Multi-criteria Decision Analysis Approach Research Articles

Using multi-criteria decision analysis to describe stakeholder preferences for new quality improvement initiatives that could optimise prescribing in England.

Hospital decision-makers have limited resources to implement quality improvement projects. To decide which interventions to take forward, trade-offs must be considered that inevitably turn on stakeholder preferences. The multi-criteria decision analysis (MCDA) approach could make this decision process more transparent. An MCDA was conducted to rank-order four types of interventions that could optimise medication use in England's National Healthcare System (NHS) hospitals, including Computerised Interface, Built Environment, Written Communication, and Face-to-Face Interactions. Initially, a core group of quality improvers (N = 10) was convened to determine criteria that could influence which interventions are taken forward according to the Consolidated Framework for Implementation Research. Next, to determine preference weightings, a preference survey was conducted with a diverse group of quality improvers (N = 356) according to the Potentially All Pairwise Ranking of All Possible Alternatives method. Then, rank orders of four intervention types were calculated according to models with criteria unweighted and weighted according to participant preferences using an additive function. Uncertainty was estimated by probabilistic sensitivity analysis using 1,000 Monte Carlo Simulation iterations. The most important criteria influencing what interventions were preferred was whether they addressed "patient needs" (17.6%)' and their financial "cost (11.5%)". The interventions' total scores (unweighted score out of 30 | weighted out of 100%) were: Computerised Interface (25 | 83.8%), Built Environment (24 | 79.6%), Written Communication (22 | 71.6%), and Face-to-Face (22 | 67.8%). The probabilistic sensitivity analysis revealed that the Computerised Interface would be the most preferred intervention over various degrees of uncertainty. An MCDA was conducted to rank order intervention types that stand to increase medication optimisation across hospitals in England. The top-ranked intervention type was the Computerised Interface. This finding does not imply Computerised Interface interventions are the most effective interventions but suggests that successfully implementing lower-ranked interventions may require more conversations that acknowledge stakeholder concerns.

Resilience and load balancing in Fog networks: A Multi-Criteria Decision Analysis approach

The advent of Cloud Computing enabled the proliferation of Internet-of-Things (IoT) applications for smart environments. However, the distance of these resources makes them unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged to provide such capabilities in proximity to end devices through distributed resources. These limited resources can collaborate to serve distributed IoT application workflows using the concept of stateless micro Fog service replicas, which provides resiliency and maintains service availability in the face of failures. Load balancing supports this collaboration by optimally assigning workloads to appropriate services, i.e., distributing the load among Fog nodes to fairly utilize compute and network resources and minimize execution delays. In this paper, we propose using ELECTRE, a Multi-Criteria Decision Analysis (MCDA) approach, to efficiently balance the load in Fog environments. We considered multiple objectives to make service selection decisions, including compute and network load information. We evaluate our approach in a realistic unbalanced topological setup with heterogeneous workload requirements. To the best of our knowledge, this is the first time ELECTRE-based methods are used to balance the load in Fog environments. Through simulations, we compared the performance of our proposed approach with traditional baseline methods that are commonly used in practice, namely random, Round-Robin, nearest node, and fastest service selection algorithms. In terms of the overall system performance, our approach outperforms these methods with up to 67% improvement.

Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens.

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

EE486 Osteoporosis Treatment Ranking in Postmenopausal Women at High Risk of Fracture in Brazil: MCDA Approach

Osteoporosis is detrimental to a patient´s quality of life, causing high mortality, and financial burden on health systems. Therapeutic alternatives available in Brazil include romosozumab and teriparatide for postmenopausal women at high risk of fracture. This study aimed to rank these treatments using a multi-criteria decision analysis (MCDA) approach to identify the best treatment considering the different preferences for criteria and treatments performance in relation to each criterion.

Ranking Countries According to Logistics and International Trade Efficiencies via REF-III

The interrelation between logistics and international trade is crucial for understanding a country's ability to increase its share in global trade. An adequate and well-integrated logistics sector and infrastructure are required for this purpose. This study employs the novel Multi-Criteria Decision Analysis (MCDA) approach known as REF-III and two distinct models to investigate the activities of countries in terms of infrastructure, logistics, international trade, and economic growth. The results from both models indicate that China and Russia are leading the rankings. However, when focusing on the efficiency of trade and economic growth, the United States occupies the first place. Notably, several Caucasian and Balkan countries rank poorly in both models, possibly due to the multiple crises, wars, and turmoil they have experienced over the past forty years. The investments and improvements made in infrastructure and logistics by the countries excelling in global trade and logistics should serve as a model for other nations to emulate.

Site suitability analysis for locating petroleum stations using analytical hierarchy process (AHP) and MCDA approach: a case of Wolayita Sodo town, Ethiopia, Africa

This study allows the local people as well as planners for the appropriate plans of land use planning in sustainable petroleum station of Wolayita Sodo town, Ethiopia, East Africa. The problem of haphazard location of petrol stations is one of the major difficulties at Wolayita Sodo towns. Due to less scientifically accepted sites and lack of attention to planning standards that has experienced the planning methods are expressed in poor sitting. The main objective of this study is to identify appropriate for locating petroleum stations and assessing existing stations by using Analytical Hierarchy Process (AHP) & Multi-Criteria Decision Analysis (MCDA) techniques. This study demonstrates the use of an integrated Idris 17.1 and Arc GIS 10.3.1 are used for to designate petroleum station site in the study area. MCDA consists of a series of techniques that permit a range of criteria relating to a particular issue to be scored, weighted and then ranked by their degree of suitability or importance for locating a particular facility or service to locate the suitable petroleum station site. The thematic maps of these criteria were prepared and standardized using pair wise comparison matrix. A weight for each criterion was given by comparing them with each other according to their importance, the consistence of comparing matrix were checked by calculating the consistence ratio (CR). The consistency ratio of this study indicated that 0.04 which is less than 0.1. Thus, consistent matrix was formed which was acceptable in order to combine all the mapping layers.

Multi-Criteria Decision Analysis for Evaluating the Effectiveness of Alternative Energy Sources in China

The transition to renewable energy sources is crucial for mitigating the impacts of climate change and achieving sustainable development goals. In China, the rapid industrialization and urbanization have led to an increasing demand for energy, highlighting the urgent need to transition to alternative energy sources. This study aims to evaluate the effectiveness of alternative energy sources in China, considering multiple criteria such as cost, environmental impact, energy output, reliability, and scalability. We employed a Multi-Criteria Decision Analysis (MCDA) approach to compare and rank different energy sources based on these criteria. Our findings indicate that wind energy is the most effective alternative energy source overall due to its relatively low cost, high efficiency, moderate environmental impact, good scalability, and high reliability. However, geothermal energy had the lowest levelized cost of electricity (LCOE), while hydro energy performed well in terms of efficiency and reliability. The environmental impact of wind energy was found to be moderate but still less severe compared to other energy sources. Our study provides important insights into the trade-offs and considerations that policymakers and industry leaders must make when selecting which energy sources to prioritize. The findings highlight the need for a comprehensive and integrated approach to energy policy that balances economic, environmental, and social considerations. In conclusion, this study contributes to the literature by emphasizing the importance of considering multiple criteria when evaluating alternative energy sources. Our findings can inform policy decisions regarding the development of a sustainable and reliable energy mix in China, and have important implications for other countries seeking to transition to renewable energy sources.

Development of a Choice-framework for Covid vaccines in India using a multi-criteria decision analysis approach

BackgroundVaccines were crucial in controlling the Covid-19 pandemic. As more vaccines receive regulatory approval, stakeholders will be faced with several options and must make an appropriate choice for themselves. We proposed a multi-criteria decision analysis (MCDA) framework to guide decision-makers in comparing vaccines for the Indian context. MethodsWe adhered to the ISPOR guidance for the MCDA process. Seven vaccine options were compared under ten criteria. Through three virtual workshops, we obtained opinions and weights from citizens, private-sector hospitals, and public health organisations. Available evidence was rescaled and incorporated into the performance matrix. The final score for each vaccine was calculated for the different groups. We performed different sensitivity analyses to assess the consistency of the rank list. ResultsThe cost, efficacy and operational score of the vaccines had the highest weights among the stakeholders. From the six scenario groups, Janssen had the highest score in four. This was driven by the advantage of having a single dose of vaccination. In the probabilistic sensitivity analysis for the overall group, Covaxin, Janssen, and Sputnik were the first three options. The participants expressed that availability, WHO approvals and safety, among others, would be crucial when considering vaccines. ConclusionsThe MCDA process has not been capitalised on in healthcare decision-making in India and LMICs. Considering the available data and stakeholder preference at the time of the study, Covaxin, Janssen, and Sputnik were preferred options. The choice framework with the dynamic performance matrix is a valuable tool that could be adapted to different population groups and extended based on increasing vaccine options and emerging evidence.*ISPOR – The Professional Society for Health Economics and Outcomes Research.

Computational experiment-aided prescriptive decision-making for complex supply chains: A case of multi-generation smartphone marketing

A supply chain is a complex multivariate evolutionary system that challenges traditional decision-making paradigms and calls for new decision-making frameworks which can comprehensively support multivariate potential emergence prediction and further extend the decision-making from prediction to prescription. To fill these methodological gaps, a computational experiment-aided prescriptive decision-making framework was proposed for the analysis of complex supply chain evolution. The proposed framework enables the exploration of multivariate emergences for solution optimization in a closed-loop interactive iteration between computational experiments and evolutionary multi-objective algorithms. This framework enhances the solution stability behind multivariate emergences and can support Pareto-optimal solution selection based on a multi-criteria decision-analysis approach. A case study of multi-generation smartphone marketing was then conducted to validate the proposed framework and illustrate its applicability. Results indicate that the proposed framework can (i) bridge the complex nonfunctional relationships between impact factors and objectives for marketing prediction; and (ii) exploit the multivariate marketing performances within these relationships to make optimal prescriptions after the closed-loop interactive iterations. This framework enables managers to obtain Pareto-optimal solution prescriptions with improved solution stability surpassing those of traditional prediction.

Insights of Bioeconomy: Biopolymer Evaluation based on Sustainability Criteria

Sustainable development in the agriculture sector can be boosted by integrating a sustainable bioeconomy and transforming renewable resources into added-value products. There are various methods to determine, measure, and compare the extent of sustainability. We promote the bioeconomy concept by utilizing agricultural waste in biopolymers considering the sustainable development in the agriculture sector. This research aims to evaluate biopolymer alternatives based on sustainability criteria and indicators using the integrated multi-criteria decision analysis approach under the sustainability umbrella. The authors evaluated the PLA, PHA/PHB, starch, protein, and cellulose-based biopolymers. As a result, the cellulose-based biopolymer shows the best performance. The research findings provide valuable information to establish a sustainable pathway for biopolymer production for industries.

A GIS-BASED SUSTAINABILITY AND HIGHEST-BEST USE (SHBU) FOR FELDA LANDS DEVELOPMENT PLANNING DECISION-MAKING

FELDA is enforcing a new direction of sustainable FELDA development in the future. It has timely developed a Sustainability and Highest and Best-use (SHBU) framework based on a GIS-based Multicriteria Decision Analysis (MCDA) approach for optimising FELDA lands, particularly crops, and other possible developments. This paper attempts to demonstrate the application of the GIS-based SHBU for FELDA Bukit Rokan discovering its capabilities. It includes generating criterion maps and sub-criterion maps, weighting criterion maps, and creating outcomes – composite maps of cropland suitability and future-physical development. The geospatial analyses for a Bukit Rokan confirmed that the SHBU model could provide the needed information for crop and settlement area developments. The production of the composite crop and physical land development maps significantly helps FELDA management or crop plantation planners optimise lands for crop plantation and future physical development. Hence, its application provides valuable information about the areas and support attributes enhancing FELDA lands development planning decision-making.

Multi-Criteria decision analysis approach for selecting feasible data analytics platforms for precision farming

Cloud computing has become a crucial part of smart farming systems. It offers various services, from data storage to data analytics and visualization. However, selecting a feasible platform is challenging since many factors and criteria need to be considered by decision-makers based on the organization's requirements to select the most optimal cloud solution. This study aimed to provide a systematic approach to selecting cloud computing-based data analytics platforms for precision farming. There are three important stages within the proposed approach: the preparation stage, the integrated model using Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an evaluation framework, and model evaluation. Three cloud computing platforms were evaluated using the proposed model for a novel smart farming project: Amazon Web Services, Google Cloud Platform, and Microsoft Azure. The results show that Google Cloud Platform (S2) is best optimal platform for the smart farming project called smart-in-ag based on the criteria and requirements defined by stakeholders. To validate the consistency and robustness of our proposed model, the sensitivity analysis method was applied to 13 cases. It was demonstrated that the proposed approach is consistent and robust for helping the experts who choose a cloud computing-based data analytics platform in a smart farming project. To the best of our knowledge, this study is the first application for selection the cloud computing platform for a real smart farming project.

A Multi-Criteria Decision Analysis (MCDA) Approach for Landslide Susceptibility Mapping of a Part of Darjeeling District in North-East Himalaya, India

Landslides are the nation’s hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development.

Multicriteria Methodology for Selection of a Personal Electric Vehicle

A methodology that can support the decision process for selecting an Electric Vehicle (EV) is crucial. Considering the quantity, diversity, and complexity of existing EV models, their advantages and disadvantages, and people's increasing concern for sustainability, selecting and buying a personal EV appropriate to one's needs and requirements is not a simple task. Currently, criteria such as price, consumption per 100 km, range, comfort, brand, safety, and technology can be used to analyse available EV models. In this context, the decision-making process can be supported by the Multicriteria Decision Analysis (MCDA), “a method that clearly structures and evaluates complex problems with several alternatives, conflicting criteria, and complex scenarios. The novelty of this paper is to present a hybrid MCDA approach, combining two powerful methods (AHP and PROMETHEE) into a single framework to assist EV selection by any given person who has no multicriteria knowledge. A survey was conducted to define relevant criteria for a private person selecting an EV. The approach was validated for EV selection, considering the available offer within a target value, based solely on available public data, and excluding qualitative criteria. The paper discusses a case study whereby the proposed approach, using both quantitative and qualitative criteria, assists a decision-maker in selecting an EV from a set of alternatives, validated by the decision-maker, proving the importance and relevance of supporting the decision-making process.

English

Communal solar-powered irrigation systems (SPIS) have the potential for sharing the upfront costs hence encouraging farmers to adopt irrigation. Conventional methods of sizing photovoltaic water pumping (PVWP) system for irrigation consider the hydraulic energy requirement by the pump and PV generator capacity separately from the available water source capacity. As a result, the potential of the technology is not optimized, leading to over or under-sizing of the system. Consequently, there is a negative impact on acquisition cost and system performance. The study aimed to determine the optimal PVWP system configuration for communal irrigation. A comparative techno-economic and environmental performance assessment was conducted on different pumping system configurations and a multi-criteria decision analysis approach was used to select the optimal configuration. The findings show a PVWP system with storage tank as the optimal configuration for the communal irrigation. Although the initial capital cost for standalone PVWP system configurations is almost two times that of the conventional diesel pumping systems (CPS), its operation and maintenance (O&M) and lifecycle cost are respectively three times and about four times lower than the CPS cost. Furthermore, the PVWP system for communal irrigation is feasible for irrigation projects that exceed 3 years due to their high acquisition costs. Therefore, promotion of solar-powered irrigation should also focus on the communal approach as a way of improving technology adoption and upscaling. Key words: Communal irrigation system, photovoltaic water pumping system, solar powered irrigation system, design optimization.

Using MCDA to assist an Intermunicipal community develop a resilience strategy in face of the pandemic caused by the SARS-CoV-2

The topic of regional economic resilience has been the subject of intense debate in the academic and political fields over the past decade and gained a new sense of urgency because of the pandemic caused by the SARS-CoV-2 virus as territories faced relevant impacts on their economies and social structures. The economic downturn, the increase in unemployment, and the deterioration of social conditions lead policy makers to search for solutions to make their territories more resilient to this type of event. The current article discusses how multicriteria decision analysis (MCDA) was used to help a Portuguese Intermunicipal Community, formed by 16 councils, develop a strategy to make its territory more cohesive, competitive, sustainable, and resilient. In addition to discussing an innovative application of a MCDA technique, this article illustrates how, through a MCDA approach, it was possible to reach a consensus among several policymakers, despite each of them having their own political agendas.

How Do the Criteria Affect Sustainable Supplier Evaluation? - A Case Study Using Multi-Criteria Decision Analysis Methods in a Fuzzy Environment

Choosing a battery supplier is a vital decision-making problem, for which it is essential to obtain stable evaluations. For such sustainable supplier evaluation, multi-criteria decision analysis (MCDA) methods are often used, as their ability to handle uncertain data gives experts more significant opportunities to consider a broader range of cases. However, given the great number of MCDA approaches, it is challenging to find out which approach is the most appropriate. Therefore, this paper presents a sensitivity analysis on evaluating battery suppliers by ARAS, EDAS, MAIRCA, TOPSIS, and VIKOR methods in a fuzzy environment. The provided study presented similar results for the considered MCDA methods confirmed by the WS similarity measure of rankings and the weighted Spearman correlation . On the other hand, the sensitivity analysis conducted on the considered methods indicated that the most relevant criteria for this problem are transportation cost, delivery time, and warranty period.

Identifying and Evaluating the Socioeconomic Factors of COVID Vaccine Hesitancy Through an Integrated Multicriteria Decision Analysis Approach.

The governments have been facing the challenges to develop tailored strategies to decrease COVID vaccine hesitancy (CVH) and refusal and increase the acceptability and uptake to ensure the protection of public health. In Pakistan, it has been a great challenge for the government to increase the acceptability of the COVID vaccine. CVH has been a great hurdle to achieving this objective. The authors found it imperative to identify and evaluate the factors of CVH in Pakistan. For this purpose, the authors used an integrated multicriteria decision analysis method (MCDM) by combining Delphi and DEMATEL methods. The factors of CVH have been identified and finalized using the Delphi method. The experts' opinions were obtained to evaluate the factors. The DEMATEL method was used to find out the most critical factor(s) of CVH. Moreover, the cause/effect relationship was also developed to have a better understating of factors and their relationships. The analysis revealed "ineffective public awareness strategies" as the most critical factor of CVH followed by "misinformation and disinformation, conspiracy theories", and "acquired knowledge". The study also examined the cause-and-effect relationship between the prioritized factors. The government in Pakistan introduced successful efforts to deal with the COVID-19 pandemic, yet the government could do more to increase vaccine acceptance. For this purpose to serve effective scientific and evidence-based public awareness strategiesare needed to increase the acquired knowledge and deal with misinformation, disinformation, and conspiracy theories to increase vaccine acceptance. The government could sue the media, especially social media to increase vaccination. The findings of the study provide a detailed understanding of the CVH in Pakistan and develop a comprehensive public health strategy to deal with any health-related potential issues in the future.

Flood Hazard Mapping Using a Multi-Criteria Decision Analysis Approach Over the Indrawati River Basin

Floods are devastating natural hazard responsible for direct mortality, deterioration of crops, and damage to infrastructure and property. So, their study is crucial for watershed management and mitigation of flood hazards. The main objective of this study was to create a scientifically valid flood hazard map of the Indrawati River Basin (IRB) through the use of the multi-criteria decision analysis approach. Topographical Wetness Index (TWI), Elevation (EL), Slope (SL), Precipitation (PPT), Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Distance from the River (DRI), Distance from the Road (DRO), Drainage Density (DD), and Soil Type (ST) were chosen as flood triggering factors based on literature review, data availability, and catchment characteristics. The analytic hierarchy process (AHP) method was used for determining the relative weight of each flood causative factor. All these factors were resampled into a 30 m × 30 m pixel size. Based on an evaluation of satellite imagery, 30 flood points were identified in the IRB, and these points were used as the testing dataset for validating the outcome. Based on results, the IRB has been classified into five different flood susceptible zones; very low, low, moderate, high, and very high. According to the study, 13%, 26%, 30%, 23%, and 8% of the total area are in very low, low, moderate, high, and very high flood susceptible zones, respectively. The Area Under the Curve (AUC) value for the success rate was 0.792. The results of this study will be crucial for concerned parties to design early warning systems and flood risk reduction measures for flood preparedness.

Assisted site-dependent selection of the most suitable Airborne Wind Energy system via Fuzzy Analytic Network Process

Over the last decade, the Airborne Wind Energy (AWE) innovation has been evolving with the development of several prototypes by various companies/research institutions. AWE crosswind systems may differ in the electricity generation mode, the wing type, the take-off/landing approaches and the control mechanisms, to name a few. Each system has characteristics which enhance their performance regarding key factors of AWE exploration at the expense of penalizing others. Hence, deciding for the most appropriate system in a possible AWE implementation, targeting a certain location, is a difficult and uncertain scenario. This paper presents a Multi-Criteria Decision Analysis (MCDA) approach, using the Fuzzy Analytic Network Process (FANP), aiming to determine, from nine alternatives, the most suitable AWE crosswind system for two distinct exploration sites: on-shore rural and off-shore. The importance of key selection criteria for AWE is presented and discussed, alternatives in each criterion are evaluated and compared and, finally, a prioritization of both alternatives and criteria is obtained. The FANP methodology indicates rigid wing pumping-cycle systems with horizontal take-off as the most suitable solution for both investigated sites, revealing Aerodynamic Performance, Mass-to-Area Ratio and Controlability as the most relevant factors in the decision.