Preference Modeling to Support Stakeholder Outreach toward the Common Fishery Policy Objectives in the North Mediterranean Sea

The objective of this study is to review applications of multicriteria decision analysis (MCDA) methods to provide structure for and practical insights on how MCDA methods are applied in different healthcare areas. Four databases (Embase, Medline, Web of Science, and PubMed) were searched from 1980 to February 7, 2018. A scoping review was performed to outline the most frequently applied MCDA methods, in which only case studies were considered, as the objective was to assess the applications of MCDA methods. Seventy case studies were selected. The review showed that the first real‐life application was published in 1990. With regard to areas of application, three aspects were analysed: type of health services (public or private), type of intervention (preventive, diagnostic, or treatment), and healthcare area (policymaking; resource allocation; health technology assessment; pharmaceutics; and hospital, clinical, or patient level). Data revealed that MCDA was principally used in public health services (n = 59). Three types of interventions were identified: prevention (n = 36), diagnosis (n = 32), and treatment (n = 2). An MCDA process of application was then followed to provide practical insights. During the problem‐structuring phase, results showed that processes for selecting stakeholders and criteria differed among healthcare areas. For model building, authors preferred value measurement methods and especially the analytical hierarchy process. Authors of the 70 case studies included and confirmed the usefulness of MCDA in different healthcare contexts. The stakeholders, criteria, and MCDA methods chosen differ from one study to another, answering different research objectives in different healthcare areas. This scoping review highlights the importance of each MCDA step (using Belton and Stewart's framework), from problem structuring (including the selection of stakeholders) to the model building phase (encompassing the selection of the appropriate MCDA method). We recommend structured and justified analysis so that decision makers and stakeholders can feel confident during the application process in order to make final decisions more meaningful. Further work is needed to help researchers and decision makers when choosing a suitable MCDA method.

Multi-criteria decision analysis methods for energy sector's sustainability assessment: Robustness analysis through criteria weight change

In recent years, the forest ecosystems of Mediterranean basin countries such as Algeria have experienced massive degradation as a result of fires that continue to destroy forest areas from year to year. Yearly, 36000 ha are burned due to fires, with over 13414 ha burned in 2017. Approximately 29 percent of Algeria's forest cover is located in the western part of the country, where fires continue to be the most threatening factor. With regard to this critical situation, actions must be taken as urgently as possible to fight against this phenomenon. The assessment and the mapping of the Forest Fires Risk (FFR) to highlight vulnerable areas appear to be the most efficient ways. Last years, several models were developed to assess the forest fire risk each of which involves a certain number of parameters which influence differently on the fire spread. The evolution of geospatial techniques such as geographic information systems (GIS) and Multi Criteria Decision Analysis (MCDA) methods have effectively improved solving this type of problem. In this paper we aim to assess the risk of forest fires using GIS-based MCDA model. The approach consists, in the first step, of a spatialization of the FFR parameters by integrating different factors into a GIS. In the second step, the Analytic Hierarchy Process (AHP) MCDA method was used to calculate et assign weights to each factor to produce a FFR potential map. The obtained FFR map shows that 28.87% and 25% of the study area is dominated by Moderate and High FFR. The spatial analysis of the produced map with the actual fires limit reveals a significant correlation through an overall accuracy of 62.60%. This study is carried out in the forest of Nesmoth located in the Mascara province in northwestern Algeria. The obtained results show that the combination of GIS and MCDA methods can be a very useful tool for forest managers and decision-makers in reducing fire risk.

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.

In the contemporary literature on sustainability, one can notice an increasingly frequent use of Multi-Criteria Decision Analysis (MCDA) methods instead of classic sustainability indices. The MCDA method should be tailored to the specific sustainability problem and decision situation so that its results are credible and satisfactory for the decision-maker. Therefore, the following research questions arise: (1) which MCDA methods are most often used in sustainability problems, and (2) which methods should be used depending on the characteristics of a particular sustainability decision problem and its assessment. The aim of the article is to scientifically analyse the applicability of various MCDA methods in decision-making problems related to sustainability, sustainable development, and sustainability assessment. In the article, based on the analysis of the literature, a set of features has been developed that determines the possibility of using individual MCDA methods in sustainability problems. Then, the characteristics of 28 methods are presented and the framework for selecting the MCDA method for the purpose of sustainability decision problems is indicated. As a result of the conducted research, it was found that the most commonly used MCDA methods in sustainability problems are primarily methods based on arithmetic aggregation of criteria. In addition, fuzzy methods and fuzzy modifications of classical methods are used more often. Research has established that MCDA methods are more functional than classic sustainability indices. In addition, the use of MCDA methods in the assessment of sustainability gives much more flexibility than the use of classic indices. The proposed framework allows the decision-maker to independently assess the potential of using individual multi-criteria methods in specific decision-making problems related to sustainability. The framework enables the selection of an appropriate MCDA method depending on the defined needs of the decision-maker, resulting from the decision problem, its structure, and decision-making situation.

The main objectives of this article are 2-fold. First, we explore the application of multicriteria decision analysis (MCDA) methods in different areas of health care, particularly the adoption of various MCDA methods across health care decision making problems. Second, we report on the publication trends on the application of MCDA methods in health care. PubMed was searched for literature from 1960 to 2019 in the English language. A wide range of keywords was used to retrieve relevant studies. The literature search was performed in September 2019. Articles were included only if they have reported an MCDA case in health care. The search yielded 8,318 abstracts, of which 158 fulfilled the inclusion criteria and were considered for further analysis. Hybrid methods are the most widely used methods in health care decision making problems. When it comes to single methods, analytic hierarchy process (AHP) is the most widely used method followed by TOPSIS (technique for order preference by similarity to ideal solution), multiattribute utility theory, goal programming, EVIDEM (evidence and value: impact on decision making), evidential reasoning, discrete choice experiment, and so on. Interestingly, the usage of hybrid methods has been high in recent years. AHP is most widely applied in screening and diagnosing and followed by treatment, medical devices, resource allocation, and so on. Furthermore, treatment, screening and diagnosing, medical devices, and drug development and assessment got more attention in the MCDA context. It is indicated that the application of MCDA methods to health care decision making problem is determined by the nature and complexity of the health care problem. However, guidelines and tools exist that assist in the selection of an MCDA method.

Multi-Criteria Decision-Analysis (MCDA) methods are successfully applied in different fields and disciplines. However, in many studies, the problem of selecting the proper methods and parameters for the decision problems is raised. The paper undertakes an attempt to benchmark selected Multi-Criteria Decision Analysis (MCDA) methods. To achieve that, a set of feasible MCDA methods was identified. Based on reference literature guidelines, a simulation experiment was planned. The formal foundations of the authors’ approach provide a reference set of MCDA methods ( Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Complex Proportional Assessment (COPRAS), and PROMETHEE II: Preference Ranking Organization Method for Enrichment of Evaluations) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). This allowed the generation of a set of models differentiated by the number of attributes and decision variants, as well as similarity research for the obtained rankings sets. As the authors aim to build a complex benchmarking model, additional dimensions were taken into account during the simulation experiments. The aspects of the performed analysis and benchmarking methods include various weighing methods (results obtained using entropy and standard deviation methods) and varied techniques of normalization of MCDA model input data. Comparative analyses showed the detailed influence of values of particular parameters on the final form and a similarity of the final rankings obtained by different MCDA methods.

Human decisions about the design, deployment, and governance of AI systems are increasingly complex, involving multiple desiderata such as accuracy, fairness, transparency, cost, and environmental impact. This paper proposes a novel and scalable framework for managing this complexity through application of resources from the field of Multi-Criteria Decision Analysis (MCDA). After an introduction to the “ n > 2 Criteria Problem” in decision-making about AI, an introduction to MCDA methods, and a review of prior applications of MCDA to decision support in decisions involving AI, we lay out our own novel and scalable framework for applying MCDA to such decisions. We illustrate the framework through application to four simulated scenarios involving a medical institution’s choice among AI-powered diagnostic tools with varying performance on accuracy, fairness, transparency, and cost. We apply five multi-criteria decision analysis (MCDA) methods — namely, the Weighted Sum Model (WSM), the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje), and the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) — to rank ten AI models under four distinct weight configurations reflecting realistic deployment contexts. Sensitivity analyses are then conducted to examine how variations in criterion weight assignments affect model rankings across methods and scenarios. In concluding sections we discuss the scalability of these techniques for design, deployment, and policy decisions involving AI, as well as the likely limitations of MCDA methods for decision support involving AI.

Flooded forests are very important ecosystems that are rich in terms of their diverse flora and fauna. However, they are mostly degraded in many parts of the world, and the remaining fragmented areas are in a critical condition. Forest fires are one of the major environmental disasters that cause serious damage to forest ecosystems, and negatively affect the sustainability of forest resources. In order to minimize the potential effects of fires on forest ecosystems, forest fire risk maps should be generated, and thereby the necessary precautionary measures can be taken in these areas, according to fire risk levels. Geographical information system (GIS) techniques, integrated with multi-criteria decision analysis (MCDA) methods, can be effectively used to develop risk maps for natural hazards, such as forest fires, winter storms, floods, etc. In this study, GIS techniques integrated with an AHP (analytic hierarchy process) method were used to generate a forest fire risk map. The study was implemented in the Karacabey flooded forest, located in the city of Bursa in Turkey. In the solution process, the forest fire risk was evaluated considering two major risk factors, including stand structures (tree species, crown closure, and tree stage) and topographic factors (slope and aspect). The vegetation factor under climate control was considered, instead of directly using data of climatic elements such as temperature and humidity. The results indicated that 25.28% of the forest area was of high fire risk, while 53.17% and 21.55% was of medium and low fire risk, respectively. It was found that the most effective criterion was tree species, followed by tree stage. This aspect had the least effective criterion on forest fire risk. It was revealed that GIS techniques integrated with MCDA methods can be used effectively to estimate forest fire risk zones.

AHP by Saaty and Super Decisions decided on eight criteria for five alternative building materials in Bew Capital Nusantara. It is designed to facilitate decision-making processes using multi-criteria decision analysis (MCDA) methods. MCDA methods are used when decisions involve multiple criteria or factors. Weighting by these two tools has been able to explain to construction actors that for a housing project location which is 99km from Samarinda to IKN by land, about 1,343 km from Surabaya by land, and 74.6 km from Balikpapan, which can be reached by land. Selection through weighting on both tools, AHP and Super Decision, ensuring that a total of 11,880 housing units with a total budget of IDR 45.2 trillion use materials that meet the requirements for cost, easy-to-work-with, performance, and physical properties.

Life cycle tools combined with multi-criteria and participatory methods for agricultural sustainability: Insights from a systematic and critical review

Multi-Criteria Decision Analysis (MCDA) methods are widely used in various fields and disciplines. While most of the research has been focused on the development and improvement of new MCDA methods, relatively limited attention has been paid to their appropriate selection for the given decision problem. Their improper application decreases the quality of recommendations, as different MCDA methods deliver inconsistent results. The current paper presents a methodological and practical framework for selecting suitable MCDA methods for a particular decision situation. A set of 56 available MCDA methods was analysed and, based on that, a hierarchical set of methods' characteristics and the rule base were obtained. This analysis, rules and modelling of the uncertainty in the decision problem description allowed to build a framework supporting the selection of a MCDA method for a given decision-making situation. The practical studies indicate consistency between the methods recommended with the proposed approach and those used by the experts in reference cases. The results of the research also showed that the proposed approach can be used as a general framework for selecting an appropriate MCDA method for a given area of decision support, even in cases of data gaps in the decision-making problem description. The proposed framework was implemented within a web platform available for public use at www.mcda.it.

Life cycle sustainability performance assessment framework for residential modular buildings: Aggregated sustainability indices

For sustainable development of air transport future aircraft designs play a crucial role. New technologies as well as new design strategies could lead to aircraft designs with higher economic efficiency without impact negatively environment and society. Therefore, sustainability aspects have to be addressed well-balanced already in the design process. Currently the main objectives in aircraft design are economic and environmental aspects with focus on block fuel, operating costs, noise, and emissions. This paper deals with the question, how overall sustainability can be measured and an indicator set for aircraft design purposes is defined. For sustainability assessment of aircraft designs at conceptual design stage quantification models for each indicator are presented. All models are integrated into the Multidisciplinary Integrated Conceptual Aircraft Design and Optimization (MICADO) environment of the Institute of Aerospace Systems (ILR) of RWTH Aachen University. Furthermore, for the use of multiple sustainability criteria two multi criteria decision analysis (MCDA) methods have been selected and integrated. This design and assessment framework enables sustainability-driven aircraft design already at conceptual design stage. Two case studies are presented to show the applicability of the framework. For a designed aircraft a parameter study varying Mach number and flight level is performed and the pareto optimal operating points for the optimization criteria climate change and operating costs are determined. The results show possibilities how to operate existing aircraft designs for lower climate impact without economic disadvantages. To show the impact on overall aircraft level, the same parameter study is performed for the design mission within the design loop. The aircraft designs optimal for single sustainability indicators and, using MCDA methods, for overall sustainability are presented and discussed.

Multicriteria decision analysis (MCDA) problems (also known as multicriteria decision-making or MCDM) involve the ranking of a finite set of alternatives in terms of a finite number of decision criteria. Often times such criteria may be in conflict with each other. That is, an MCDA problem may involve both benefit and cost criteria at the same time. Although this is a frequent characteristic of many real-life MCDA problems, this subject has not received adequate attention in the literature. This paper examines the use of four key MCDA methods when two approaches for dealing with conflicting criteria are used. The two approaches are the benefit to cost ratio approach and the benefit minus cost approach. The MCDA methods used in this study are the weighted sum model, the weighted product model, and the analytic hierarchy process (AHP) along with some of its variants, including the multiplicative AHP. Not surprisingly, these two approaches for aggregating conflicting criteria may result in a different indication of the best alternative or ranking of all alternatives when they are used on the same problem. As it is demonstrated here, it is also possible for the two approaches to even result in the opposite ranking of the alternatives. An extensive empirical analysis of this methodological problem revealed that the previous phenomena might occur frequently on simulated MCDA problems. The WSM, the AHP, and the revised AHP performed in an almost identical manner in these tests. The contradiction rates in these tests were rather significant and became more dramatic when the number of alternatives was high. Although it may not be possible to know which ranking is the "correct" one, this study also theoretically proved that the multiplicative AHP is immune to these ranking inconsistencies.