Multiple Criteria Decision Analysis Approach Research Articles

Change Management and Advanced Wireless Network Implementation: Detection of Monetization Opportunities and Challenges

This study focuses on the interconnection between two topics: change management and advanced wireless networks. Advanced wireless networks can generate turmoil at the deepest level of all sectors of activity by offering the possibility of new connectivity through these networks&#x2019; assets, which would require new business models and introduce novel features into purchasing processes and consumer experiences. In addition, telecommunications companies play a fundamental role in developing and implementing these new technologies to ensure they fulfil their promise. Change management practices are critical to ensuring successful advanced wireless network implementation, especially at a strategic and operational level. This research sought to contribute to a more holistic view of the interconnections between these two topics. The multiple-criteria decision analysis approach was used to conduct analyses of the relationships between the associated monetization opportunities and challenges, which included applying the decision-making trial evaluation laboratory technique to clarify their cause-effect links. A multidisciplinary expert panel was recruited to facilitate the method&#x0027;s application. The insights obtained during two panel meetings allowed the proposed process-oriented approach to assist decision makers in analyzing monetization opportunities and challenges related to advanced wireless network adoption according to the following five clusters: <i>service offer</i>; <i>vendor value proposition</i>; <i>operational management</i>; <i>strategic management</i>; and <i>external factors</i>. Contributions and limitations are also examined.

Enhancing water security through site selection of water harvesting structures in semi-arid regions: a GIS-based multiple criteria decision analysis

Abstract This research endeavors to develop a decision support tool aimed at ascertaining the optimal site selection for water harvesting structures in semi-arid regions, employing a geographic information system-based multiple criteria decision analysis approach. Water scarcity poses a formidable challenge in various countries, particularly in arid and semi-arid areas. Among the potential solutions, the adoption of water harvesting technologies stands out for capturing and storing rainwater to address water shortages. However, the effectiveness and efficiency of these structures hinge significantly on their precise placement. To address this issue, this paper introduces a comprehensive framework that integrates multiple criteria, encompassing rainfall intensity, land slope, soil type, and land use, to discern the most suitable locations for water harvesting structures. A practical application of the proposed methodology is demonstrated through a case study conducted in the Sabarmati river basin. The results demonstrate the effectiveness of the approach in identifying highly suitable locations for water harvesting structures, ultimately contributing to the enhancement of water security in the region.

Artificial Intelligence and Smart Cities: A DEMATEL Approach to Adaptation Challenges and Initiatives

This article focused on two main topics currently on among many agendas: smart cities and Artificial Intelligence (AI). The growing interest in the former is due to these cities&#x2019; multidimensionality and adaptability in terms of residents&#x2019; needs and the requirements of each municipality&#x0027;s reality. AI, in turn, currently plays a transformative, disruptive role in various areas by performing &#x201C;smart&#x201D; tasks, thereby facilitating the automation of processes and differentiation initiatives. Smart city strategic planning, especially in the long term, will most likely have to deal with adaptations to AI. Thus, research is needed that can contribute to a more holistic view of these topics and support decision-making processes in these areas. Based on the epistemological principles of the multiple-criteria decision analysis approach (MCDA), this article developed and tested a dynamic analysis system that allows smart city initiatives to address the challenges of adapting to AI. The proposed system highlights the cause-and-effect relationships in this context. The article included an application of the decision-making trial and evaluation laboratory (DEMATEL) technique. The procedural steps followed to implement this methodology were enhanced by close collaboration with an experienced decision maker, who has coordinated various projects in this research context. The proposed system&#x0027;s contributions and limitations were also analyzed in this article.

A framework for a net environmental benefit analysis based comparative assessment of decommissioning options for anthropogenic subsea structures: A North Sea case study

Taxpayers and operators worldwide have significant current liabilities associated with decommissioning of offshore Oil &amp;amp; Gas (O&amp;amp;G) assets. Consequently, decommissioning is at the forefront of industrial, governmental, and non-governmental agendas. Decommissioning is a highly complex activity with health, safety, environmental, social, economic, and technical implications. Increasing scientific evidence supports that manmade subsea structures create hard, artificial reef habitats that provide ecological and social benefits to society. Given the significant uncertainty regarding how subsea structures should be retired at the end of their operational lifetimes, it is necessary for governments, taxpayers, and operators to understand the risks and benefits associated with potential decommissioning options. Currently, the North Sea decommissioning process is based on the policies and direction of the Oslo and Paris Convention’s (OSPAR) Decision 98/3 and follow comparative assessment (CA) multiple-criteria decision analysis (MCDA) guidelines to determine the best overall strategy for decommissioning subsea structures; however, CA MCDA processes can be biased, ambiguous, difficult to use, interpret, and replicate, and limited in their consideration of multigenerational benefits. Consequently, to assist decision-makers in understanding and evaluating options and associated benefits for decommissioning subsea structures, this study adapted the net environmental benefit analysis (NEBA) framework to supplement and strengthen the CA process for evaluating decommissioning options for offshore O&amp;amp;G facilities. The net environmental benefit analysis based comparative assessment (NEBA-CA) framework is presented that addresses the growing need for a practical, quantitative, scientifically robust, defendable, and transparent MCDA approach to determine optimized decommissioning strategies for subsea assets. Increased transparency in CAs will provide an additional layer of credibility with regulators and society. The approach is data driven and a desktop analysis mainly relying on existing data. Using a North Sea case study, this work demonstrates the ability of NEBA-CA to resolve inherent complexity in comparing decommissioning options, thereby supporting operators in working with regulators to decommission assets in a way that maximizes ecosystem service benefits to society while managing site-related risks and costs. The NEBA-CA framework supplements and strengthens the standard CA process by 1) incorporating quantified metrics including multigenerational ecosystem service benefits and risks, 2) excluding front ranking (scoring) or weighting of metrics, and 3) providing consistent graphical displays to support visual differentiation of options and metrics.

A machine learning driven multiple criteria decision analysis using LS-SVM feature elimination: Sustainability performance assessment with incomplete data

The move towards an era of big data structured problems has led to the development of state-of-the-art solutions, mainly in decision sciences. Large Scale Decision-Making (LSDM) and Data-Driven Decision-Making (DDDM) approaches have emerged from a rapidly evolving research field. A common challenge in LSDM problems formed from various heterogeneous databases is incomplete data. Multiple criteria decision analysis approaches have been utilized as the primary element of LSDM techniques to solve similar complex problems, and these methods cannot function under incomplete data. Large number of dimensions in LSDM problems are usually problematic, which has previously been addressed by machine learning driven methods. Moreover, while in traditional multiple criteria decision analysis methods, the criteria are based on beneficial and non-beneficial attributes, in LSDM problems, decision-makers define specific objectives for attributes adjusting the problem with their specific preferences. This study proposed a machine learning-driven DDDM method for solving LSDM problems with incomplete data and a substantial number of decision attributes. Incomplete data imputation was conducted using the Expectation–Maximization (EM) algorithm, and Recursive Feature Elimination (RFE) with Least Square Support Vector (LS-SVM) was used to extract the core criteria. Target-based COmbined COmpromise Solution (T-CoCoSo) and Target-based Multi-Attributive Border Approximation Area Comparison (T-MABAC) methods combined with Shannon’s Entropy weighting method were used for the performance assessment stage. Ultimately, to validate the reliability of the proposed method, sustainability performance of all countries worldwide under Sustainable Development Goals (SDGs) with incomplete data was evaluated. The outcomes show that the proposed machine learning-driven data-driven decision-making method is a reliable tool for such LSDM problems.

Measurement and valuation of the attributes of innovation of healthcare technologies: a systematic review

Objective Innovative technologies (e.g. treatments) play a pivotal role in improving patient’s well-being and in consequence population health outcomes. However, there is lack of consensus and comprehensive summary what constitutes innovation. Additionally, valuing them using traditional cost-effectiveness analysis is unlikely to capture the full range of benefits of these innovative technologies. This review aims to understand how innovation attributes were measured and/or valued in healthcare. Materials and methods We systemically searched four databases, PubMed, Embase, PsycINFO, and Econlit, from inception to April 2021. Studies were included if they measured and/or valued the attributes of innovation for healthcare identified in our previous systematic review. Any other potential recommended methods to measure and/or value the innovation attributes were also extracted. Results Of 546 articles, a total of 17 articles were finally included in this review. If attributes were measured and traded-off relative to costs, then it was considered as valuation of those attributes. Two specific attributes of innovation, i.e. substantial benefits and convenience and/or adherence were measured using adherence rate and life year or QALY gain. When innovation attribute was non-specific it was described as “overall innovation” and measured using overall innovativeness scale (e.g. point/binary scale). QALY-based cost-effectiveness analysis (CEA) was commonly used to assess and value substantial benefit attribute. Other valuation approaches were (i) rating, (ii) the economic value of life year gain, (iii) multiple criteria decision analysis (MCDA), (iv) incremental net health benefit (INHB), and (v) quality-adjusted cost of care (QACC). ICER threshold adjustment and multiple-criteria decision analysis (MCDA) are two common recommended approaches to capture the innovation comprehensively. We found that MCDA approaches often promoted and discussed but were sub-optimally used to incorporate different value attributes into decision-making. Conclusions Existing methods used by payers to measure and value the innovation component of a new product do not reflect the full range of health and cost impacts. They generally do not consider the alternative perspectives of patients, providers, caregivers, and society. Key challenges remain to appropriately measure and value innovation attributes and incorporate them into HTA decision making.

Customer satisfaction in short food supply chains: A multiple criteria decision analysis approach

In this paper, we study customer satisfaction in short food supply chains. Specifically, we address how elements of the marketing mix of short food supply chains drive customer satisfaction. Our empirical work is based on a survey adopting a method known as the multicriteria satisfaction analysis. Our results show that the process of sales and the producers are the most important marketing mix elements and receive strong indications of customer satisfaction, thus offering a competitive advantage for short food supply chains. Pricing, place, purchase environment, and promotion can be described as potential threats to short food supply chains. Finally, the product is rated as low in importance but high in satisfaction, which suggests that consumers take its premium quality for granted. Our results suggest that emphasis should be placed on the processes of sales and producers.

Proper and improper uses of MCDA methods in energy systems analysis

Over the past few decades, the strategies to perform energy systems analysis have evolved into multiple criteria-based frameworks. However, there still remains a lack of guidance on how to select the most suitable Multiple Criteria Decision Analysis (MCDA) method. These methods provide different decision recommendations for the Decision Makers, including ranking, sorting, choice, and clustering of the alternatives (e.g., technologies or scenarios) under evaluation. They deal with a variety of data typologies and preferences, and lead Decision Makers in shaping the energy systems of the future. Here, we evaluate the MCDA methods used in 56 case studies performing energy systems analysis at different scales. We find that close to 60% of these studies chose an MCDA method that was not the most adequate for the respective decision problem. In particular, this concerned the use of weighting methods (e.g., Analytical Hierarchy Process) in MCDA approaches not suited for this type of weights, sub-optimal selection of MCDA techniques for specific types of problem statements, and lack of handling rather evident interactions in preference models. Our analysis demonstrates that these deficiencies can be overcome by using a recently developed methodology and software that support Decision Makers and analysts in selecting the most suitable MCDA method for a given type of decision-making problem.

The Regional Allocation of Asylum Seekers in Greece: A Multiple Criteria Decision Analysis Approach

One of the long-term challenges for policy makers in host countries of migrants is the optimal geographical allocation of the migrant population so as to strengthen integration outcomes and serve the crucial goal of social inclusion. The political debate on the appropriate placement policy of newcomers has continued for years after the large-scale inflows of asylum seekers in Greece. This paper focuses on the evaluation of the dispersal policy of asylum seekers in Greece as implemented under the reception and accommodation scheme. Furthermore, it provides decision makers with an alternative dispersal policy framework driven by the integration outcomes of the already established migrant population in the country. The research encompasses Eurostat NUTS 2 annual data for a choice upon availability of the Zaragoza Integration Indicators and the immigrant population indicator for the thirteen Greek regions. The data cover a ten-year period from 2010 to 2019, and they are analysed with the multiple criteria decision-making method PROMETHEE. In addition to desk research, fieldwork has been conducted to illuminate the results of the model. The paper extends the literature on the governance of migration and contributes to the planning of migrant integration policies in their host countries.

Enhancing strategic management using a “quantified VRIO”: Adding value with the MCDA approach

• A quantified VRIO is presented. • The proposed approach overcomes some limitations of the traditional VRIO framework. • MCDA improves understanding of causal relationships between resources and capabilities. • The Choquet integral adds value to the Resource-based View. Strategic management has been popularized since the 1960s as a useful tool in the search for success factors in organizations’ internal and external environments. Strategic management researchers have observed and created strategies now considered to be essential pillars of contemporary management operations. This approach relies on managers’ ability to understand and evaluate contextual trends, but a variety of questions have been left unanswered, especially regarding analyses of combinations of quantitative and qualitative decision criteria. This study sought to enhance strategic management by developing a quantified valuable, rare, inimitable, and organized (VRIO) framework using the multiple-criteria decision analysis (MCDA) approach. To accomplish this objective, the proposed VRIO model was combined with the Choquet integral, and a real-life application was conducted to test this approach’s adequacy to support strategic management. The integrated methodology developed was shown to offer an innovative business improvement process. The perceived benefits and limitations are also presented and analyzed.

An Extended Prospect Theory for Robot Evaluation and Selection considering Risk Preferences and Interactive Criteria

The evaluation and selection process can be regarded as a complex multiple criteria decision analysis (MCDA) problem which involves various interaction relationships among criteria under high uncertain environment. In addition, the decision-makers are always bounded rational in the risk decision-making process. However, the current robot evaluation and selection approach seldom considers the decision-maker’s risk preference and interactive criteria under high uncertain environment. Thus, the purpose of this paper is to develop a hybrid MCDA approach for solving the robot evaluation and selection problem. In the proposed framework, the interval type-2 fuzzy set is used to express the uncertain evaluation information provided by decision-makers. Next, the distance measure of interval type-2 fuzzy numbers is developed to determine the fuzzy measure of each criterion. Then, the extended prospect theory based on developed Choquet integral is proposed to evaluate and prioritize the robot by considering the decision-maker’s risk preference and interactive criteria. Finally, a case study of robot evaluation and selection in the auto industry is selected to exemplify the application of the proposed framework. After that, comparison and sensitivity studies are conducted to further demonstrate the robustness, effectiveness, and reasonableness of the developed approach.

Strengthening urban sustainability: Identification and analysis of proactive measures to combat blight

The process of identifying and analyzing proactive measures for the prevention of urban blight is complicated by its complexity and death of data. Blight affects people’s lives in many ways, including economically, socially, psychologically, and physically. The phenomenon comprises many factors, including making appropriate identification and eradication decisions that are challenging to analyze and structure. This study took a constructivist stance and sought to develop a new approach to urban blight to complement the existing literature. The methodology combined cognitive mapping techniques applied in decision conferences, and the decision making trial and evaluation laboratory technique based on the multiple-criteria decision analysis approach. This integrated approach facilitated the development of a multi-criteria model that identifies proactive anti-blight measures in a more complete, transparent way. This dual methodology enables incorporating objective and subjective elements, and field specialists’ knowledge and experiences, thereby reducing important decision criteria omissions. Additionally, this approach generates a transparent visualization of relationships among criteria. Identified blight determinants can be used to prevent blight, and improve the quality of life in the affected areas. These strategies were carefully analyzed by two independent experts from the Direção Geral do Território (General Directorate of the Territory of Portugal). The advantages and limitations of the proposed methodology are also deliberated. • This real-world study contributes to a holistic understanding of urban blight. • The process-oriented approach followed overcomes some limitations of purely statistical methods. • Cognitive mapping and DEMATEL improve the understanding of proactive anti-blight strategies. • No prior research was found that has adopted the proposed methodology in this study context.

Ranking product systems based on uncertain life cycle sustainability assessment: a stochastic multiple criteria decision analysis approach

Purpose – Life cycle sustainability assessment (LCSA) provides useful and comprehensive information on product system performance. However, it poses several challenges for decision-making process due to (i) multidimensional indicators, (ii) conflicting objectives and (iii) uncertainty associated with the performance assessment. This research proposes an approach able to account uncertain life cycle sustainability performances through multiple criteria decision analysis (MCDA) process to support decision-making.Design/methodology/approach – Our method is structured in three phases: i) assessing the uncertainty of LCSA performances, ii) propagating LCSA uncertainty into MCDA methods and iii) interpreting the stochastic results. The approach is applied on an illustrative case study, ranking four alternatives to biodiesel supply.Findings –The recommendation generated by this approach provides an information about the confidence the decision maker can have in a given result (ranking of solutions) under the form of a probability, providing a better knowledge of the risk (in this case due to the uncertainty of the preferred solution). As such, stochastic results, if appropriately interpreted, provide a measure of the robustness of the rankings generated by MCDA methods, overcoming the limitation of the overconfidence of deterministic rankings.Originality/value – The fundamental contributions of this paper are to (i) integrate LCSA uncertainty into decision-making processes through MCDA approach; (ii) provide a sensitivity analysis about the MCDA method choice, (iii) support decision-makers’ preference choices through a transparent elicitation process and (iv) provide a practical decision-making platform that accounts simultaneously uncertain LCSA performances with stakeholders’ value judgments.

Shifting to foot-and-mouth disease-free status without vaccination: Application of the PROMETHEE method to assist in the development of a foot-and-mouth national program in Uruguay

Foot-and-mouth disease (FMD) is highly contagious, and the introduction of FMD virus in countries free of the disease can result in large epidemics, similar to those observed in the United Kingdom, Japan, and Uruguay. Many countries or regions of South America are recognized as “FMD-free with vaccination” or “FMD-free without vaccination.” Uruguay has been certified as FMD-free with vaccination, and the transition to the status of FMD-free without vaccination has been discussed among the stakeholders of the Ministry of Livestock Agriculture and Fisheries (MGAP in Spanish). This study illustrates how the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) can identify and rank alternative actions to be used in the national FMD program in Uruguay. It uses multiple-criteria decision analysis (MCDA) to structure the problem comprehensively. This helps to identify critical issues and potential solutions when planning a national FMD program for a country with a disease-free without vaccination status. The analysis highlighted the following perspectives: i) the importance of communication strategies as the country changes to “FMD-free without vaccination” status, as well as potential challenges such as a lack of trust in government; ii) the importance of enhancing epidemiological capacity and data management to allocate resources better and provide feedback from the surveillance system; iii) the need to re-evaluate the animal health compensation strategy to stimulate biosecurity and disease reporting; and iv) the importance of assisting the backyard pig sector given the risks it carries and the complexity of dealing with because of its informality, the educational status of its owners, and the lack of formal assistance by veterinary services. The results suggests that the MCDA approach could be a useful tool for animal health agencies when planning a program to eliminate FMD (as well as other diseases) in a structured way because it helps to identify which alternatives would yield more effective results and it helps to address future challenges.

'It takes two to tango': Bridging the gap between country need and vaccine product innovation.

Despite a growing global commitment to universal health coverage, considerable vaccine coverage and uptake gaps persist in resource-constrained settings. One way of addressing the gaps is by ensuring product innovation is relevant and responsive to the needs of these contexts. Total Systems Effectiveness (TSE) framework has been developed to characterize preferred vaccine attributes from the perspective of country decision-makers to inform research and development (R&D) of products. A proof of concept pilot study took place in Thailand in 2018 to examine the feasibility and usefulness of the TSE approach using a rotavirus hypothetical test-case. The excel-based model used multiple-criteria decision analysis (MCDA) to compare and evaluate five hypothetical rotavirus vaccine products. The model was populated with local data and products were ranked against decision criteria identified by Thai stakeholders. A one-way sensitivity analysis was performed to identify criteria that influenced vaccine ranking. Self-assessment forms were distributed to R&D stakeholders on the usability of the approach and were subsequently analysed. The model identified significant parameters that impacted on MCDA rankings. Self-assessment forms revealed that TSE was perceived as being able to encourage closer collaboration between country decision makers and vaccine developers. The pilot study demonstrates that it is feasible to use an MCDA approach to elicit stakeholder preferences and determine influential parameters to help identify the preferred product characteristics for R&D from the perspective of country decision-makers. It found that TSE can help steer manufacturers to develop products that are better aligned with country need. Findings will guide further development of the TSE concept.

Framework for Drug Formulary Decision Using Multiple-Criteria Decision Analysis.

Background. Reviewing drugs to determine coverage or reimbursement level is a complex process that involves significant time and expertise. Review boards gather evidence from the submission provided, input from clinicians and patients, and results of clinical and economic reviews. This information consists of assessments on multiple criteria that often conflict with one another. Multiple-criteria decision analysis (MCDA) includes methods to address complex decision making problems with conflicting objectives and criteria. We propose an MCDA approach that infers a utility model based on reviews of previously submitted drugs. Methods. We use a recent extension of the UTilitiés Additives DIScriminantes approach, UTADISGMS. This disaggregation approach deconstructs a portfolio of elements such as a set of drugs that have been reviewed and for which a decision has been made. It derives global and marginal utility functions that are consistent with the preferences exhibited by the review boards in their recommendations. We apply the method to oncology drugs reviewed in Canada between 2011 and 2017. We also illustrate how to conduct scenario analyses and predict the coverage decisions for new drugs. Results. Applying the method yields a utility value for each submission along with a set of thresholds that partition the utility values based on the submission outcomes. Scenario analyses illustrate the predictive ability of the method. Conclusion. Preference disaggregation is an indirect way of eliciting an additive global utility value function. It requires less of a cognitive effort from the decision making bodies because it infers preferences from the data rather than relying on direct assessments of model parameters. We illustrate how it can be applied to validate existing decisions and to predict the recommendation of a new drug.

Multiple criteria decision analysis approach to consider therapeutic innovations in the emergency department: The methoxyflurane organizational impact in acute trauma pain.

BackgroundAcute trauma pain is poorly managed in the emergency department (ED). The reasons are partly organizational: ED crowding and rare trauma care pathways contribute to oligoanalgesia. Anticipating the organizational impact of an innovative care procedure might facilitate the decision-making process and help to optimize pain management.MethodsWe used a multiple criteria decision analysis (MCDA) approach to consider the organizational impact of methoxyflurane (self-administered) in the ED, introduced alone or supported by a trauma care pathway. A MCDA experiment was designed for this specific context, 8 experts in emergency trauma care pathways (leading physicians and pharmacists working in French urban tertiary hospitals) were recruited. The study involved four steps: (i) Selection of organizational criteria for evaluating the innovation’s impact; (ii) assessment of the relative weight of each criterion; (iii) choice of appropriate scenarios for exploring the organizational impact of MEOX under various contexts; and (iv) software-assisted simulation based on pairwise comparisons of the scenarios. The final outcome measure was the expected overall organizational impact of methoxyflurane on a 0-to-100 scale (score >50: positive impact).ResultsNine organizational criteria were selected. "Mean length of stay in the ED" was the most weighted. Methoxyflurane alone obtained 59 as a total score, with a putative positive impact for eight criteria, and a neutral effect on one. When a trauma care pathway was introduced concomitantly, the impact of methoxyflurane was greater overall (score: 75) and for each individual criterion.ConclusionsOur model highlighted the putative positive organizational impact of methoxyflurane in the ED—particularly when supported by a trauma care pathway—and the relevance of expert consensus in this particular pharmacoeconomic context. The MCDA approach could be extended to other research fields and healthcare challenges in emergency medicine.

Decision support framework for solid waste management based on sustainability criteria: A case study of tailings pond cover systems

Abstract Tailings are one of the major categories of industrial waste, the disposal of which is problematic from an environmental viewpoint. The weathering of sulfide bearing waste materials commonly generates acidity and dissolved metals in the environment creating acid mine drainage (AMD). Typically, the chosen reclamation solutions emphasize low construction costs over environmental and technical sustainability challenging decision making. The aim of this work was to develop a framework for analysis of alternative cover scenarios with multiple-criteria decision analysis (MCDA) applied for the purpose. The case study comprised totally 10 criteria and five different cover structure options: moraine-based cover, biofuel fly ash/steel slag cover, stabilization with ordinary Portland cement or with composite binder, and use of an advanced hardpan cover liner (AHCL) system. The proposed methodological approach provides the structural steps of MCDA process to compare and contextually evaluate the economic, technical and social-ecological implications of cover alternatives. Modified Analytical Hierarchy Process (AHP) was applied as a MCDA approach for structuring complex problems and assisting decision makers to choose the best alternative among a discrete set of scenarios. Differing preferences of the stakeholders were resolved by synthetizing the conflicting weightings in the beginning of the analysis. Life-Cycle Assessment (LCA) was used to evaluate the social-ecological impacts of emissions, energy demand and impact to human health. Overall, the results from this study show that when considering total sustainability, the AHCL was preferred over the other alternatives when the weighting of the main technical, social-ecological and economic criteria was equal (33%). The weighting of main criteria and consequently sub-criteria impacts the ranking order of compared solutions. Decision making comprising viewpoints of different stakeholders is important when evaluating sustainability criteria. Higher weighting of social-ecological and technical criteria (50%) favored stabilization with composite binder while higher weighting (50%) of costs favored moraine cover. The proposed framework can process competing and conflicting objectives in addition to quantitative and qualitative criteria, hence providing a holistic and interdisciplinary approach to complex problems. The results from this project can help to develop sustainable remediation strategies to reduce the environmental risks associated with mine wastes.

IMPROMPTU: A Reactive and Distributed Resource Consolidation Manager for Clouds

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.

Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA

Pressure from legislation and customers has motivated companies to consider reverse logistics (RL) in their operations. Since it is a complex procedure that requires an adequate system, the recent trend consists in outsourcing RL to third-party reverse logistics providers (3PRLPs). This paper provides the background of sustainable triple bottom line theory with focus on economic, environmental, and social aspects under 3PRL concerns. The relevant sustainability criteria are used in a case study conducted in cooperation with an Indian automotive remanufacturing company. To select the most preferred service provider, we use a hybrid method combining a variant of ELECTRE I accounting for the effect of reinforced preference, the revised Simos procedure, and Stochastic Multi-criteria Acceptability Analysis. The incorporated approach exploits all parameters of an outranking model compatible with the incomplete preference information of the Decision Maker. In particular, it derives the newly defined kernel acceptability and membership indices that can be interpreted as a support given to the selection of either a particular subset of alternatives or a single option. The proposed ELECTRE-based method enriches the spectrum of multiple criteria decision analysis approaches that can be used to effectively approach the problem of the 3PRLP selection. As indicated by the extensive review presented in the paper, this application field was so far dominated by Analytic Hierarchy Process and TOPSIS, whose weaknesses can be overcome by applying the outranking methods.