

Bridge management is essential for optimizing available resources. Prioritising maintenance and assessing the effectiveness of different maintenance strategies are based on the conventional condition ratings of bridge components. This paper presents the development of a Decision Support System for maintenance management at the Network Bridge Level, based on the theory of the Dominance-Based Rough Set Approach. Dominance-Based Rough Set Approach produces a decision model expressed in terms of easily understandable “if…, then…” decision rules which control the decision process and avoid the “black box” effects of many alternative decision support methods. In the proposed Decision Support System, bridges are described by a set of 16 parameters that describe the state of degradation, structure, territory, traffic and network characteristics. To illustrate this Decision Support System, a case study involving Italian roads is presented. The proposed Decision Support System is a flexible tool because it allows updating parameters periodically as a consequence of practice, expertise and management policies.

A bioeconomy is an economic system, which replaces fossil resources with renewable biological resources. As strategies are implemented, new bio-based value chains are created and others expand to replace fossil fuel based supply chains. Understanding the sustainability impacts of this development requires tools for assessing the impacts. The environmental impacts of bioeconomies have been studied through life cycle assessment, but the social impacts are poorly understood. Commonly applied social sustainability methods are local in scope and lack a life cycle perspective. The aim of this paper was to compare the priority setting in global and local approaches to social sustainability assessment and to explore possibilities for integrating them. A multi-region input-output model was used to estimate the social life cycle impacts of Finnish wood products. The main social issues were found in health and safety and gender inequality, with a large part of the impacts occurring outside the forest sector and outside Finnish boundaries. In contrast, local stakeholders views on social sustainability focused mostly on local conditions, employment and co-operation between companies. Ways to combine the contrasting results were explored and a framework for integrating the local and global approaches was outlined. [ABSTRACT FROM AUTHOR]

We present a group decision making framework for evaluating sustainability of the insulating materials. We tested thirteen materials on a model that was applied to retrofit a traditional rural building through roof’s insulation. To evaluate the materials from the socio-economic and environmental viewpoints, we combined life cycle costing and assessment with an adaptive comfort evaluation. In this way, the performances of each coating material were measured in terms of an incurred reduction of costs and consumption of resources, maintenance of the cultural and historic significance of buildings, and a guaranteed indoor thermal comfort. The comprehensive assessment of the materials involved their assignment to one of the three preference-ordered sustainability classes. For this purpose, we used a multiple criteria decision analysis approach that accounted for preferences of a few tens of rural buildings’ owners. The proposed methodological framework incorporated an outranking-based preference model to compare the insulating materials with the characteristic class profiles while using the weights derived from the revised Simos procedure. The initial sorting recommendation for each material was validated against the outcomes of robustness analysis that combined the preferences of individual stakeholders either at the output or at the input level. The analysis revealed that the most favorable materials in terms of their overall sustainability were glass wool, hemp fibres, kenaf fibres, polystyrene foam, polyurethane, and rock wool. [ABSTRACT FROM AUTHOR]

A fundamental and widely applied approach to decision-making is the multi-attribute utility theory. Because it aggregates preferences in the compensatory manner, it can be extended with the concept of veto function, which is adopted from the outranking approach, and models full or partial non-compensation of unsatisfactory preferences. This paper discusses the methodological foundations underlying this approach, and introduces the mechanisms of decision support on the basis of utility models with discordance-related information. It then presents the main contribution of the research, in which the forms and properties of veto functions are studied, particularly with respect to risk aversion. Outcomes of risk averse, risk seeking and risk neutral veto functions are analysed and compared. Risk aversion of veto functions is also correlated with risk aversion of utility functions, which are aggregated in the same multi-attribute model and exhibit common complementary preference structures of the decision-maker. The study aims to assess the influence of risk aversion on the decision, to identify possible anomalies in preference structures, to determine the suitability of different risk aversion formats and intensities for various problem settings, and to derive key characteristics. The experimental model considers several evaluation factors, such as validity of results, ability to efficiently discriminate alternatives, richness of output data, extremeness of results and robustness. [ABSTRACT FROM AUTHOR]

Preference elicitation is a challenging activity in any decision-making process, yet preferences are fundamental since the recommendations are meaningful and acceptable only if the Decision Maker's values are taken into account. This study proposes an ex-post application of a recent ranking method named Simple Ranking with Multiple Points (S-RMP) to support a participatory decision-making process. The method has been tested on a real-world case study simulating the selection of the most suitable site for locating a new landfill. The purpose of the research is twofold: (i) to explore the applicability and relevance of the S-RMP method to support environmental decision-making processes and (ii) to provide guidelines for the elicitation of preference parameters of the S-RMP ranking method. The results highlight that the proposed method opens a promising line of research in the environmental decision-making domain, thanks to its ability to use heterogeneous information consistently with the increasing amount of qualitative data embedded in real decision-making processes. [ABSTRACT FROM AUTHOR]

The concept of university-industry partnership sustainability (UIPS) stands for well-adjusted progress among key players from universities and industry by sustaining their welfare, both in the present and in the future. This paper sought to develop an evaluation system for UIPS. The need for such a system is justified at three levels: the micro level (i.e., research and innovation performance, transfer and absorptive capability, and technology development), the meso level (i.e., institutional arrangements, communication networks, and local and indigenous rules) and the macro level (i.e., supply and demand, regulations, financing, taxes, culture, traditions, market, climate, politics, demographics, and technology). The UIPS evaluation system developed in this study offers the possibility of calculating a fair value of UIPS and providing recommendations for improving university--industry (U--I) partnerships. This can be of great importance for entrepreneurial universities that would like to strengthen their corporate links and/or reduce/reverse the "hollowing effect" of globalisation in disadvantaged regions. Additionally, this paper also contains discussions on the advantages, limitations, and managerial implications of this proposal. [ABSTRACT FROM AUTHOR]

The increasing complexity of hospital management indicates a greater need for scientific instruments that underlie the performance evaluations of the organizational processes in this sector. From this perspective, this work evaluates the performance of the main activities of the internal logistics process of a Brazilian public teaching hospital's supply chain. To this end, a multicriteria decision analysis approach is used as an instrument of intervention. The multicriteria model constructed serves as a decision support system to aid decision makers, allowing managers to measure logistics performance in the hospital context and to visualize the performance profile of the main logistics activities, enabling the identification of activities requiring greater attention and improvement. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to develop a likelihood-based assignment method based on interval type-2 fuzzy sets and apply it to decision-making problems involving multiple criteria evaluation and the ranking/selection of alternatives. The linear assignment method is a well-known outranking method in the field of multiple criteria decision analysis. The theory of interval type-2 fuzzy sets is useful for addressing the uncertainty and imprecision associated with a subjective environment. In this paper, the key feature of the proposed method is the incorporation of the extended concept of likelihoods of fuzzy preference relations between interval type-2 trapezoidal fuzzy numbers into the main structure of the linear assignment methodology. An effective ranking procedure using the optimal membership degree determination method is proposed to determine criterion-wise preference rankings of the alternatives. The proposed method establishes the novel concepts of an (adjusted) rank frequency matrix and an (adjusted) rank contribution matrix to combine the relative performances of the alternatives in terms of each criterion. Based on a signed distance comparison approach, this paper constructs a likelihood-based assignment model to obtain an aggregate ranking of the alternatives that is in the closest agreement with the criterion-wise preferences of the alternatives. The feasibility and applicability of the proposed method are illustrated with two practical multiple criteria decision-making applications concerning the selection of landfill sites and the selection of treatment options. Finally, a comparative analysis with other relevant methods is conducted to validate the effectiveness and advantages of the current methods in decision aiding. [ABSTRACT FROM AUTHOR]

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Concerning the increasing emphasis on risk management in this uncertain global environment, there is an urgent demand for practical decision support tools that support supply chain risk communication and management. This research proposes an integrated framework that takes explicit account of multiple types of risk in aiding decision-making, and compares and ranks alternative risk mitigation strategies individually and collectively in indicator basis using fuzzy set theory and multiple criteria decision analysis methods. Through an illustrative case, the research demonstrates that the proposed framework provides a holistic view of supply chain risks and enables firms to foresee, spot and respond to the exposed risks in an effective and efficient manner. [ABSTRACT FROM AUTHOR]