Application Of Multi-criteria Decision Analysis Research Articles

Rehabilitation of an Industrial Water Main Using Multicriteria Decision Analysis

This paper demonstrates the application of multicriteria decision analysis (MCDA) methodology in a real case study aiming to choose the best rehabilitation intervention of an industrial water main. This methodology is composed of three main stages: problem identification, structuring, and evaluation. Problem structuring, a crucial stage for the outcomes, includes identifying objectives, selecting scenarios within the analysis period, defining problem alternatives, and defining the problematic type. Problem evaluation includes the selection of assessment metrics, selection of the aggregation method, application of the method, and a sensitivity and robustness analysis. Two scenarios, nine assessment metrics, and seven alternatives are established, and two ranking methods (the additive model and ELECTRE III method) are used to compare the alternatives. The results show that the best solution corresponds to building a new pipe and deactivating the existing one, as it significantly reduces the pipe failure risk and O&M costs, whereas the worst solution is always the status quo case, since it is the only alternative that does not improve the system performance, independently of the ranking method and the analyzed scenario.

The determinants of renewable energy sources for the fueling of green and sustainable economy

This study seeks to evaluate potential solutions to Pakistan's energy shortages based on a renewable green hydrogen source provided by geothermal, wind, biomass, and solar energy. To this end, the application of multi-Criteria Decision Analysis (MCDA) and the Fuzzy-analytical hierarchical process was tested on four primary criteria: social acceptance, economic, commercialization, and environmental. The Data Envelopment Analysis (DEA) is used to analyze the development of hydrogen energy using existing renewable sources under the set parameters. Based on results from the fuzzy-led DEA study, the efficiency of wind energy sources is best adapted to produce hydrogen energy for all four criteria in Pakistan. The DEA-led analysis also deems wind energy to be Pakistan's effective source of hydrogen energy. In other words, to produce hydrogen energy, the findings revealed the best optimal rank 1.00 for wind energy, second highest score 0.97 for biomass, third rank for solar energy with a score of 0.75, and geothermal ranked at last position with a score of 0.662. The findings emphasize that the development of wind energy projects will help to fulfill local energy requirements and minimize fossil energy usage. This study can assist policymakers design fact-based initiatives in their particular regions of hydrogen energy.

Choosing the right nature-based solutions to meet diverse urban challenges

Increasing focus on Nature-Based Solutions (NBS) in cities has significantly expanded the range of applications in which urban nature is considered useful, as well as the range of NBS available. Traditional parks, gardens and street trees now sit alongside innovative approaches including rooftop pollinator habitats, constructed wetlands and hydroponic green facades, each of which has its own particular challenges and benefits. This variety of solutions introduces an important new decision-making challenge for cities wishing to implement NBS: choosing the right set of specific NBS interventions.Decision support tools such as Multi-Criteria Decision Analysis (MCDA) can help navigate complex decisions, but their application to urban NBS selection decisions has been limited. Current NBS assessment frameworks tend to either give highly aggregated results, or are tailored to only one specific ecosystem service. Here we demonstrate a novel application of MCDA to the practical challenge of selecting a set of NBS to address multiple urban challenges. The MCDA tool developed here was used in seven cities participating in the European Union project ‘Urban GreenUP’. We describe the development and use of the MCDA tool, and explore how cities used the tool to guide NBS selection. We also evaluate the tool using sensitivity analysis and feedback from users.We find that participating cities are seeking to balance a large number of possible benefits from NBS solutions, and the tool proved useful for navigating the selection decision. Users identified opportunities to improve the tool’s usability and clarity. They also noted a key strength of the tool as a prompt for co-production of knowledge and decisions. Collectively, these findings advance the design and application of tools to support complex decisions about selecting NBS to address diverse urban challenges.

Multicriteria decisions and portfolio analysis: land acquisition for biological and social objectives.

Resource allocation for land acquisition is a common multiobjective problem that involves complex trade-offs. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service currently uses the Targeted Resource Acquisition Comparison Tool (TRACT) to allocate funds from the Migratory Bird Conservation Fund (MBCF; established through the Migratory Bird Hunting and Conservation Act of 1934) for land acquisition based on cost-benefit analysis, regional priority rankings of candidate land parcels available for acquisition, and the overall biological contribution to duck population objectives. However, current policy encourages decision makers to consider societal and economic benefits of lands acquired, in addition to their biological benefits to waterfowl. These decisions about portfolio elements (i.e., individual land parcels) require an analysis of the difficult trade-offs among multiple objectives. In the last decade the application of multicriteria decision analysis (MCDA) methods has been instrumental in aiding decision makers with complex multiobjective decisions. In this study, we present an alternative approach to developing land-acquisition portfolios using MCDA and modern portfolio theory (MPT). We describe the development of a portfolio decision analysis tool using constrained optimization for land-acquisition decisions by the NWRS. We outline the decision framework, describe development of the prototype tool in Microsoft Excel, and test the results of the tool using land parcels submitted as candidates for MBCF funding in 2019. Our results indicate that the constrained optimization outperformed the traditional TRACT method and ad hoc portfolios developed using current NWRS criteria.

Toward a rational and ethical sociotechnical system of autonomous vehicles: A novel application of multi-criteria decision analysis.

The impacts of autonomous vehicles (AV) are widely anticipated to be socially, economically, and ethically significant. A reliable assessment of the harms and benefits of their large-scale deployment requires a multi-disciplinary approach. To that end, we employed Multi-Criteria Decision Analysis to make such an assessment. We obtained opinions from 19 disciplinary experts to assess the significance of 13 potential harms and eight potential benefits that might arise under four deployments schemes. Specifically, we considered: (1) the status quo, i.e., no AVs are deployed; (2) unfettered assimilation, i.e., no regulatory control would be exercised and commercial entities would “push” the development and deployment; (3) regulated introduction, i.e., regulatory control would be applied and either private individuals or commercial fleet operators could own the AVs; and (4) fleets only, i.e., regulatory control would be applied and only commercial fleet operators could own the AVs. Our results suggest that two of these scenarios, (3) and (4), namely regulated privately-owned introduction or fleet ownership or autonomous vehicles would be less likely to cause harm than either the status quo or the unfettered options.

Prioritising earthquake retrofitting in Wellington, New Zealand

This study describes the application of multi-criteria decision analysis (MCDA) to prioritise the seismic risk mitigation of existing buildings in earthquake-prone Wellington, New Zealand. Through retrofitting or demolition, this is an important requirement in many cities around the Pacific Rim and in other high-level seismic hazard locations. The prioritisation strategy proposed here, based on MCDA methods, can provide decision-makers with a fast and reliable support tool for identifying the optimal sequencing of their retrofitting programmes. The premise of the MCDA analysis presented in this paper is that there are multiple criteria that determine societal prioritisation preferences; these are limited not just to life safety (often the explicit/exclusive priority of governments) and commercial value (the main concern of many building owners). The study demonstrates how different measures, within four general criteria-life safety and geo-spatial, economic, and socio-cultural roles-can be operationalised as a viable framework for establishing intervention policy priorities.

Application of multi-criteria decision analysis for investment strategies in the Indian equity market

In the Indian equity market, the Systematic Investment Plan (SIP) is the most popular strategy due to its convenience for disciplined investing regardless of market conditions. This study analyzes the excess returns of an extensive dataset of listed Indian companies from 2010 to 2019, along with a value-based version of the Multi-Criteria Decision Analysis (MCDA), to identify top performing stocks, based on their sectors and market capitalization. The findings of the study provide empirical evidence of Value Averaging (VA) as a viable alternative strategy over SIP (also known as Dollar Cost Averaging or Rupee Cost Averaging) as 352 out of 359 companies yielded higher returns under VA. The superiority of the VA strategy over the SIP was particularly marked in the consumer goods, financial services and industrial manufacturing sectors, with a clear dominance of small cap companies. The results also show that risk factors for VA strategy play an important role and should be taken into account, rather than base investment decisions on excess returns alone. The efficiency scores of individual stocks provide important insights for mutual funds, financial brokers and individual investors in India.

The Application of Multicriteria Decision Analysis Methods in Health Care: A Literature Review.

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.

An Overview of Multi-Criteria Decision Analysis (MCDA) Application in Managing Water-Related Disaster Events: Analyzing 20 Years of Literature for Flood and Drought Events

This paper provides an overview of multi-criteria decision analysis (MCDA) applications in managing water-related disasters (WRD). Although MCDA has been widely used in managing natural disasters, it appears that no literature review has been conducted on the applications of MCDA in the disaster management phases of mitigation, preparedness, response, and recovery. Therefore, this paper fills this gap by providing a bibliometric analysis of MCDA applications in managing flood and drought events. Out of 818 articles retrieved from scientific databases, 149 articles were shortlisted and analyzed using a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach. The results show a significant growth in MCDA applications in the last five years, especially in managing flood events. Most articles focused on the mitigation phase of DMP, while other phases of preparedness, response, and recovery remained understudied. The analytical hierarchy process (AHP) was the most common MCDA technique used, followed by mixed-method techniques and TOPSIS. The article concludes the discussion by identifying a number of opportunities for future research in the use of MCDA for managing water-related disasters.

Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning

AbstractModelling infectious diseases is a complex and multi‐disciplinary problem that necessitates the combined use of multicriteria decision analysis (MCDA) and machine learning (ML) in a spatial framework. This research attempts to demonstrate the extensive applications of MCDA in the field of public health and to illustrate its utility with the combined use of spatial models and machine learning. The study investigates the risk factors for communicable diseases with a focus on vector‐borne infectious diseases, such as West Nile Virus (WNV), malaria, dengue, etc. It aims to quantify vector‐borne disease risk by examining the geographic contextual effects of socio‐economic, climatic, and environmental factors using the objective‐weighting technique adopted from MCDA and machine learning in a geographic information systems (GIS) framework. The authors attempted to minimize subjective bias from the decision space by utilizing an objective‐weighted technique to quantify the risk. The study adopted Shannon's entropy to derive weights for each factor and its classes. The derived weighted layers are fed to an artificial neural network to obtain a final map of risk susceptibility. This final risk map allows policymakers to examine vulnerable areas and identify the factors pivotal to the contribution of risk. Findings show the traffic volume as the most influential variable, and terrain slope as the least one in the disease spread for the study area. The risk appears to be concentrated and distributed along vegetation, wetlands, and around water bodies. The results produced by ensemble learning show great promise with more than 94% accuracy. The accuracy of the results was determined by the confusion matrix and the kappa index of agreement (KIA). The vector control programmes need to adapt to better manage the dynamic changes in patterns involving vector‐borne infectious diseases.

Assessment and design of greener deep eutectic solvents – A multicriteria decision analysis

Deep eutectic solvents (DES) are often considered as green solvents because of their properties, such as negligible vapor pressure, biodegradability, low toxicity or natural origin of their components. Due to the fact that DES are cheaper than ionic liquids, they have gained many applications in a short period of time. However, claims about their greenness sometimes seem to be exaggerated. Especially, bearing in mind lots of data gaps for DES properties as well as their individual components. To clarify the situation on their greenness status, a comprehensive assessment of commonly used hydrogen bond acceptors and donors separately and as DES is performed. The application of multicriteria decision analysis (TOPSIS ranking) with combination of biological effect modeling for DES to rank these alternatives according to greenness criteria is proposed. Also traditional organic solvents and ionic liquids as greenness reference points for better understanding are introduced. The ranking results show that many DES, which are synthetized by mixing sugars alcohols, alcohols, sugars and amides are promising environmentally friendly solvents, more than some imidazolium-based ionic liquids. Mixtures including components with metal ions and organic acids are less green.

Agricultural entrepreneurship fostering from behavioral decision theory perspective. Celebrity branding impact on financial and non-financial motivation

Agricultural entrepreneurship fostering is an important part of the EU and US rural policies. Despite the fact that literature points attention to the issue of limited number of new entrants in agriculture, existing policies are mostly focused on support of existing entrepreneurs. Recent research describes such behavioral reasons of limited number of new entrants as low attractiveness and not appealing image of the agricultural sector. This paper considers the issue of attracting new entrepreneurs to agriculture from behavioral decision theory perspective and suggests celebrity branding as a tool of agricultural entrepreneurship fostering. Through the application of multi-criteria decision analysis, the paper proves that celebrity branding has a significant positive effect on attractiveness of entrepreneurship in agriculture and on expected financial and non-financial benefits from entrepreneurial career in the sector. Also, the paper reveals that financial motivation plays the most important role for individuals attracted to urban spheres of entrepreneurship and is the second most important factor (after self-realization) for individuals attracted to the agricultural sphere of entrepreneurship. The paper also demonstrates a debiasing effect of celebrity branding on the perception of entrepreneurial career in agriculture.

Aplicação do Apoio Multicritério à Decisão no Planejamento Urbano:

Os contextos relacionados a planejamentos e transportes apresentam complexidade para a tomada de decisão, por envolver vários atores e objetivos conflitantes. Neste sentido, foi proposta uma aplicação do Apoio Multicritério à Decisão, na área de planejamento urbano, especificamente, em um estudo para a escolha de pavimentos em uma cidade brasileira de porte médio. Foi desenvolvido em três etapas principais: estruturação do problema, avaliação das alternativas e recomendações. Avaliou-se alternativas em Concreto Asfáltico Usinado à Quente (CAUQ), em Peças Pré-moldadas de Concreto (PPC) e uma terceira em PPC dotada de estrutura para infiltração e armazenamento de águas pluviais. Como resultado, recomendou-se a alternativa em CAUQ. A aplicação do AMD proporcionou uma visão abrangente para a escolha de pavimentos urbanos, embasada no juízo de valores dos atores envolvidos; possibilitou a identificação das potencialidades e as limitações de cada pavimento; identificou valores que determinaram o melhor desempenho global do CAUQ e que se revelaram relevantes para a gestão pública. O modelo desenvolvido pode ser aplicado em outras áreas de transporte e planejamento urbano.

The application of multi-criteria decision analysis to inform in resource allocation

Background: There is a perception held by payers that orphan products are expensive. As a result, the current health technology assessment systems might be too restrictive for orphan drugs, therefore potentially denying patients access to life-saving medicines. While price is important, it should be considered in relation to a broader range of disease-related product attributes that are not necessarily considered by many health technology assessment agencies. To overcome these challenges, multi-criteria decision analysis has been proposed as an alternative to evaluate technologies. Methods: A targeted literature review was conducted to identify the most frequently cited attributes in multi-criteria decision analysis (MCDA) in rare diseases. From the leading attributes identified, we developed a multi-criteria decision analysis framework with which to aggregate the orphan drug values. We subsequently reviewed and plotted the relationship between single attributes and the average annual treatment costs for 8 drugs used in the treatment of rare endocrine diseases. The annual treatment costs were based on UK list prices for the average daily dose per patient. Results: The five most frequently mentioned attributes in the literature were as follows: Disease severity, Unmet need (or availability of therapeutic alternatives), Comparative effectiveness or efficacy, Quality of evidence and Safety & tolerability. Results from the multi-criteria decision analysis framework indicate a wide range of average annual per-patients costs for drugs intended for the same diseases, and likewise for diseases with a similar level of Disease severity. Conclusions: Multi-criteria decision analysis may offer a viable alternative to support discussion in reimbursement decisions for orphan drugs. The analyses can be used to inform investigations on the application of MCDAs in rare diseases.

Argumentation Corrected Context Weighting-Life Cycle Assessment: A Practical Method of Including Stakeholder Perspectives in Multi-Criteria Decision Support for LCA

Despite advances in the data, models, and methods underpinning environmental life cycle assessment (LCA), it remains challenging for practitioners to effectively communicate and interpret results. These shortcomings can bias decisions and hinder public acceptance for planning supported by LCA. This paper introduces a method for interpreting LCA results, the Argumentation Corrected Context Weighting-LCA (ArgCW-LCA), to overcome these barriers. ArgCW-LCA incorporates stakeholder preferences, corrects unjustified disagreements, and allows for the inclusion of non-environmental impacts (e.g., economic, social, etc.) using a novel weighting scheme and the application of multi-criteria decision analysis to provide transparent and context-relevant decision support. We illustrate the utility of the method through two case studies: a hypothetical decision regarding energy production and a real-world decision regarding polyphenol extraction technologies. In each case, we surveyed a relevant stakeholder group on their environmental views and fed their responses into the model to provide decision support that is relevant to their perspective. We found marked differences between results using ArgCW-LCA and results from a conventional analysis using an equal-weighting scheme, as well as differentiation between stakeholder preference groups, indicating the importance of applying the perspective of the particular stakeholder group. For instance, there was a rank reversal of alternatives when comparing between an equal weighting approach for all environmental and economic dimensions and ArgCW-LCA. ArgCW-LCA provides opportunity for both public and private sector incorporation of LCA, such as in developing enlightened stakeholder value measures. This is achieved through enabling the LCA practition to provide public and private actors’ interpreted LCA results in a manner that incorporates educated stakeholder perspectives. Furthermore, the method encourages stakeholder multiplicity through participatory design and policymaking that can enhance public backing of actions that can make society more sustainable.

Environmental sustainability and multifaceted development: multi-criteria decision models with applications

In recent years, decision makers, policy analysts, and other actors, have become increasingly aware of sustainability, and begun to combine economic, social and environmental criteria in their efforts to maintain competitiveness, long-term growth and development. In multiple stakeholder settings, the presence of diverse objectives and conflicting criteria often leads to a complex multi-criteria decision problem. The multi-criteria decision analysis (MCDA) offers an integrated framework to model and study sustainability criteria and related inter-criteria relationships. In this paper, we review some of the most significant literature on environmental sustainability, and categorise it to show how and why MCDA models are widely used and becoming increasingly popular. Our systematic analysis suggests that, there has been significant growth in environmental applications of MCDA in diverse areas, ranging from energy management and policy to land use, recycling management and sustainable tourism. Among the various MCDA methods and techniques, analytical hierarchy process, TOPSIS, and goal programming are the most frequently used approaches. Many authors use a combination of different MCDA techniques to balance various factors important to achieve sustainability related goals. We expect sustainability related criteria to be an essential consideration in most future multi-criteria models.

МНОГОФАКТОРНАЯ ОЦЕНКА КОНКУРЕНТОСПОСОБНОСТИ КОММЕРЧЕСКОГО ЭНЕРГОБЛОКА ТИПА БН В СИСТЕМЕ ЭНЕРГЕТИКИ РОССИИ

In order to ensure the creation of a two-component nuclear power system based on VVER-type and fast neutron reactors (RBN), design and development work is being carried out to increase the competitiveness of RBN with sodium coolant. The paper presents the results of the development and application of multi-criteria decision analysis that combines promising approaches to a comprehensive assessment of technical, economic, resource, environmental, infrastructural and other indicators with a scenario-dynamic consideration of options for constructing a system of energy sources, and takes into account world practices of such assessments. The development potential of nuclear technologies based on fast neutron reactors with sodium coolant has been demonstrated to provide improved technical and economic characteristics, increase the level of safety, and ensure multipurpose use. The possibility of using the calculation and analytical approaches of multi-criteria analysis for a representative comparison of energy technologies and demonstration of the evolution of nuclear technologies is shown.

A decision-support system based on Pythagorean fuzzy VIKOR for occupational risk assessment of a natural gas pipeline construction

Natural gas pipeline construction projects are major projects between countries that include onshore and offshore sections. Aim of these projects is to supply the natural gas from one place to another. Safety of workplace, environment and employers is also another important argument. In this respect, it is essential to conduct an occupational risk assessment (ORA) study to eliminate the risks that may arise around the pipeline. Application of multi-criteria decision analysis (MCDA) methods for the ORA has shown great increase recently. Also, developing a decision support system (DSS) which is easy to use and capable of extracting results enables decision makers to assess health and safety risks. Therefore, (i) A DSS based on Pythagorean fuzzy VIKOR (PFVIKOR) in ORA literature is developed for the first time; (ii) A case study is employed to risk assessment for trenchless crossings installation by auger boring process of the pipeline project; (iii) A sensitivity analysis is performed in the results of PFVIKOR-based DSS; and (iv) Two additional risk parameters called sensitivity to PPE non-utilization and undetectability are considered in ORA process apart from the classical risk assessment parameters of severity and probability. Developing such a DSS and applying it for risk assessment in a natural gas pipeline construction will contribute stakeholders in the energy industry.

Application of multi-criteria decision analysis to assess sampling strategies in eutrophic urbanized waterbodies.

Different water quality sampling practices such as location selection or frequency can inform future watershed management strategies. The objective of this work was to compare water quality sampling strategies based on different weighted criteria to determine the optimal sampling frequency and sampling location for an urbanized, eutrophic, freshwater system. Weekly water sampling was conducted over a 2-year period at five locations for six water quality parameters. This high frequency (HF) dataset was then deconstructed into a lower frequency (LF) dataset to simulate a monthly sampling strategy. Statistical analyses conducted showed that for all sampling locations the LF datasets were not significantly different from the HF datasets, suggesting monthly sampling is sufficient to capture the overall water quality conditions in this system. A multi-criteria decision analysis was constructed for statistical and operational criteria to determine the optimal sampling locations given different criteria weights. Results showed that the optimal sampling location changed depending on the criteria weighting, suggesting that statistical analyses alone would not be sufficient to determine optimal sampling locations in this system. This analysis was then used if optimal sampling location depended on specific water quality monitoring goals. Results showed that the optimal location depends on the particular water quality monitoring goals and that this effect should also be considered in the design of future sampling programs.

Spatial Multi-Criteria Decision Analysis for Rehabilitation Priority Ranking: A Collaborative Application to Heritage Workforce Housing Sites

ABSTRACT This work presents a methodology to rank heritage sites regarding rehabilitation, considering both the characteristics of building sites and of the urban environment in the surrounding area. The objective is to aid the decision process of building rehabilitation by ranking the sites according to their potential for re-emergence in the affordable housing rental market. The developed methodology is based on a combination of multi-criteria decision analysis (MCDA) and spatial analysis of geographical data, in order to construct an index, the “rehabilitation potential”, which is understandable by rehabilitation technicians and land managers and is applicable to support a list of priorities of building rehabilitation interventions. The methodology was applied to a case study consisting of a set of 33 heritage sites of the workforce housing typology in Lisbon. These were built in the early industrial age in Portugal and are owned by the city municipality. The application of MCDA was a collaborative process that brought together the expertise of the academy and of the public administration. The results included a sensitivity analysis and gave form to a recommendation of five sites, selected from the total workforce housing set, to be rehabilitated in the near future.