An extension of the Electre I method for group decision-making under a fuzzy environment

Many real-world decision problems involve conflicting systems of criteria, uncertainty and imprecise information. Some also involve a group of decision makers (DMs) where a reduction of different individual preferences on a given set to a single collective preference is required. Multi-criteria decision analysis (MCDA) is a widely used decision methodology that can improve the quality of group multiple criteria decisions by making the process more explicit, rational and efficient. One family of MCDA models uses what is known as “outranking relations” to rank a set of actions. The Electre method and its derivatives are prominent outranking methods in MCDA. In this study, we propose an alternative fuzzy outranking method by extending the Electre I method to take into account the uncertain, imprecise and linguistic assessments provided by a group of DMs. The contribution of this paper is fivefold: (1) we address the gap in the Electre literature for problems involving conflicting systems of criteria, uncertainty and imprecise information; (2) we extend the Electre I method to take into account the uncertain, imprecise and linguistic assessments; (3) we define outranking relations by pairwise comparisons and use decision graphs to determine which action is preferable, incomparable or indifferent in the fuzzy environment; (4) we show that contrary to the TOPSIS rankings, the Electre approach reveals more useful information including the incomparability among the actions; and (5) we provide a numerical example to elucidate the details of the proposed method.     

Benchmarking of maintenance and outage repair in an electricity distribution company using the value-based DEA method

Benchmarking of electricity distribution utilities has been widely used as a means to contribute for the adoption or reinforcement of enhanced competitiveness and innovation practices to optimize costs, increase customer satisfaction, improve corporate image and maximize profits. The purpose of this paper is to present a benchmarking study for the maintenance and outage repair activity carried out by a Portuguese electricity distribution company, EDP Distribuição (EDP-D), using the value-based DEA method, which builds on links between data envelopment analysis (DEA) and multiple criteria decision analysis (MCDA). This study illustrates the impact of the incorporation of managerial preferences in the classification and ranking of 40 network areas served by EDP-D, confronting the results with a previous study based on a BCC DEA model. In order to deal with the underlying uncertainty, the value-based DEA method for performance evaluation is adapted to include the concept of super-efficiency. Besides identifying best practices, sources of inefficiency, gaps relatively to best practices and opportunities for improvement, this analysis supports the introduction of corrective measures and informs decisions about future goals.     

Supporting the allocation of software development work in distributed teams with multi-criteria decision analysis

In the fast moving software industry, projects have been increasingly developed by distributed teams, which are located in geographically remote offices and collaborate using information communication technologies. In such environments, project distribution presents specific challenges, as work in distributed teams increases project technical complexity, communication lines multiply and stakeholders’ interests may be divergent. Despite the importance and complexity of this type of problem, it seems that there is a lack of reports, in the literature, of systems that could support these decisions. This paper presents a real-world case study, where we developed a multi-criteria model for supporting the distributed team work allocation decision for a major global software company. It was developed with a group of software development project managers, using decision conferencing and multi-attribute value analysis. The model deals not only with software engineering attributes, but also “soft” and strategic issues, like team satisfaction and training opportunities. We also discuss some issues and challenges faced during this modelling process.     

A robust ranking method extending ELECTRE III to hierarchy of interacting criteria,imprecise weights and stochastic analysis

A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all assessment criteria are considered at the same level, however, decision problems encountered in practice often impose a hierarchical structure of criteria. The hierarchy helps to decompose complex decision problems into smaller and manageable subtasks, and thus, it is very attractive for computational efficiency and explanatory purposes. To handle the hierarchy of criteria in MCDA, a methodology called Multiple Criteria Hierarchy Process (MCHP), has been recently proposed. MCHP permits to consider preference relations with respect to a subset of criteria at any level of the hierarchy. Here, we propose to apply MCHP to the ELECTRE III ranking method adapted to handle three types of interaction effects between criteria: mutual-weakening, mutual-strengthening and antagonistic effect. We also involve in MCHP an imprecise elicitation of criteria weights, generalizing a technique called the SRF method. In order to explore the plurality of rankings obtained by the ELECTRE III method for possible sets of criteria weights, we apply the Stochastic Multiobjective Acceptability Analysis (SMAA) that permits to draw robust conclusions in terms of rankings and preference relations at each level of the hierarchy of criteria. The novelty of the whole methodology consists of a joint consideration of hierarchical assessments of alternatives performances on interacting criteria, imprecise criteria weights, and robust analysis of ranking recommendations resulting from ELECTRE III. An example regarding the multiple criteria ranking of some European universities will show how to apply the proposed methodology on a decision problem.     

Understanding multi-methodology: Evaluating the perceived impact of mixing methods for group budgetary decisions

This paper reports on the design and implementation of a multi-methodology intervention intended to support a budget prioritisation decision by a multi-organisational group tasked with tackling the problem of teenage pregnancy in an English borough. The intervention approach involved the combined use of cognitive/causal mapping and a multi-criteria decision analysis method to develop and prioritise a number of projects aimed at alleviating issues associated with teenage pregnancy. The paper describes and discusses the process of developing and applying the intervention approach, and provides an evaluation of its perceived impact by the client group. Drawing on the rich seam of data gathered over the course of our work with the multi-organisational group, we explore the varying degrees of impact that the mapping and multi-criteria evaluation methods achieved during and after the intervention. Analysis of the intervention data suggests that both methods were perceived to facilitate a different way of thinking and learning. In addition, we find that the mapping and multi-criteria evaluation methods successfully addressed participants' democratic and rational concerns. However, despite these reported positive effects, the full recommendations of the intervention were not implemented. An attempt is made to explain this outcome in terms of both the multi-organisational context within which the intervention took place and the nature of the group task. Directions for further research are then proposed.     

动态经济系统分析的经济计量模型与方法

综述可有效阐明动态经济系统长期关系和因果关系的因果测度理论. 首先简要介绍多变量时间序列的协整过程及与此相关的若干概念,并总结了在经济计量学领域评价较高的多变量自回归模型的统计识别方法. 基于多变量时间序列协整过程的向量自回归模型,较详细讨论了多变量时间序列间各种因果测度的定义及其沃尔德检验. 所述单方向因果测度及其统计检验理论作为C1W. J . Granger 非因果性理论的扩张,不仅可以检验两组时间序列间的因果影响存在与否,还可以定量描述影响的程度. 单方向因果测度理论为分析复杂经济系统提     

Heuristic methods for the analysis of managerial problems

Problem analysis is a critical element of managerial problem solving that has not been included in or explicated by traditional stage models of the process. This paper argues that an analysis stage is needed to develop the implications of a problem's definition and to direct the selection and pursuit of solution strategies. Problem analysis is a beuristic activity. The paper explains and applies seven heuristic methods of analysis to a classic management case, generating a richer understanding and broader set of solution alternatives. Strategies for future research on problem analysis are discussed.     

Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain

The food sector has a prodigious focus and is constantly gaining in importance in today’s global economic marketplace. Due to an increasing global population, society faces a greater challenge for sustainable food production, quality, distribution, and food safety in the food supply chain. Adopting green supply chain management (GSCM) elements is essential for utilizing the food supply chain in an environmentally benign way. As a solution to the above challenge, the economic and green characteristics for supplier selection in green purchasing are studied in this paper. For an organization, the evaluation and selection of the green supplier is a vital issue due to several tangible and intangible criteria involved. Accordingly, we apply multiple criteria decision aiding techniques.We propose a hybrid approach that combines the revised Simos procedure, PROMETHEE methods, algorithms for constructing a group compromise ranking, and robustness analysis. At first, the revised Simos procedure is used to derive the criteria weights. Next, the PROMETHEE method is applied to rank the suppliers according to each Decision Maker׳s (DM׳s) preferences. Then, the compromise ranking is constructed to minimize the distance of the individual׳s rankings from the solution adopted by the whole group. For this purpose, we introduce and apply some original procedures based on Binary Linear Programming. Finally, the results are validated against the outcomes of robustness analysis. The applicability and efficiency of the proposed approach is endorsed with a case study in an Indian food industry.     

Multicriteria decision support in local energy planning: An evaluation of alternative scenarios for the Sustainable Energy Action Plan

One of the major challenges for the implementation of local energy planning is the successful development of a Sustainable Energy Action Plan (SEAP) by the local authorities (especially within the framework of their participation to the Covenant of Mayors’ initiative). This aspect constitutes a decision making problem, since the local authorities have to identify the best fields of actions and opportunities for reaching their long-term CO₂ reduction target. However, the already available methods and tools do not offer an integrated framework for the SEAPs’ development and especially the selection of sustainable Renewable Energy Sources (RES) and Rational Use of Energy (RUE) technologies. In this context, the aim of this paper is to present a participatory supportive framework for the implementation of local energy planning. At the first level, the proposed approach incorporates the development of alternative Scenarios of Actions (using knowledge-based process, participatory approach and aspiration level). At the second level, a direct and transparent multicriteria decision support is introduced, in order to evaluate the feasible Scenarios. It includes the application of a multicriteria ordinal regression approach and an extreme ranking analysis method for the estimation of the best and worst possible ranking position of each Scenario. The results from the pilot appraisal of the methodological approach to a “real” problem are presented and discussed. The adopted approach contributes to the selection of the most appropriate combination of RES/RUE actions, supporting in this way the local authorities to the development of their SEAP.     

Measurement and decomposition of the Malmquist productivity index for parallel production systems

The conventional Malmquist productivity index (MPI), which ignores the internal structure of a production system when measuring changes in performance between two periods, may produce misleading results. This paper thus takes the operations of the component processes into account in investigating the MPI of parallel production systems. A relational data envelopment analysis (DEA) model is developed to measure the biennial MPIs of the system and internal processes at the same time, and it is shown that the former is a linear combination of the latter. This decomposition helps identify the processes that cause the decline in performance of the system. An example of 39 branches of a commercial bank, with deposits, sales, and services as the three major functions operating in parallel, is used to illustrate this approach.     

A path analytic study of the effect of top management support for information systems performance

Information systems (IS) have become a vital component of an organization's competitive practices. Organizations have tried to differentiate themselves based on their use and adaptation of new information technology. Top management support (TMS) is a significant factor in influencing the effectiveness of the IS function in an organization. The literature has conceptually supported this notion, but empirical evidence has been sparse. This paper develops a two-tiered framework for studying the relationship between top management support, the IS function, and IS performance. This conceptual model was empirically tested using structural equation modeling based on data collected through a survey instrument. The results support the direct and indirect relationships depicted in the model between top management support and IS performance.     

The Application of Genetic Information for Regulatory Standard Setting Under the Clean Air Act: A Decision-Analytic Approach

In 2002, the U.S. Environmental Protection Agency (EPA) released an “Interim Policy on Genomics,” stating a commitment to developing guidance on the inclusion of genetic information in regulatory decision making. This statement was followed in 2004 by a document exploring the potential implications. Genetic information can play a key role in understanding and quantifying human susceptibility, an essential step in many of the risk assessments used to shape policy. For example, the federal Clean Air Act (CAA) requires EPA to set National Ambient Air Quality Standards (NAAQS) for criteria pollutants at levels to protect even sensitive populations from adverse health effects with an adequate margin of safety. Asthmatics are generally regarded as a sensitive population, yet substantial research gaps in understanding genetic susceptibility and disease have hindered quantitative risk analysis. This case study assesses the potential role of genomic information regarding susceptible populations in the NAAQS process for fine particulate matter (PM_2.5) under the CAA. In this initial assessment, we model the contribution of a single polymorphism to asthma risk and mortality risk; however, multiple polymorphisms and interactions (gene‐gene and gene‐environment) are known to play key roles in the disease process. We show that the impact of new information about susceptibility on estimates of population risk or average risk derived from large epidemiological studies depends on the circumstances. We also suggest that analysis of a single polymorphism, or other risk factor such as health status, may or may not change estimates of individual risk enough to alter a particular regulatory decision, but this depends on specific characteristics of the decision and risk information. We also show how new information about susceptibility in the context of the NAAQS for PM_2.5 could have a large impact on the estimated distribution of individual risk. This would occur if a group were consequently identified (based on genetic and/or disease status), that accounted for a disproportionate share of observed effects. Our results highlight certain conditions under which genetic information is likely to have an impact on risk estimates and the balance of costs and benefits within groups, and highlight critical research needs. As future studies explore more fully the relationship between exposure, genetic makeup, and disease status, the opportunity for genetic information and disease status to play pivotal roles in regulation can only increase.     

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.     

A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit

This article presents an iterative six‐step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.     

Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture

In this paper we consider the use of data envelopment analysis (DEA) for the assessment of efficiency of units whose output profiles exhibit specialisation. An example of this is found in agriculture where a large number of different crops may be produced in a particular region, but only a few farms actually produce each particular crop. Because of the large number of outputs, the use of conventional DEA models in such applications results in a poor efficiency discrimination. We overcome this problem by specifying production trade-offs between different outputs, relying on the methodology of Podinovski (J Oper Res Soc 2004;55:1311–22). The main idea of our approach is to relate various outputs to the production of the main output. We illustrate this methodology by an application of DEA involving agricultural farms in different regions of Turkey. An integral part of this application is the elicitation of expert judgements in order to formulate the required production trade-offs. Their use in DEA models results in a significant improvement of the efficiency discrimination. The proposed methodology should also be of interest to other applications of DEA where units may exhibit specialization, such as applications involving hospitals or bank branches.     

A Framework for Estimating the Adverse Health Effects of Contamination Events in Water Distribution Systems and its Application

Intentional or accidental releases of contaminants into a water distribution system (WDS) have the potential to cause significant adverse health effects among individuals consuming water from the system. A flexible analysis framework is presented here for estimating the magnitude of such potential effects and is applied using network models for 12 actual WDSs of varying sizes. Upper bounds are developed for the magnitude of adverse effects of contamination events in WDSs and evaluated using results from the 12 systems. These bounds can be applied in cases in which little system‐specific information is available. The combination of a detailed, network‐specific approach and a bounding approach allows consequence assessments to be performed for systems for which varying amounts of information are available and addresses important needs of individual utilities as well as regional or national assessments. The approach used in the analysis framework allows contaminant injections at any or all network nodes and uses models that (1) account for contaminant transport in the systems, including contaminant decay, and (2) provide estimates of ingested contaminant doses for the exposed population. The approach can be easily modified as better transport or exposure models become available. The methods presented here provide the ability to quantify or bound potential adverse effects of contamination events for a wide variety of possible contaminants and WDSs, including systems without a network model.     

Understanding the experience of stressors: The use of sequential analysis for exploring the patterns between various work stressors and strain

The availability of traditional self-report instruments for measuring work stressors may have diverted attention from exploring the way in which different stressors relate to one another. In order to develop a better understanding of the nature of the stressor experience a study was undertaken to explore the stressor-strain relationship using sequential tree analysis, a stepwise procedure that provides a “visual display” of the patterns and associations between stressors and strains. The study employed a sample of 695 principals and deputy principal teachers of secondary schools in New Zealand, who received a questionnaire measuring stressors and strains. SPSS AnswerTree® (version 2.0.1) was used to identify the patterns of association. The patterns of stressors that emerge from this analysis were used in a didactic or illustrative way to identify issues of measurement that may need to be resolved in order to derive a better understanding of the stressor experience. Different stressor patterns were associated with different levels of, but lower levels of strain were not simply the obverse of those stressors that cause higher levels of strain. Two not mutually exclusive issues emerge from the results, suggesting that stressor measurement practices may need to be reviewed. The first includes structural level issues such as considering the number, type, and potency of different stressors. The second includes issues best described as conditions of association. These concern understanding why different stressor patterns form, the relationship between stressors in those patterns, and the potency of patterns.