Stochastic Multicriteria Acceptability Analysis Research Articles

Advancing chemical hazard assessment with decision analysis: A case study on lithium-ion and redox flow batteries used for energy storage

Batteries are important for promoting renewable energy, but, like most engineered products, they contain multiple hazardous materials. The purpose of this study is to evaluate industrial-scale batteries using GreenScreen® for Safer Chemicals, an established chemical hazard assessment (CHA) framework, and to develop a systematic, transparent methodology to quantify the CHA results, harmonize them, and aggregate them into single-value hazard scores, which can facilitate quantitative comparison and a robust evaluation of data gaps, inconsistencies, and uncertainty through the implementation of carefully selected scenarios and stochastic multicriteria acceptability analysis (SMAA). Using multiple authoritative toxicity data sources, six battery products are evaluated: three lithium-ion batteries (lithium iron phosphate, lithium nickel cobalt manganese hydroxide, and lithium manganese oxide), and three redox flow batteries (vanadium redox, zinc-bromine, and all-iron). The CHA results indicate that many materials in these batteries, including reagents and intermediates, inherently exhibit high hazard; therefore, safer materials should be identified and considered in future designs. The scenario analysis and SMAA, combined, provide a quantitative evaluation framework to support the decision-making needed to compare alternative technologies. Thus, this study highlights specific strategies to reduce the use of hazardous materials in complex engineered products before they are widely used in this rapidly-expanding industry sector.

Numerical Calculation Method of Target Damage Effectiveness Evaluation under Uncertain Information of Warhead Fragments

In order to scientifically evaluate the target damage effectiveness when warhead fragments formed by projectile explosion attack targets, this paper proposes a new method by using the multi-layer warhead fragments distribution division mechanism to study an evaluation and calculation model of target damage effectiveness under uncertain information of warhead fragments. On the basis of the spatial relationship between projectile explosion position and target, we first analyze the target damage rules based on the target’s own vulnerability characteristics. Then we use the fuzzy prior probability theory to establish the membership function of the target damage factor and an evaluation model of the target damage grade, and use the stochastic multi-criteria acceptability analysis method to obtain the evaluation index value of the target damage level. Finally, considering the ratio between the effective area of the warhead fragment group attacking target and the area of target itself and the effective number and area ratio of warhead fragments penetrating target, we give a numerical calculation method of target damage effectiveness. Through simulation and calculation, the results show that when the distance between the projectile explosion position and the target is less than 7 m and the coordinate interval of the projectile flight direction is −2.5 m to 2.5 m, the damage probability of warhead fragments to the target is greater than 75%. When the weight of the damage factor of the target changes, the damage effectiveness of the target also changes, and this result is in accordance with the actual experiment, which verifies the rationality of the proposed mathematical model.

A decision-making framework for evaluating environmental tradeoffs in enhancing ecosystem services across complex agricultural landscapes

Decision-making processes to ensure sustainability of complex agro-ecosystems must simultaneously accommodate production goals, environmental soundness, and social relevancy. This means that besides environmental indicators and human activities, stakeholders' perceptions need to be considered in the decision-making process to enable the adoption of mitigation practices. Thus, the decision-making process equates to a multi-criteria and multi-objective problem, requiring additional tools and methods to analyze the possible tradeoffs among decision alternatives based on social acceptability. This study was aimed at establishing a decision support system that integrates hydro-ecologic models and socio-cultural perspectives to identify and assess feasible land management alternatives that can enhance the Kaskaskia River Watershed (KRW) ecosystem services in Illinois (USA). The Soil and Water Assessment Tool (SWAT) was used to simulate the spatio-temporal response of nine environmental predictors to four major management alternatives (crop rotation, cover cropping, reduced tillage, modified fertilizer application) based on stakeholder acceptability and environmental soundness, under 32 distinct climate projections. The stochastic multicriteria acceptability analysis (SMAA) was then applied to classify the management alternatives from the least to the most efficient based on three preference schemes: no preference, expert stakeholders’ preference, and non-expert stakeholders’ preference. Results showed that preference information on watershed ecosystem services is crucial to guide the decision-making process when a broad spectrum of criteria is considered to assess the management alternatives' systemic response. The disparity between expert and non-expert stakeholders' preferences showed different rankings of alternatives across several subcatchments, where the two-year corn one-year soybean rotation scheme was expected to offer the best management alternative to ensure a sustainable agro-production system in the highly cultivated subcatchments of the KRW. In contrast, non-conventional tillage practices were expected to contravene agricultural production, and therefore should be discarded unless combined with complementary measures. This study will enable stakeholders to identify the most suitable management practices to adapt to natural and anthropogenic changes and encourage engagement between government institutions and local communities (multi-stakeholder consensus) to provide a better platform for decision-making.

An [formula omitted] based emergency response plan evaluation with MULTIMOORA method in group decision making

The eruption of COVID-19 at the beginning of 2020 has sounded the alarm, making experts pay more attention to public health emergency events. A suitable emergency response plan plays a vital role in handling emergency events. Therefore, this paper focuses on the evaluation of emergency response plans among a set of group in the comprehensive prospect, and an emergency decision making method integrated with the interval type-2 fuzzy information based on the third generation prospect theory (PT3) and the extended MULTIMOORA method is proposed. Individuals express their preferences using some given linguistic terms set. Furthermore, considering the conflicts may occur in the group, a convergent iterative algorithm is designed for group consensus reaching. Then, the stochastic multi-criteria acceptability analysis (SMAA) method and the Borda Count (BC) method are generated to combine the results instead of the dominance theory in MULTIMOORA system. Finally, based on the background of the COVID-19 pandemic from Wuhan, a case study about the selection of emergency response plan and the corresponding sensitivity and comparative analysis are exhibited to explain the effectiveness of the proposed method.

Choquet-Integral-Based Data Envelopment Analysis with Stochastic Multicriteria Acceptability Analysis

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiencies of decision-making units (DMUs) by using a set of inputs and a set of outputs. However, traditional DEA models always assume that the inputs or outputs are independent of each other, which is unrealistic in practical problems. To reflect the interactions between inputs or outputs, the Choquet integral is employed in DEA models. The traditional DEA models are usually used to find some specific input and output weights of DMUs to optimize the efficiency score of DMUs, but the corresponding input and output weights for the optimal efficiency score of a DMU may not be distributed symmetrically, that is to say, the space of weights may be different for different DMUs. Instead of finding the self-efficiency score and the cross-efficiency score of a DMU in traditional DEA models based on some specific input and output weights, stochastic multicriteria acceptability analysis is used to explore the input or output evaluation space and weight space to calculate the Choquet-integral-based acceptability indices of DMUs. The proposed method considers the interactions between inputs or outputs, which can make more DMUs efficient and can also measure the acceptability of a DMU to become an efficient one by exploring the supporting information space. Examples are given to illustrate the proposed method.

Selection of learning strategies supported on SMAA-M

The present work introduces a systematic decision making process which, based on Stochastic Multicriteria Acceptability Analysis – Matching, is aimed at supporting the selection of pedagogical strategies according to the theoretical paradigms provided by the Color Theory and the Learning Styles concept. This novel procedure is illustrated by an example which allowed comparison with the traditional decision mechanism. The results show that the innovation is valuable for case, since it allows a more tuned-to-reality solution that prioritizes relevant pedagogical strategies and discards insignificant ones. Another underlying advantage of this novel process as compared to the traditional one is the possibility it offers to develop a broader and more detailed analysis, since it provides both the set of pedagogical strategies for a course or group of students and a personalized analysis for each student, thus facilitating the teacher's work.

A Novel Multi-Criteria Decision-Making Framework in Electrical Utilities Based on Gray Number Approach

Given the current trend of reviving the power system, which is considered by competitive markets, the privatization of the power system is forcing them to develop the necessary decision-making policies from a technical and economic point of view to improve their asset management practices. Reliability-centered maintenance is an efficient process to consider these two important aspects, i.e. technical and economic ones, when performing maintenance optimization. This paper proposes a new technique to solve the actual stochastic Multi-Criteria Decision-Making (MCDM) problems with uncertain weight information using a combination of Stochastic Multi-Criteria Acceptability Analysis (SMAA) and Elimination Et Choice Translating Reality (ELECTREIII) methods combined with gray system theory. In maintenance planning, gray system theory is used to determine the specific types of power system components that should receive the most attention. Then, the optimal maintenance strategy of every critical component is determined by recognizing the lowest costs associated with various strategies. The suggested framework demonstrates its relevance and efficacy for actual asset management optimizations in electric power systems, as demonstrated in the IEEE 14-bus test system.

Supporting Decisions by Unleashing Multiple Mindsets Using Pairwise Comparisons Method

Inconsistency in pairwise comparison judgements is often perceived as an unwanted phenomenon and researchers have proposed a number of techniques to either reduce it or to correct it. We take a viewpoint that this inconsistency unleashes different mindsets of the decision maker(s) that should be taken into account when generating recommendations as decision support. With this aim we consider the spanning trees analysis which is a recently emerging idea for use with the pairwise comparison approach that represents the plurality of mindsets (in terms of a plurality of vectors corresponding to different spanning trees). Until now, the multiplicity of the vectors supplied by the spanning trees approach have been amalgamated into a single preference vector, losing the information about the plurality of mindsets. To preserve this information, we propose a novel methodology taking an approach similar to Stochastic Multi-criteria Acceptability Analysis. Considering all the rankings of alternatives corresponding to the different mindsets, our methodology gives the probability that an alternative attains a given ranking position as well as the probability that an alternative is preferred to another one. Since the exponential number of spanning trees makes their enumeration prohibitive, we propose computing approximate probabilities using statistical sampling of the spanning trees. Our approach is also appealing because it can be applied also to incomplete sets of pairwise comparisons. We demonstrate its usefulness with a didactic example as well as with an application to a real-life case of selecting a Telecom backbone infrastructure for rural areas.

Customer satisfaction in the presence of imperfect knowledge of data

AbstractEnsuring customer satisfaction is at the top of any manager's agenda wishing to keep or raise profits. To do so, conducting regular surveys is quintessential. However, provided the large number of questions that typically constitute those surveys, resulting data are plagued with imperfect knowledge. Data from these surveys are typically expressed in ordinal scales. No mathematical operation like addition or multiplication is allowed in these cases. Recognizing the limitations of existing alternatives in the literature to model imperfect knowledge, this work proposes an alternative model for the study of satisfaction and its determinants. Our approach is based on merging the Stochastic Multicriteria Acceptability Analysis and the Multicriteria Satisfaction Analysis (model). We explore the potential of this approach and apply it to an empirical case related to hospitality.

Efficiency evaluation of healthcare services in China based on stochastic multicriteria acceptability analysis and directional distance function

AbstractEvaluating the efficiency of healthcare services accurately can help in analyzing the rationality of inputs and outputs in such services. Considering the consistency and equity of assessment criteria, this study conducts the stochastic multicriteria acceptability analysis (SMAA‐2) with a directional distance function to evaluate the efficiency of healthcare services in 31 provincial administrative regions of mainland China, as observed in 2018. We use SMAA‐DDF to explore all the projection directions to the efficient frontier instead of a certain projection direction. We measure the maximum and average efficiencies for each of the 31 provincial healthcare services. Our empirical findings show that only seven provinces achieve optimal healthcare service efficiency; the eastern area performed the best, followed by the central, western, and northeast areas. Furthermore, the path along the projection directions is provided to help inefficient provinces improve their efficiency and obtain the best possible positions.

Supply chain finance platform evaluation based on acceptability analysis

With the rapid development of financial technology, an increasing number of supply chain finance (SCF) service platforms have sprung up to optimize the flows of financial resources, information and materials in the supply chain. The evaluation and selection of SCF platforms have consequently become critical issues not only for platform customers but also for competitors. Although the practices and challenges of SCF have been analyzed in-depth and several studies have investigated the factors that influence SCF platform performance, research on the evaluation of different SCF platforms is scarce. To comprehensively evaluate different types of SCF platforms, this study first identifies critical factors that influence the performance of SCF platforms. A stochastic multicriteria acceptability analysis for group decision-making (SMAA-2) model is then applied to address the SCF platform evaluation problem. Finally, a questionnaire survey is conducted to validate the accuracy of the proposed evaluation model. The empirical results show that the proposed method provides rankings similar to the survey results and thus is accurate and reliable. The evaluation factors and the rankings of SCF platforms can provide references and decision support not only for financial service providers and platform customers but also for regulatory institutions.

Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria

AbstractAs one of the most popular multiple criteria decision‐making (MCDM) methods, the stochastic multicriteria acceptability analysis (SMAA) is powerful in dealing with the uncertain preference information of decision makers. However, how to find compromise solutions for MCDM problems remains an open question in the SMAA family. A compromise solution refers to an alternative that is the closest to the ideal solution. In this paper, we propose an improved SMAA method, called the SMAA with compromise solutions (SMAA‐CO), to determine compromise solutions for MCDM problems with the uncertain preferences of decision makers and conflicting criteria. The SMAA‐CO uses nonmonotonic additive value functions to depict decision makers’ ideal values for reference points. We apply the proposed method to capture multiple decision makers’ uncertain preference information. An illustrative example concerning supplier selection in the pulp and paper industry is used to show the applicability of the proposed method. Some managerial insights are given.

The stochastic decision making framework for long-term multi-objective energy-water supply-ecology operation in parallel reservoirs system under uncertainties

The long-term multi-objective reservoir operation (LTMORO) involving multiple competitive goals such as energy, water supply and ecology protection is usually implemented under uncertain and changeable environment. Conventional methods usually obtain trade-off solutions and select most preferred one by multi-criteria decision making (MCDM) under deterministic environment. However, these methods are difficult to handle real-world reservoir system where multiple uncertainties exist in criteria performance values (PVs) and weights (CWs). In this paper, framework for solving reservoir operation and MCDM problem under various uncertainties is developed. A novel multi-objective method based on shuffled frog leaping algorithm (SFLA) is proposed to obtain high-quality trade-off solutions. Besides, hybrid utility forms based on TOPSIS, grey correlation analysis (GCA) or other models are impacted by subjectivity of combination coefficient, resulting in extra uncertainties for MCDM. To this end, the stochastic multi-criteria acceptability analysis (SMAA) model is developed by constructing new utility function based on SMAA-2 and modified GCA. The modified GCA helps to offset the uneven distribution problem in conventional version and enhance differentiation of utility function in SMAA-2. Moreover, deterministic CWs are obtained based on minimum deviation principle and two types of stochastic CWs following probability distributions are used to estimate CWs uncertainty. The risk caused by uncertain information propagating from PVs and CWs to decision results is also quantified. Finally, efficiency of established framework is tested by conducting three numerical experiments compared with deterministic GCA and SMAA-2. Results indicate that, compared with deterministic models, the novel framework provides decision maker with more reliable decision support and quantified risk information. The novel SMAA-GCA model produces relatively high probabilities for solutions to obtain their respective ranks compared with that of SMAA-2. It is effective to reduce impact of complex uncertainties on MCDM for LTMORO under stochastic environment.

DEA cross‐efficiency aggregation based on preference structure and acceptability analysis

AbstractAlthough a considerable amount of efforts have been made to overcome the drawbacks of traditional equal aggregation in the Data Envelopment Analysis cross‐efficiency evaluation, the endogenous preference structures in the cross‐efficiency matrix are still largely unexplored. We first untangle both preferential differences denoting the preference degrees among different Decision Making Unit (DMU), and preferential priorities denoting the favorite ranking of the DMUs. A revised cross‐efficiency matrix is formulated. We then apply the stochastic multicriteria acceptability analysis for group decision‐making method to obtain a set of holistic acceptability indices corresponding to different DMUs. Finally, we develop a satisfaction index to evaluate the satisfactory level of the proposed method. Two numerical examples with respect to the Flexible Manufacturing System selection problem and industrial robot selection problem are conducted to validate the proposed methodology. The superiority of our methodology is manifested in terms of an improvement of the overall satisfactory level as 18.69% and 8.48%, respectively.

Energy and environmental efficiency of China’s transportation sectors considering CO2 emission uncertainty

• Energy and environmental efficiency of China’s transportation sector is investigated. • Uncertainty in estimation of CO 2 emission is considered. • Stochastic multicriteria acceptability analysis and data envelopment analysis are used. • Results show that great efficiency disparities exist among regions. • Uneven development has occurred in areas of China’s transportation sectors. China’s transportation sector suffers from energy over-consumption and CO 2 over-emission, resulting in increasing pressure to improve energy and environmental efficiency. Current measurement techniques cannot produce precise CO 2 emission data, and this uncertainty makes previous approaches problematic for analyzing energy and environmental efficiency. This study combines stochastic multicriteria acceptability analysis (SMAA-2) with data envelopment analysis (DEA) to evaluate the energy and environmental efficiency of Chinese transportation sectors in the presence of uncertain CO 2 emission data. The improved SMAA-DEA approach effectively handles CO 2 data uncertainty and also considers all possible input and output weights, thus providing meaningful information (such as maximum efficiency, average efficiency, and rank acceptability index) to guide the development of effective policies to improve efficiency. This study’s empirical findings show that the energy and environmental efficiency of transportation sectors in 30 provincial regions is poor, great efficiency disparities exist between regions, and uneven development has occurred in China.

Application of a Stochastic Multicriteria Acceptability Analysis to support decision-making within a macro-scale energy model: Case study of the electrification of the road European transport sector

Energy system models based on the TIMES framework can explore possible energy futures through the construction of a network of technological processes, to satisfy a set of exogenously imposed service demands at the least-cost system configuration. This work presents a methodology aimed at supporting decision-makers in finding optimal energy-system configuration in a complex energy environment, characterized by uncertain information. First, the EUROfusion TIMES Model is used to assess the sensitivity of different long-term evolutions of the European energy system, with particular reference to the road transport sector, considering different trends for the prescribed future variations of investment cost and vehicle efficiency. In a second step, the Stochastic Multicriteria Acceptability Analysis is used to critically evaluate the alternative optimal configurations proposed by the model simulations under a set of economic and environmental criteria. The results of the analysis allow the discussion of the influence of such criteria on the different alternative cases, complementing the cost minimization approach. In the case study considered here, high penetrations of electric vehicles in the road fleet are favored by economic, environmental and energy-related priorities.

Efficacy and safety in the treatment of hyperprolactinemia: A systematic review and network meta-analysis.

Hyperprolactinemia is a neuroendocrine disease that is responsible for a quarter of cases of secondary amenorrhea, which can lead to infertility in women. Dopaminergic agonists (bromocriptine, cabergoline, quinagolide) can be used in the treatment. However, there is a lack of secondary studies that compare their efficacy and safety, especially through a network meta-analysis. Thus, to contribute to the decision-making, a systematic review and network meta-analyses (NMA) were performed to evaluate the efficacy and safety of dopaminergic agonists in the treatment of hyperprolactinemia. Randomized clinical trials (RCT) were retrieved through PubMed, Web of Science and Scopus databases. The efficacy and safety of the drugs were compared, considering the following outcomes: prolactin (PRL) levels, number of patients with galactorrhoea, menstrual irregularities and adverse drug reactions. NMA was built for each outcome. Results were reported as odds ratios (OR) with 95% credibility intervals. Ranking probabilities were calculated by surface under the cumulative ranking analysis (SUCRA) and Stochastic multicriteria acceptability analysis (SMAA). Seventeen RCTs were included in the systematic review and fifteen in the meta-analyses. The drugs had similar efficacy, considering the PRL levels. The SUCRA analysis showed that quinagolide (0.075 and 0.05mg/day) was superior for reducing irregular menstruation, whereas bromocriptine was the best (97%) for galactorrhoea. Cabergoline proved to be the safest drug, except for abdominal pain at a dose of 1mg/week. The SMAA demonstrated similar results to SUCRA. This is the first network meta-analysis that evaluated the efficacy and safety of dopaminergic agonists in the treatment of hyperprolactinemia. The results of this review revealed that these drugs have similar efficacy, but cabergoline has a better safety profile.

Robust measurement of innovation performances in Europe with a hierarchy of interacting composite indicators

ABSTRACT For a long time, the measurement of innovation has been at the forefront of policymakers' and researchers' agenda worldwide. Therefore, there is an ongoing debate about which indicators should be used to measure innovation. Recent approaches have favoured the use of composite innovation indicators. The European Innovation Scoreboard (EIS) is a relevant tool for benchmarking innovation in Europe. Still, the EIS lacks a proper scheme for weighting the included indicators according to their relative importance. Moreover, despite there is a consensus on the importance of interaction between different dimensions of innovation, there is no composite indicator taking into account of this. We propose an appraisal methodology permitting to take into consideration the interaction of dimensions to be aggregated and robustness concerns related to the elicitation of the weights assigned to the elementary indicators structured in a hierarchical way. With this aim, we apply a multiple-criteria decision-making approach being the conjunction of three methodologies, namely, the multiple-criteria hierarchy process, the Choquet integral and the stochastic multicriteria acceptability analysis. It helps the users to rank and benchmark countries' innovation performance at partial and global level taking into account the importance and interaction of dimensions as well as robustness concerns.

Dimensionality reduced robust ordinal regression applied to life cycle assessment

Life Cycle Assessment quantifies the multi-dimensional impact of goods and services and can be handled by Multi-Criteria Decision Analysis. In Multi-Criteria Decision Analysis, Robust Ordinal Regression manages all the compatible preference functions at once when assessing a set of alternatives and a group of preferences on reference alternatives. Robust Ordinal Regression is thus a versatile method of reducing the cognitive effort required by decision makers for eliciting their preference structures in Life Cycle Assessment, although it does not directly operate on noisy alternatives and requires Stochastic Multicriteria Acceptability Analysis to deal with such scenarios.We propose integrating a dimensionality reduction technique, Principal Component Analysis, and Robust Ordinal Regression methods, to reduce the problem dimensionality and ensure the actual problem features are considered.A generated dataset, a dataset from literature and a Life Cycle Assessment case study are used to test the effectiveness of the proposed methods.

Multi-criteria approach to stochastic and fuzzy uncertainty in the selection of electric vehicles with high social acceptance

In recent years, a number of legal solutions have been adopted in Poland aimed at reaching the number of 1 million electric cars in use in 2025. Therefore, an important research issue is the assessment of electric vehicles available on the Polish market and the identification of vehicles that meet consumers' expectations to the greatest extent. The assessment of electric vehicles is a multi-criteria issue, characterized by a number of uncertainties related to their operational parameters, as well as the preferences of individual users and, more broadly, the preferences of society. A novelty of the article is the application of the fuzzy multi-criteria decision aid method called NEAT F-PROMETHEE (New Easy Approach To Fuzzy PROMETHEE), combined with the Monte Carlo method and elements of the SMAA (Stochastic Multicriteria Acceptability Analysis) method for the assessment of vehicles under uncertainty conditions. The theoretical contribution of this research therefore includes the synthesis of a fuzzy and stochastic approach to decision-making support, supported by outranking and incomparability relationships. Such a set of approaches to uncertainty makes it possible to improve the accuracy of decision-making, because the approaches indicated verify each other's results. Moreover, the application of the NEAT F-PROMETHEE method has solved the problem of evaluating electric vehicles from the perspective of a single decision-maker, while the combination of NEAT F-PROMETHEE and the stochastic approach has made it possible to simulate preferences of the society. Although there are many uncertainties in the decision-making problem, the approach has allowed to identify almost unambiguously the electric vehicle that is likely to gain the highest acceptance. As a result of the conducted research it was found that the approaches to uncertainty based on fuzzy sets, outranking relations and stochastic analysis complement each other, allowing the decision-maker to conduct a wider analysis of the imprecision of the obtained solution.