Multi-criteria Decision Analysis Approach Research Articles

Assessment of priorities in critical material recovery from Waste Electrical and Electronic Equipment

A major problem in developing environmental management systems for assessing recovery priorities for high added-value materials such as critical metals from wastes is that the input data required are contained in a variety of databases compiled for different purposes with different levels of content and accuracy. To deal with decision-making priorities for environmental issues where uncertainties and inconsistencies are inherent in input data sources a Multi-Criteria Decision Analysis (MCDA) approach using an Analytic Hierarchy Process (AHP) to determine weights and fuzzy numbers to account for input data uncertainties is developed and is illustrated for determination of critical metal recovery priorities from Waste Electrical and Electronic Equipment (WEEE) in Europe. A spreadsheet programme with many user-friendly input and output features is developed to aid data-handling and the calculations required in the MCDA approach. Where data uncertainties exist, weighted input parameters for the critical metals are determined by an absolute judgement panel of key stakeholders and include data on: recovery potential, geographical supply concentration and stability, end use substitution, future metal demand and economic importance in key industrial sectors. Sensitivity analyses can be carried out on results of recovery priority outputs by simple numerical variation of input parameters and/or by the use of specialist input decision panels representing stakeholder groups with differing priorities, and the results suggest that recovery priorities in the European situation should concentrate on gallium, indium and germanium. The spreadsheet-MCDA program set up in developing the methodology for criticality has wide general application in environmental decision-making analysis particularly where uncertainties exist in the input data required.

HT2 APPLYING MULTI CRITERIA DECISION ANALYSIS TO GUIDE HEALTHCARE DECISION MAKING IN A DEVELOPING COUNTRY

The Multi-criteria decision analysis (MCDA) approach has been promoted as a mean to support systematic consideration of a broad range of decision criteria when appraising healthcare interventions. While it is important to capture all relevant aspects of value, these may not be fully transferable without considering country-specific aspects. The objective of this exploratory analysis is to discuss which criteria would be relevant in value determination for a pharmaceutical product in the Lebanese context.

PRO41 A MULTI-CRITERIA DECISION ANALYSIS CONTRIBUTION TO THE CANADIAN INTRAVENOUS IMMUNOGLOBULIN COST-EFFECTIVENESS ANALYSIS

To enhance the intravenous immunoglobulin (IVIg) cost-effectiveness analysis (CEA) for idiopathic thrombocytopenic purpura (ITP) treatment, in order to generate a ranking through a multi-criteria decision analysis (MCDA) approach.

The Potential Role of Ammonia as Marine Fuel—Based on Energy Systems Modeling and Multi-Criteria Decision Analysis

To reduce the climate impact of shipping, the introduction of alternative fuels is required. There is a range of different marine fuel options but ammonia, a potential zero carbon fuel, has recently received a lot of attention. The purpose of this paper is to assess the prospects for ammonia as a future fuel for the shipping sector in relation to other marine fuels. The assessment is based on a synthesis of knowledge in combination with: (i) energy systems modeling including the cost-effectiveness of ammonia as marine fuel in relation to other fuels for reaching global climate targets; and (ii) a multi-criteria decision analysis (MCDA) approach ranking marine fuel options while considering estimated fuel performance and the importance of criteria based on maritime stakeholder preferences. In the long-term and to reach global GHG reduction, the energy systems modeled indicate that the use of hydrogen represents a more cost-effective marine fuel option than ammonia. However, in the MCDA covering more aspects, we find that ammonia may be almost as interesting for shipping related stakeholders as hydrogen and various biomass-based fuels. Ammonia may to some extent be an interesting future marine fuel option, but many issues remain to be solved before large-scale introduction.

Application of catastrophe theory to spatial analysis of groundwater potential in a sub-humid tropical region: a hybrid approach

Geospatial techniques and Multi-Criteria Decision Analysis (MCDA) play a crucial role in the planning and management of land and water resources. GIS-based MCDA technique ‘Catastrophe theory’ has been recently proposed for evaluating groundwater potential. However, the major limitation of ‘Catastrophe theory’ is that only quantitative factors/thematic layers can be used for assessing groundwater potential, though qualitative factors are equally important. To overcome this inherent limitation, a novel GIS-based MCDA approach named ‘Hybrid Catastrophe’ technique is proposed in this study. The ‘Hybrid Catastrophe’ technique integrates the original ‘Catastrophe theory’ with the ‘Analytic Hierarchy Process (AHP)’ to take into account both qualitative and quantitative thematic layers for assessing groundwater potential, thereby improving the reliability and versatility of the original Catastrophe technique. The applicability of ‘Hybrid Catastrophe’ technique is demonstrated through a case study wherein 8 influential thematic layers (both quantitative and qualitative) were considered for assessing groundwater potential. The four quantitative layers were assigned weights based on the ‘Catastrophe theory’ and the remaining four qualitative layers were assigned weights based on the ‘AHP theory’. These thematic layers were integrated in GIS to delineate groundwater potential zones. The ‘Hybrid Catastrophe’ technique yields four groundwater potential zones in the study area: (i) ‘very good’ (covering 16% of the study area), (ii) ‘good’ (54%), (iii) ‘moderate’ (29%) and (iv) ‘poor’ (1%) and its accuracy was found to be 77% that is reasonably high. The proposed ‘Hybrid Catastrophe’ technique is versatile and it can be successfully applied to other parts of the world for evaluating groundwater potential at diverse spatial scales irrespective of agro-climatic, hydrologic and hydrogeologic conditions.

A fuzzy multi-criteria decision analysis approach for risk evaluation in healthcare logistics outsourcing: Case of Morocco.

Public-private partnerships in emerging countries are gradually considered a tool for growth development. One particular public-private partnership is that of outsourcing logistics in the healthcare sector. These partnerships provide several benefits for both private and public sectors but are generally associated with numerous risks that must be evaluated and subsequently managed. The aim in this paper is to develop a risk evaluation approach to rank risks when decision makers' judgment is taken into consideration and where traditional risk evaluation techniques become less applicable. The proposed approach makes use of fuzzy multi-criteria decision analysis methods since they are efficient techniques to rank alternatives based on selected evaluation criteria and can accommodate human judgment and preferences. The approach treats predetermined risks like alternatives, and evaluation criteria are represented by areas of impact of the risks. To validate the approach, we applied it to a real case of logistics outsourcing to private service providers in a Moroccan public pharmaceutical supply chain. The ranking results obtained show that the two methods lead to some differences in ranking but are highly correlated. The obtained results support healthcare policy makers in setting priorities to effectively deploy preventive and mitigation efforts.

Risk Mapping of Influenza D Virus Occurrence in Ruminants and Swine in Togo Using a Spatial Multicriteria Decision Analysis Approach.

Influenza D virus (IDV) has been identified in several continents, with serological evidence for the virus in Africa. In order to improve the sensitivity and cost–benefit of IDV surveillance in Togo, risk maps were drawn using a spatial multicriteria decision analysis (MCDA) and experts’ opinion to evaluate the relevance of sampling areas used so far. Areas at highest risk of IDV occurrence were the main cattle markets. The maps were evaluated with previous field surveillance data collected in Togo between 2017 and 2019: 1216 sera from cattle, small ruminants, and swine were screened for antibodies to IDV by hemagglutination inhibition (HI) assays. While further samples collections are needed to validate the maps, the risk maps resulting from the spatial MCDA approach generated here highlight several priority areas for IDV circulation assessment.

A Geospatial Framework Using Multicriteria Decision Analysis for Strategic Placement of Reserve Generators in Puerto Rico

Extreme events such as hurricanes cause damages to various infrastructures, including power supply and transportation networks. Restoration of power supply services after extreme events is crucial for disaster response and recovery activities. With damages to transportation and other supply chain networks following extreme events, energy utility companies face significant challenges in achieving faster restoration of power supply services. This was the case for the utility companies in Puerto Rico after Hurricane Maria in 2017. One option for the utility companies in such circumstances will be to temporarily connect impacted populations to reserve generators, strategically located before the event, for quicker restoration of services. The objectives of this article are to: 1) develop a geospatial framework using a multicriteria decision analysis (MCDA) approach for placement of reserve generators and 2) identify strategic locations using 12 criteria representing physical, socioeconomic, environmental, and built environment conditions in Puerto Rico for the placement of reserve generators. Five different approaches are used to determine weights for the 12 criteria used in the MCDA approach. The geospatial framework developed in this article is comprehensive, which along with the weight determination approaches could be adapted to identify potential sites for the placement of additional energy infrastructures, including transformers, mobile stations, and microgrids, to power a city during extreme events.

Spatial Modelling of Migration Decision-Selectivity in the Klang Valley Region, Malaysia

In the era of global urbanization, migration phenomena—particularly urban-to-urban migration—is one of the key drivers of urban development. This has led to a higher demand for spatial-social and spatial-economic activities, tremendously spurring the changes in demographic, size, and urban socio-economics. However, many researchers agree that this phenomenon is quite difficult to understand, particularly destination choices, due to complexity in migration behavioral factors. Inspired by this, this article attempts to demonstrate the capability of the ‘MGP model', the so-called ‘migration potential model'—a GIS-based multicriteria decision analysis approach, which incorporates migration decision-selectivity factors—for spatial migration modelling in the Klang Valley, Malaysia. Empirically tested, the MGP model, intended to be an advanced approach in migration analysis, GIS, and MCDA applications that provides urban planners with information about the areas that will be the centers of potential migration, will look at locations and expected levels. Finally, this article concludes with a discussion on the advantages of the MGP model, particularly for urban planning purposes and suggestions for further research.

A multi-criteria support for the cost-effectiveness analysis of health interventions/ Uma contribuição multicritério para a análise de custo-efetividade de medicamentos

The use of multi-criteria decision analysis (MCDA) in the economic evaluation of drugs has been widely discussed. Thus, this study aims to propose a way to enhance the cost-effectiveness analysis (CEA) of health interventions using the PROMETHEE II multi-criteria method. In order to accomplish this, PROMETHEE II was applied to the same data used in the CEA of lipegfilgrastim in the Brazilian healthcare system for the reduction of chemotherapy-induced neutropenia. In this application, lipegfilgrastim and its comparators formed a set of alternatives. The cost parameters used to compute the total costs of the treatments along with the effectiveness outcomes formed a set of criteria, showing that a conventional CEA is in an MCDA setting. The CEA demonstrated lipegfilgrastim as a cost-saving strategy over its comparators. PROMETHEE II generated the following outcomes to contribute to the CEA: (1) a ranking of studied interventions; (2) a classification level for each intervention from A (best level) to E (worst level); (3) a positioning scenario showing the dominance relation and distance among the interventions; and (4) a criteria panel classifying each criterion as strong or weak point for each intervention when compared to the others. The MCDA approach presented in this study does not exclude the use of CEA but complements it to support the decision-making process.

A multi-criteria decision analysis approach towards efficient rainwater harvesting

Rainwater Harvesting (RWH) is becoming one of the most promising alternative sources of fresh water which can be potentially captured and utilized, particularly in arid and semi-arid regions (ASARs). However, identification of potential sites for RWH on a large scale has always been a great challenge, and therefore, requires further studies. Most recent studies on this topic select RWH zones employing Geographic Information Systems (GIS), combined with multi-criteria analysis and hydrological models. Nevertheless, there are a few studies, mostly in small catchments, that locate potential zones considering socio-economic factors. In this paper, a GIS-based Multi-Criteria Decision Analysis (MCDA) is presented to identify potential RWH areas incorporating socio-economic factors including distance from drainage networks, roads, urban areas, faults, farms and wells. The proposed methodology was implemented at a large catchment scale in Mashhad Plain Basin with an area of 9762 (km2) located in the northeast of Iran. Results show that 48% of the study area is not suitable for RWH. The rest of the study area is divided into three potential zones that are poor, moderate and good for RWH, covering 31%, 43%, and 26% of the potential areas, respectively. Ponds and pans, check dams and terracing were considered as the best practices for the good RWH potential areas. Preliminary estimates suggest that up to 158, 135 and 110 M m3 of precipitation could be received respectively by each system.

Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran

Groundwater is one of the most important natural resources, as it regulates the earth’s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use/land cover (LU/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.

The value of obinutuzumab for untreated advanced Follicular Lymphoma: an assessment based on Multicriteria Decision Analysis (MCDA)

Introduction: Multicriteria Decision Analysis (MCDA) provides a framework that enhances transparency and repeatability of decisions taken on a multicriteria basis. Objective: This analysis aims at assessing obinutuzumab compared to rituximab used as a first-line treatment for Follicular Lymphoma (FL) in the Italian health care system, using an MCDA approach. Materials and Methods: We used the EVIDEM V10 MCDA framework and a Delphi approach to scrutinize the views of a panel of physicians, payers and patients on value domains and their application to our research target. Results: All stakeholders attached medium-high scores to FL severity (patients at higher risk of relapsing), unmet needs, obinutuzumab clinical benefit and evidence quality, and lower scores to organizational impact and, except for payers, to costs. The comparative analysis highlighted positive scores for the domains “incremental efficacy” (2.6: range −5/+5) and “incremental patient benefit” (1.5: range −5/+5) of obinutuzumab compared to rituximab. A slight increase of severe adverse events (≥3) for obinutuzumab was estimated by the panellists. Obinutuzumab compared to rituximab received a neutral evaluation for costs and for organizational impact. Conclusion: This study reveals that MCDA could be a useful framework for evaluating a drug and it can be used to elicit the views of different stakeholder groups (as patients). The key criteria driving the value of obinutuzumab.

PNS265 MULTI-CRITERIA DECISION ANALYSIS AS A SUPPORTIVE TOOL FOR EFFICIENCY MEASURING IN HEALTH CARE

Research is performed in order to assess the applicability of a multi-criteria decision analysis (MCDA) approach for additional evaluation of the health care system efficiency. To achieve the objectives of this research, the modified Delphi method was used as an expert method, inviting experts of medical branches and representatives of patients’ organisations. The questionnaire contained five questions, each of them provided initial information and versions of criteria, as well as provided the experts with opportunity to make the additions, and to propose the weights of scores. For data processing and analysis, the methods of economic analysis and statistical analysis are embraced. The health care system can be defined as a multi-level open system. The authors created a three-level performance evaluation model for assessment of the efficiency of a health care organisation, based on macro-level evaluation (policy impact assessment), meso-level evaluation (system analysis, functional and economic efficiency measuring) and micro-level evaluation (production function, allocative and technical efficiency measures, market analysis). For each of the levels experts provided the weighted scores of criteria which can improve the efficiency measuring of the health care system performance. Taking into account the circumstances of limited state budget resources for health care, the MCDA provides additional opportunities to setting the priorities, based, for instance, on evaluation of the budget allocated diseases’ impact on life expectancy, ability to work, and quality of life. According to the experts’ judgments the group of diagnoses “Neoplasms” gives the greatest common impact on life expectancy, ability to work, and quality of life, followed by the “Diseases of the circulatory system” and “Diseases of the blood and blood forming organs and certain disorders involving the immune mechanisms”. MCDA approach can be used for setting priorities and additional evaluation of the health care system efficiency at each level of its performance.

Evaluating and Prioritizing the Green Supply Chain Management Practices in Pakistan: Based on Delphi and Fuzzy AHP Approach

Nowadays, green supply chain management (SCM) practices are increasing among firms to adopt green practices and reduce the negative effects of supply chain operations on the environment. Firms such as manufacturing, mining, and agriculture have to improve their capacity in green SCM practices because environmental regulations force them to consider these issues. However, green practices are new and require comprehensive study to determine this problem. This study has taken the case of three garment manufacturing firms for the evaluation of green SCM practices in the context of Pakistan. The green SCM requires multi-dimensional techniques; therefore, fuzzy-based multi-criteria decision analysis approaches must be adopted while assessing green SCM practices of firms. This is because fuzzy-based methods obtain a significant solution for complex, vague, and uncertain multi-attribute problems in fuzzy environment. Therefore, in this study, a hybrid decision model comprised of Delphi, and Fuzzy Analytical Hierarchy Process (AHP) methodologies is proposed for assessing the green SCM practices of firms in terms of green design, green purchasing, green production, green warehousing, green logistics, and reverse logistics. The Fuzzy AHP method results reveal that “green purchasing,” “green design,” and “green production” are ranked the most important green indicators. Further, results reveal the ranking of manufacturing firms (alternatives) in the context of green SCM practices. This study shall help industries to focus on green SCM practices and adopt the green manufacturing process.

Prioritization of livestock transboundary diseases in Belgium using a multicriteria decision analysis tool based on drivers of emergence.

During the past decade, livestock diseases have (re‐)emerged in areas where they had been previously eradicated or never been recorded before. Drivers (i.e. factors of (re‐)emergence) have been identified. Livestock diseases spread irrespective of borders, and therefore, reliable methods are required to help decision‐makers to identify potential threats and try stopping their (re‐)emergence. Ranking methods and multicriteria approaches are cost‐effective tools for such purpose and were applied to prioritize a list of selected diseases (N = 29 including 6 zoonoses) based on the opinion of 62 experts in accordance with 50 drivers‐related criteria. Diseases appearing in the upper ranking were porcine epidemic diarrhoea, foot‐and‐mouth disease, low pathogenic avian influenza, African horse sickness and highly pathogenic avian influenza. The tool proposed uses a multicriteria decision analysis approach to prioritize pathogens according to drivers and can be applied to other countries or diseases.

Bikeway prioritization to increase bicycle network connectivity and bicycle-transit connection: A multi-criteria decision analysis approach

Due to limited resources such as budget and land space, it remains challenging to identify key prior corridors on which investments on bikeways (e.g., traditional and protected bike lanes) are most valuable to increasing low-stress bike network connectivity and bicycle-transit connection. In built-up urban areas, it’s usually difficult to find the extra space for building bikeways, and vehicle delays could be worse off if there is no road widening. Facing these challenges, the paper introduces a systematic planning analytics aiming to increase bicycle connectivity and bicycle-transit connection with minimized impacts on motor vehicles and cost. The stakeholders concerned include bicyclists, transit users, motor vehicle drivers, and investors. To reduce conflict potentials due to multiple factors involved in decision making choices between stakeholders, a Multi-Criteria Decision Analysis (MCDA) technique is applied to compare alternatives based on a set of performance evaluation criteria. Performance criteria include bicycling connectivity between origins and destinations, bike share connectivity to destinations, bicycle-transit service coverage population, bike share connectivity to transit, automobile traffic delay, and capital costs. Criteria weights are determined by pair-wise comparisons in which the conflicts between stakeholders and trade-offs among criteria are considered in a quantity-based method. A final preference score calculated in the sum of the weighted and normalized performance measurements is used to rank alternatives. The approach is applied through a case study in the Uptown Cincinnati, Ohio to determine the priorities of the proposed new bikeways in the area. The results indicate that the protected bike lanes exhibit a more significant advantage over bike lanes in terms of the benefits for bicyclists and transit users. At the same time, however, higher construction cost and countermeasures to reduce the potential of consequent traffic congestion need to be need carefully considered.

Urban environment dynamics and low carbon society: Multi-criteria decision analysis modeling for policy makers

The aim of achieving stable economic growth and development deter the pathways of being a low carbon society in developing countries. Nonetheless, it is imperative to understand and analyze the long-term goals of such societies to upgrade and redirect priorities towards the low-carbon structure. Therefore, this study is carried by applying a novel multi-criteria decision analysis (MCDA) approach and the Bilan Carbone model to examine the different possibilities for cost/benefit analysis from direct reductions in carbon emission levels in the Lahore Metropolitan Area (LMA) of Pakistan. The LMA is a metropolitan city in the developing country and is marked with a huge spike in energy demands and carbon emissions due to increasing population. Three scenarios including one current (2010), and two future (business as usual—2050BaU and low carbon scenario—2050LCS), were developed. Our findings show no evidence of renewable energy use—such as solar, wind, and biomass—in the current energy mix of LMA. Furthermore, an increase in carbon dioxide (CO2) emissions from 3.5 in 2010 to 14 million tonnes (mt) in 2050 is found. Mitigation potential analysis of different sectors showed that the industrial sector of the city has the biggest mitigation capacity (13%) until the year 2050, whereas, the smallest proportion may come from the commercial sector (4%). The carbon emissions in LMA would be reduced as much as 50% as compared to the BaU scenario until 2050. Moreover, this mitigation potential study would also serve as a ripple effect in the data deficient cities of low-income countries.

Grey-based Multi-Criteria Decision Analysis approach: Addressing uncertainty at complex decision problems

In complex systems, decision makers encounter uncertainty from various sources. In this paper, a new hybrid grey-based Multi-Criteria Decision Analysis (MCDA) approach is proposed to optimize the evaluation space in decision problems that are subject to subjective and objective uncertainty over different types of interrelated criteria. The four-phase methodology begins with the formulation of a decision problem through the analysis of the system of concern, its functionality, and substantial connections among evaluation criteria. The second phase involves the development of grey linguistic scales to handle the uncertainty of human judgements. The third phase integrates the grey linguistic scale, concepts of grey systems theory, and principles of Analytical Network Process to prioritize criteria. Finally, to evaluate and rank alternatives in such a complex setting, Preference Ranking Organization METHod for Enrichment Evaluation II is extended using a grey linguistic scale to articulate subjective uncertainty, grey numbers to account for objective uncertainty, grey operating rules to normalize evaluation measures, and the proposed approach of prioritizing evaluation criteria to establish relative preferences. To demonstrate the viability of the methodology, a case study is presented, in which a strategic decision is made within the context of innovation. To validate the methodology, a comparative analysis is provided.

Designing the National Network for Automatic Monitoring of Water Quality Parameters in Greece

Water quality indices that describe the status of water are commonly used in freshwater vulnerability assessment. The design of river water quality monitoring programs has always been a complex process and despite the numerous methodologies employed by experts, there is still no generally accepted, holistic and practical approach to support all the phases and elements related. Here, a Geographical Information System (GIS)-based multicriteria decision analysis approach was adopted so as to contribute to the design of the national network for monitoring of water quality parameters in Greece that will additionally fulfill the urgent needs for an operational, real-time monitoring of the water resources. During this cost-effective and easily applied procedure the high priority areas were defined by taking into consideration the most important conditioning factors that impose pressures on rivers and the special conditions that increase the need for monitoring locally. The areas of increased need for automatic monitoring of water quality parameters are highlighted and the output map is validated. The sites in high priority areas are proposed for the installation of automatic monitoring stations and the installation and maintenance budget is presented. Finally, the proposed network is contrasted with the current automatic monitoring network in Greece.