Multi-criteria Decision Support Research Articles

Model of integration and cooperation of multi-criteria decision support methods

The aim of the article was to present the model of integration and cooperation (hybridization) of selected methods of decision support as a specific research procedure. The model is the basis for the construction of the so-called decision-making dashboard, which is the main module of the academic, prototype version of the multi-criteria decision support system – DSS 3.0 [1], [2]. Three methods were hybridized: AHP/ANP and ELECTRE TRI included in the area of multi-criteria decision analysis and the method of identification and econometric evaluation based on multiple regression analysis. Due to the limited volume of the study, the description of the algorithms of these methods was abandoned. The focus was on defining the set of input data common to the above-mentioned methods and on the description of the integration process of the results of the decision analysis obtained with their use, indicating the effects of their synergy. The properties of the model are illustrated on a practical example of employee team assessment (case study). The decision making dashboard provided answers to some interesting questions. How is the ranking of employees shaped? What is the overall assessment of a given employee (or candidate for a position) compared to the results of the entire staff? What is the distribution of the number of assignments of the ranked employees to the established classes (profiles) of multi-criteria assessment? Are the ranking favourites in the group of the highest, average or poorly rated employees? What is the ratio of the level of employees’ remuneration to the ratings obtained in the ranking?

An approach for selecting a business process modeling language that best meets the requirements of a modeler

Abstract The work presented in this paper consists in an approach for selecting a business process modeling language using multi-criteria decision, more precisely the AHP (Analytic Hierarchy Process) method. The selected business process modeling language should best meet the requirements and objectives of the modeler according to the process to be modeled. The comparative framework established for our approach consists in constructing a decision problem solved using the AHP multi-criteria analysis method. The prioritized criterion describes the modeler requirements and the alternatives represent the set of business process modeling languages under study. The comparative framework is based on seven criteria: Expressiveness, Flexibility, Formality, Readability, Support Tools, Usability and Ease of Learning. Our approach is, essentially, justified by the fact that none of the existing languages fully supports all the criteria. Multi-criteria decision support analysis is therefore the most appropriate approach for their comparison.

Use of a multicriteria model for evaluating the use of sustainable practices in a clothing industry

This study aims to use a multicriteria model to evaluate sustainable practices in sectors of a garment industry. Therefore, a theoretical basis was formed and steps were implemented regarding the case study, using not only the sustainability, but also the synchronisation of sectors in the company. The multicriteria decision support method fuzzy TOPSIS was used to evaluate the performance of alternatives. The results showed that the use of this method to evaluate the sectors can help managers to make decisions, so that the company improves more and more in the aspect of a sustainable experience. Other categories, such as the general direction of the company, can be considered in future studies. Previous research focused on theoretical studies on sustainability. This study showed the mathematical models to define the best alternative of sustainability in the manufacturing industries.

Multi-criterion Intelligent Decision Support system for COVID-19

COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is “if the disease can travel faster information has to travel even faster”. The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan-European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered). The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using the Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation.

Método para el diseño arquitectónico de casas de madera en Portugal

Se presenta un método para el diseño arquitectónico de casas de madera. Se entrevistó a empresas de construcción y fabricación y se desarrolló un caso de estudio simulando cuatro variaciones de un proyecto, incluidos diferentes sistemas de madera (entramados ligeros, poste y viga, troncos, y madera contralaminada encolada). Sobre la base de los problemas identificados, se definió un método de diseño utilizando tablas de apoyo a las decisiones, listas de procedimientos y estrategias de optimización. Un proceso de apoyo a la decisión de criterios múltiples fue propuesto como una ayuda en la evaluación y selección de la mejor solución. Durante la fase del programa, el uso de un catálogo de tipologías con sistemas de construcción y sistemas formales promueve la optimización del proceso. En la fase de anteproyecto, la adopción de un proceso de evaluación, procedimientos de diseño específicos y estrategias preestablecidas (durabilidad, integridad de la construcción, economía) tienen como objetivo garantizar la calidad de la construcción.

A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process

Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based models of random forest (RF) and a human knowledge-based multi-criteria structure of analytical hierarchical process (AHP) in accordance with the objective and the subjective factors of the textile manufacturing process. More importantly, the textile chemical manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile chemical manufacturing processes.

Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study

The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the analyzes, both the positions in the final rankings, as well as the corresponding values of utility functions of the decision variants were studied. As a result of the detailed analysis of the obtained results, recommendations were presented in the field of reference criteria set for the considered decision problem, thus demonstrating the practical usefulness of the authors’ proposed approach. It should be pointed out that the presented study of criteria relevance is an important factor for objectification of the multi-criteria decision support processes.

Choosing flying hospitals in the fight against the COVID-19 pandemic: structuring and modeling a complex problem using the VFT and ELECTRE-MOr methods

The increase in the number of cases of the new coronavirus has threatened the capacity of health systems worldwide, with several states declaring the collapse of the hospital system. In order to find a way to support Brazil in the fight against the pandemic, this article used the Value-Focused Thinking (VFT) and ELECTRE-MOr methods to select the most suitable hospital aircraft to be acquired by the Brazilian government. The airplanes would be used in aeromedical evacuations, supporting the fight against the pandemic, especially in regions where there are no more ICU beds available, transporting patients to other locations with greater structure, technological and technical experience. To this end, six models manufactured by Embraer, employed in aeromedical evacuations, were evaluated in the light of operational, technical and commercial criteria. This paper aims to solve a problem of extreme relevance to academia and society, as it proposes the application of a new Multi-Criteria Decision Support (MCDS) method to contribute to the solution of a problem that affects millions of people in Brazil and worldwide. After the application of the methods, Legacy 450 and Praetor 500 aircrafts were chosen as the most indicated to be acquired by the Government and used as hospital aircrafts in the fight against the pandemic.

Renewable Energy Problems: Exploring the Methods to Support the Decision-Making Process

In the current scenario of increasing energy demand and encouraging sustainable development in countries, the energy sector’s planning has become more complex, involving multiple factors, such as technical, economic, environmental, social, and political. The decision process plays a vital role in structuring and evaluating complex decision situations related to the sector, considering various criteria and objectives, encouraging adopting policies to promote energy efficiency actions by increasing research on renewable energy sources and strategic energy decisions. The high number of multi-criteria decision support methods (MCDM) available and their efficiency in solving highly complex problems results in an impasse with their selection and application in specific decision situations. Thus, the scientific community requires methodological approaches that help the decision-maker select the method consistent with his problem. Accordingly, this paper conducts a Systematic Literature Review (SLR) of renewable energy problems associated with MCDM methods based on a final set of 163 articles. We identified five categories of problems solved by MCDM techniques: Source selection, location, sustainability, project performance, and technological performance. We separate the MCDM process into five evaluation steps (alternative selection, criteria selection, criteria weighting, evaluation of alternatives, and post-assessment analyzes), and we extract the methods used in each MCDM step from papers. This paper’s main contribution is identifying the most common MCDM methods in the renewable energy area and the energy problem they solve. Accordingly, this manuscript helps energy decision-makers, entrepreneurs, investors, and policy-makers to improve their ability to choose the proper MCDM methods to solve energy problems.

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths.

Multicriteria Decision Support Would Avoid Overdiagnosis and Overtreatment.

Population-level studies confirm the existence of significant rates of overdiagnosis and overtreatment in a number of conditions, particularly those for which the screening of asymptomatic individuals is routine. The implication is that the possibility of being overdiagnosed and/or overtreated must be mentioned as a possible harm in generating informed consent and participation from the individual invited to be screened. But how should the rates of such preference-insensitive population-level phenomena be introduced into preference-sensitive individual decision making? Three possible strategies are rejected, including the currently dominant one that involves presenting the rates relevant to overdiagnosis and overtreatment as discrete pieces of information about a single criterion (typically condition-specific mortality). Extensive quotation from a review of cancer decision aids confirms that processing this complex and isolated information is not a practical approach. However, the task is unnecessary, since an outcome-focused multicriteria decision support tool will incorporate the effects of overdiagnosis and overtreatment - along with the effects of any underdiagnosis and undertreatment.

Decision support for scenario analysis in a complex water resource project

Multi-criteria decision support (MCDS) was developed and demonstrated for a complex reservoir system for a comprehensive assessment of performance for a complex reservoir system. A management model for the Tandula complex system in India was developed in MIKE HYDRO Basin. This system has three interconnected reservoirs, three irrigation commands, and one industrial user. The results from simulations were analyzed in multi-criteria decision support using an economic and six performance indicators to get a comprehensive performance index for the system. The results of the analysis confirmed that the yearly deficit of the Tandula command maybe about 6.36 MCM with 88% reliability under the present efficiency of 51% and no groundwater use (SCN-1). The comprehensive performance index for scenario SCN-1 was 0.13 which can be increased to 0.86 by increasing efficiencies and consumptive use. Abbreviations: AHP: Analytical hierarchical process; ANN: Artificial Neural Network; BSP: Bhilai Steel Plant; CEA: Cost effectiveness analysis; DAPP: Dynamic adaptive policy pathways; DS: Decision scaling; GW: Groundwater; Ha: Hectare; MORDM: Many objective robust decision making; MOVA: Many-objective visual analytics; MCDA: Multi Criteria Decision Analysis; MCM: Million cubic meter; MCDA: Multi-criteria decision analysis; NB-DSS: Nile Basin Decision Support System; ROA: Real options analysis; RRV: Reliability, resilience, vulnerability; RDM: Robust decision making; WEAP: Water Evaluation and Planning; SCN: Scenario; VIDEO Visually interactive decision-making and design using evolutionary multi-objective optimization

Multi-criteria decision support systems based on linguistic intuitionistic cubic fuzzy aggregation operators

This article is an advanced approach to linguistic intuitionistic fuzzy variable through application of cubic set theory. For instance, we establish the idea of the linguistic intuitionistic cubic fuzzy variable (LICFV) theory and define several operations for LICFV; also establish a series of weighted aggregation operators under linguistic intuitionistic cubic fuzzy information, so called linguistic intuitionistic cubic fuzzy weighted averaging (LICFWA) operator, linguistic intuitionistic cubic fuzzy order weighted averaging (LICFOWA) operator, linguistic intuitionistic cubic fuzzy weighted geometric (LICFWG) operator, linguistic intuitionistic cubic fuzzy order weighted geometric (LICFOWG) operator, linguistic intuitionistic cubic fuzzy hybrid averaging (LICFHA) operator, and linguistic intuitionistic cubic fuzzy hybrid geometric (LICFHG) operator; and further study their fundamental properties and showed the relationship among these aggregation operators. In order to demonstrate the feasibility and practicality of the mentioned new technique, we develop multi-criteria decision-making algorithm under linguistic intuitionistic cubic fuzzy environment. Further, the proposed method applied to mobile phone selection, consider numerical application of mobile phone. Comparing the proposed techniques with other pre-existing aggregation operators, we concluded that the proposed technique is better, reliable, and effective.

Soil multifunctionality: Synergies and trade‐offs across European climatic zones and land uses

AbstractWith increasing societal demands for food security and environmental sustainability on land, the question arises: to what extent do synergies and trade‐offs exist between soil functions and how can they be measured across Europe? To address this challenge, we followed the functional land management approach and assessed five soil functions: primary productivity, water regulation and purification, climate regulation, soil biodiversity and nutrient cycling. Soil, management and climate data were collected from 94 sites covering 13 countries, five climatic zones and two land‐use types (arable and grassland). This dataset was analysed using the Soil Navigator, a multicriteria decision support system developed to assess the supply of the five soil functions simultaneously. Most sites scored high for two to three soil functions, demonstrating that managing for multifunctionality in soil is possible but that local constraints and trade‐offs do exist. Nutrient cycling, biodiversity and climate regulation were less frequently delivered at high capacity than the other two soil functions. Using correlation and co‐occurrence analyses, we also found that synergies and trade‐offs between soil functions vary among climatic zones and land‐use types. This study provides a new framework for monitoring soil quality at the European scale where both the supply of soil functions and their interactions are considered.Highlights Managing and monitoring soil multifunctionality across Europe is possible. Synergies and trade‐offs between soil functions exist, making it difficult to maximize the supply of all five soil functions simultaneously. Synergies and trade‐offs between soil functions vary by climatic zone and land‐use type. Climate regulation, biodiversity and nutrient cycling are less frequently delivered at high capacity.

A multi-criteria decision support framework for municipal solid waste landfill siting: a case study of New South Wales (Australia)

Sanitary waste disposal and site selection for establishing landfills are challenging problems for environmental planners. This paper aims to take environmental, socio-economic, geological, geomorphological, hydrological and ecological factors into consideration to provide a decision support framework for landfill siting. Analytical hierarchy process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) are coupled to develop an efficient multi-criteria decision-making method to be utilized in a Geographic Information System (GIS) environment for evaluating the suitability for landfill siting. As the first attempt to employ DEMATEL effectively in a landfill site selection problem, the proposed method is tested with landfill siting scenarios in New South Wales (NSW), Australia. Regional analysis is also performed to identify the potentially most suitable statistical divisions for landfill siting in NSW. The top two ranked zones covering 0.7% and 22% of the study area, respectively, are considered as the optimal areas for establishing landfills, while the bottom two ranked zones are not recommended for further consideration. Further detailed analysis is also conducted on the existing landfills, which shows that 1.0% and 37.0% of them are ranks 1 and 2, respectively. The scenario-based analysis implies that, among the contributing factors; geological and economic factors are highly important.

Efficiency of Polder Modernization for Flood Protection. Case Study of Golina Polder (Poland)

This paper presents an analysis of the effectiveness of variants of the reconstruction of a polder, which is part of the flood protection system of a large urban agglomeration. The Golina polder, located in the floodplain of the Warta River, was selected as the case study. The multi-criteria decision support methods AHP and Fuzzy AHP were used to assess the effectiveness of individual variants. Information on the floods from 1997 and 2010, data on land cover, land development, nature and historical objects were used to estimate the value of the coefficients for the multi-criteria decision methods. It was shown that the planned deep modernization of the hydrotechnical infrastructure and the purchase of land in the polder area is less effective than maintaining the current state of development of the polder.

Multicriteria Decision Support for Selection of Alternatives Directed to Integrated Urban Water Management

The management of water resources in medium and large cities has been changing in recent years, mainly as a result of climate change and the sharp increase of urbanization. In addition to the traditional solution of expansion of water supply, an approach that integrates technological alternatives of demand management in the infrastructure of urban constructions is proposed herein. However, these facilities are often more complex and expensive than conventional ones, being also challenging to achieve a balance between costs and benefits. While the environmental, social, and engineering benefits seem obvious, expressing them in monetary terms is usually a challenge that cost-effectiveness do not respond satisfactorily. Therefore, this research uses the Fuzzy Analytic Hierarchy Process multiple criteria method to rank three main alternatives (rainwater harvesting system, gray water recycling system, and water saving devices) that can be applied in low-income residences in Brazil. For this purpose, this paper considers criteria normally used in this type of analysis such as economic, technical, environmental and social criteria but also includes the local population acceptance and the evaluation of experts. The main results suggest that the most appropriate alternative is the use of water saving devices and that there is also a need for public policies that offer financial incentives to the population and prevent socioeconomic conditions from inhibiting the adoption of water conservation practices.

Risk Assessment of Business Models driven by Industry 4.0

Industry 4.0 changes business models and in so doing presents new risks that need to be addressed. Research presented in this paper developed a risk assessment for business model digitisation (RADi) matrix which consists of technical, competences, employee and customer consent, data security, and financial risk. The expert evaluation uses the FARE method as a multi-criteria decision support approach. The results determine which parts of a business model driven by the Industry 4.0 are most affected by different risks. The results indicate the greatest impact is on customer channels, key resources, revenue stream, and customer segments, whilst key partners are the least impacted. Our research demonstrates that the RADi model can be used by firms to identify and plan for critical risks as well as to implement the digitisation of business models. Policymakers will find the RADi approach useful to anticipate risks and prioritise public support and regional development.

Planning and Managing the Integrated Water System: A Spatial Decision Support System to Analyze the Infrastructure Performances

The demand for water is constantly increasing, while there are factors related to climate change and pollution that make it less and less available. Addressing this problem means being able to face it with a global approach, which takes into account that human beings need water to survive, as well as all the systems on which they rely, namely sanitation, health, education, business, and industry. While human behavior is influenced by the growing awareness on this topic promoted by organizations specifically targeting this mission, the need to protect water resources in operational terms has led mainly to the need for smart urban infrastructure planning, consistent with the objective of promoting sustainable development. To this aim, the authorities in charge of monitoring the implementation of the investment plans by operators need to perform accurate evaluations of the technical quality of the services provided. The present paper introduces a framework to design a Multi-criteria Spatial Decision Support System, conceived to help decision-makers define and analyze the investment priorities of the individual service operators. By building a knowledge model of the network under investigation, decision-makers are aware of physical components of the whole system and are provided with an intervention priority index related to the network objects that could be affected by the planning action to be implemented.

A multi-criteria decision support method towards selecting feasible and sustainable housing renovation strategies

This research presents a decision support method for housing managers to identify the most appropriate renovation strategy, which combines a multi-criteria assessment of ten renovation factors together with an economic feasibility analysis. In contrast to the majority of decision support systems, this method not only assesses the impact of the renovation proposals, but also includes and evaluates the management of the renovation process through a procedure of quantifying and weighting multiple social, technical, and economic variables related to the renovation work. The method, based on two representative cases of housing renovation projects in Sweden, has been designed and tested and is also applicable to other locations, multi-family buildings, and ownership models. Via a graphical display, the results show the impact generated during the work, the benefits obtained after the interventions, and the economic feasibility in different timeframes of each design strategy, which supports housing owners’ decision-making and promotes feasible and suitable actions from a multidisciplinary approach. The conclusions highlight the possibility of implementing renovation strategies at various levels and provide policy implications of this method for a cleaner operation in the building sector with responsible management of urban regeneration by generating sustainable renovation rates and satisfactory proposals.