Multi-criteria Decision Support System Research Articles

A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management

The phenomenon known as urban heat islands (UHIs) is becoming more common and widespread, especially in large cities and metropolises around the world. The main cause of these temperature variations between the city center and the suburbs is the replacement of large tracts of natural land with artificial (built-up) surfaces that absorb solar heat and radiate it back at night. UHIs have been the subject of numerous studies, most of which were about defining the main characteristics, factors, indexes, etc., of UHIs using remote sensing technologies or about determining mitigating activities. This paper provides a comprehensive overview of the literature, as well as a bibliometric analysis, to discover research trends related to the application of decision support systems and multi-criteria decision-making for UHI management, with a special emphasis on fuzzy theory. Data collection is conducted using the Scopus bibliographic database. Throughout the literature review, it was found that there were not many studies on multi-criteria analysis and decision support system applications regarding UHIs. The fuzzy theory application was also reviewed, resulting in only a few references. However, this topic is current, with an increase in published papers, and authors see this as an opportunity for improvement and further research.

Developed analytic hierarchy process and multi criteria decision support system for wind farm site selection using GIS: A regional-scale application with environmental responsibility

As determined by empirical analysis, renewable energy sources account for less than 1.73 percent of the developing countries energy market. The western region of Iran, or province of Kermanshah, presents significant opportunities for power exports to neighboring country (Iraq). In this research, an appropriate location for wind farms in Western Iran is determined with the assistance of a combined method of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) description. Electrical, techno-economic, environmental, and geo-infrastructure aspects, as well as their respective sub-criteria, were subjected to a comprehensive analysis and weighting in this study. The Analytical Hierarchy Process (AHP) was utilized to achieve this result. The aforementioned conclusions were reached through an extensive examination of insightful interviews conducted with esteemed experts in the domain of wind energy. Significant suitability for the potential installation of large-scale wind turbines or wind farms is evident in six critical areas, encompassing more than 20 % of the evaluated region, according to the findings of this paper. Conversely, it was deduced that precisely 80 % of the areas examined were deemed inappropriate for such endeavors. The assessment yielded four locations that were deemed suitable, denoted as A, B, C, and D. The southern, western, and central regions of the province contain these locations. The proposed total capacity is 216 MW, which is determined by the combined surface areas of these regions (19 km2, 55 km2, 64 km2, and 40 km2, respectively). An anticipated reduction of 1,262,471 tons in greenhouse gas emissions is anticipated as a result of the implementation and substitution of the proposed wind farms for the gas turbine power plants that presently predominate in Iran's thermal power plant sector. The method used in this article can be extended to all cases of other faculties to measure the potential of wind farm resources.

Integrated GIS and analytic hierarchy process for flood risk assessment in the Dades Wadi watershed (Central High Atlas, Morocco)

Flood risk assessment is crucial for delineating flood hazard zones and formulating effective mitigation strategies. Employing a multi-criteria decision support system, this study focused on assessing a Flood Risk Index (FHI) at the Dades Wadi watershed scale. Seven main flood-causing criteria were broadly selected, namely flow accumulation, distance from hydrographic network, drainage network density, land use, slope, rainfall, and permeability. The relative importance of each criterion prioritized as per their contribution toward flood risk, which were employed a blend of the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS)/Remote Sensing (RS) techniques. The significance of each criterion was determined based on their contribution to flood hazard, and their relative importance was established through an AHP pair-wise comparison matrix. The efficacy of the AHP model was performed with a consistency ratio of 0.08, which indicated that each flood criterion weight is confirmed. Among the flood-causing criteria, the hydrologic flow accumulation factor was identified as the most influential (weight: 3.11), while permeability exhibited the least prominence (weight: 0.58). Approximately 40.36% of the total area, equivalent to around 1319, 89 km2, was concentrated within very high flood-risk zones situated near rivers. In contrast, an area of approximately 399,943 km2 (56.33%) exhibited a low to very low flood risk zone. The validation of the FHI map encompassed the application of the Receiver Operating Characteristic Curve (ROC) technique, revealing an Area Under the Curve (AUC) of 85%.

Multi-criteria decision support system using AHP method to determine the weight of transformer paper insulation index

The transformer has evolved into an electrical device that operates based on the principle of electromagnetic induction without altering the frequency, capable of transferring electric energy from one or more electrical circuits to others by means of magnets. Insulation material is utilized as equipment to safeguard electrical components, where the resilience of each insulation plays a significant role in the functioning of the system. In addition to providing mechanical strength and distance, paper insulation functions as a dielectric medium. The quality of paper insulation deteriorates due to excessive heat generated from oil oxidation, leading to a degradation process known as depolymerization. In addressing this issue, a Decision Support System is required to aid in calculating the condition index of the paper insulation based on given parameters. The chosen method is the Analytic Hierarchy Process (AHP) utilizing multiple criteria. The outcome of applying this method is that the entire system operates as intended, and the calculation process yields appropriate results. Testing results, which involve a comparison between manual calculations and the Confusion Matrix system, exhibit a 100% accuracy rate. Additionally, user testing yields an average satisfaction level of 90.97%. In conclusion, this system adequately fulfils its intended purpose and significantly helps to determine the quality level of paper insulation in transformers.

Towards a multi-criteria decision support system for heating and cooling equipment selection in residential buildings

Towards a multi-criteria decision support system for heating and cooling equipment selection in residential buildings

Designing a Website-Based Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) for Parental Control of Children's Media

The massive amount of children's television programming poses a challenge for parents seeking to mediate suitable viewing habits. This research puts forward a website-based Decision Support System (DSS) leveraging Analytic Hierarchy Process to recommend appropriate shows per personalized criteria. The DSS allows parents to set priorities across factors like educational value, empathy, and social norms reflected in shows. Quantitative AHP methodology ranks options by synthesizing comparative judgments. A prototype is implemented on PHP/MySQL with an intuitive interface explaining decision logic. Testing with parent focus groups indicates significantly increased perceived utility over traditional controls. Recommendation accuracy metrics averaged 0.81 against test cases. Outcomes demonstrate feasibility and benefits of applying multi-criteria DSS techniques to aid value-based media selection for impressionable minds. The system aims to aid busy caregivers overwhelmed by the exponential growth in media choices, promoting discerning consumption. Further enhancements incorporating explanatory facilities and predictive analytics promises wider applicability inside content distribution ecosystems. This research highlights the importance of DSS-AHP integration for child development in the modern information age.

Multi-criteria decision support system for bridge construction system selection utilizing value engineering and TOPSIS

When selecting the appropriate bridge deck construction system, it is essential to consider many criteria such as the span length, geographical location, construction speed, cost, site conditions, resource availability, technology, ease of construction, and service life. The objective of this study is to optimize the decision-making process for selecting a bridge deck construction system in the preliminary design and planning stage. The proposed model allows designers or decision-makers to make an informed choice of an appropriate construction system according to project criteria through a decision support system. The model employs value engineering methodology and a multi-criteria decision-making method and utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making method. To gather modeling data from a focus group consisting of professional bridge engineers, a semi-structured interview and two questionnaires are conducted. When applying the proposed model to two active bridge construction projects in Egypt, it reveals that "Span by Span using launching girder" and "precast post tension girder" are better suited to cases one and two, respectively. The study makes a contribution by presenting a decision support system that combines value engineering methodology and a multi-criteria decision-making method (TOPSIS). This system empowers designers and decision-makers to make project decisions considering specific criteria and constraints.

Multicriteria decision support system for triage and ethical allocation of scarce resources to COVID-19 patients

Multicriteria decision support system for triage and ethical allocation of scarce resources to COVID-19 patients

Multi-criteria Decision Support System for Prioritizing Challenges of Internet of Things (IoT)

Abstract: The adoption and utilization of Internet of Things (IoT) technologies such as IoT Platforms, 5G, connected sensors, WAN, etc mostly come with numerous, seemingly complex and expensive challenges for institutions and organisations, particularly in developing countries as they work to fill their fair share of the global IoT market. Multi-criteria decision support systems (MCDSS) can be indispensable tools for evaluating and prioritizing these complex competing difficulties by multistakeholder decision makers. While eliminating conventional decision problems such as, human biases, time consumption, cost of decision, etc, MCDSS make it possible for companies to concentrate on solving challenges which their limited resources can cater for. This study presents an approach for design and implementation of a prototype MCDSS for prioritizing challenges that institutions and organizations face while adopting and utilizing IoT technologies using Fuzzy Analytic Hierarchy Process (FAHP) methodology. ASP.Net model View controller (MVC) framework and C Sharp programming Language was used for the GUI and the Logic development. The system's default IoT challenges and dataset were adapted from the works of A.K. Mohammadzadeh (Baseline dataset). The system usability test result obtained shows that the system is friendly and usable. The output weights and rating of the IoT technology adoption challenges/sub-challenges exhibit over 80% similarity when compared to the Baseline dataset. Further studies are recommended to make the system a

Application of a geographic information system and the method of analysis of hierarchies to assess construction sites for landfills for municipal waste in the Khabarovsk Territory

An integrated system for managing municipal solid waste at the regional level should be consistent with the goals of Russia's sustainable development. The key element of the system is the landfill. One of the most pressing issues in waste management is the choice of a suitable site for the construction of a landfill. This article presents the results of a study on the use of a geographic information system and the method of analyzing hierarchies to select a land plot for a municipal waste disposal site in the Komsomolsk cluster of the Khabarovsk Territory. Three land plots proposed by the municipal authorities for the construction of the landfill were studied using eleven evaluation factors. Evaluation factors were the population within a radius of 1.5 km, the presence of enterprises within a radius of 1.5 km, the population density in the service area, the distance to the boundaries of the settlement, the distance to the airport, the distance to water bodies, the current use of the land, the depth of the first aquifer, site slope, site area, distance to transport routes. As a result of the study, it was shown that the option of locating the MSW landfill in the Solnechny municipal district is the most preferable in terms of a set of evaluation factors. The worst option is the construction of a landfill in the vicinity of Komsomolsk-on-Amur. The study showed that solving the problem of taking into account socio-economic and sanitary-environmental factors when choosing a land plot for the construction of a MSW landfill requires a practical methodology that can reduce the subjectivity of a management decision. Satisfying the need for a methodology to improve the validity of management decisions when choosing the best places for the construction of MSW landfills is possible through the use of GIS technologies and a multi-criteria decision support system.

Multi-criteria decision support systems for electric vehicle charging schedules in a smart secondary distribution system

The operation of a smart secondary distribution network (SDN) has gained significance due to the penetration of chargers for electric vehicle (EV) and in-situ generation. The SDN operators (SDNOs) need dedicated decision support systems (DSS) for scheduling the EVs for charging without overloading the distribution transformer. Multi-criteria DSS(MCDSS) are proposed for the first time to balance the conflicting constraints stemming from the perspectives of both EV users and grid operators. This new approach has also incorporated a contribution-based fairness value (CFV) for each prosumer. MCDSS formulations employing analytical hierarchy process (AHP), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), and Preference Ranking Organisation METHod for Enrichment Evaluation (PROMETHEE) have been developed for generating fair charging schedules. Performance metrics have also been proposed to compare the three MCDSS techniques. The developed scheduling approaches have been tested in an Australian 19-bus SDN with Ausgrid datasets, and the schedules obtained are presented to bring out the significance of the proposed MCDSS.

Integrating collective know-how for multicriteria decision support in agrifood chains-application to cheesemaking.

Agrifood chain processes are based on a multitude of knowledge, know-how and experiences forged over time. This collective expertise must be shared to improve food quality. Here we test the hypothesis that it is possible to design and implement a comprehensive methodology to create a knowledge base integrating collective expertise, while also using it to recommend technical actions required to improve food quality. The method used to test this hypothesis consists firstly in listing the functional specifications that were defined in collaboration with several partners (technical centers, vocational training schools, producers) over the course of several projects carried out in recent years. Secondly, we propose an innovative core ontology that utilizes the international languages of the Semantic Web to effectively represent knowledge in the form of decision trees. These decision trees will depict potential causal relationships between situations of interest and provide recommendations for managing them through technological actions, as well as a collective assessment of the efficiency of those actions. We show how mind map files created using mind-mapping tools are automatically translated into an RDF knowledge base using the core ontological model. Thirdly, a model to aggregate individual assessments provided by technicians and associated with technical action recommendations is proposed and evaluated. Finally, a multicriteria decision-support system (MCDSS) using the knowledge base is presented. It consists of an explanatory view allowing navigation in a decision tree and an action view for multicriteria filtering and possible side effect identification. The different types of MCDSS-delivered answers to a query expressed in the action view are explained. The MCDSS graphical user interface is presented through a real-use case. Experimental assessments have been performed and confirm that tested hypothesis is relevant.

Intelligent Multi-Criteria Decision Support for Renovation Solutions for a Building Based on Emotion Recognition by Applying the COPRAS Method and BIM Integration

With accelerating climate change and the urgent need to cut carbon emissions, global focus has turned to the existing building stock and its renovation. Sustainable renovation helps to achieve better energy performance and gain wider sustainability benefits, such as increased value of a building, improved indoor and outdoor comfort, reduced carbon emissions, and the higher satisfaction and better emotional state of inhabitants. Numerous systems and tools have been developed worldwide to assist with decision making in the choice of preferred modernisation scenarios and alternatives. However, social aspects are often neglected in the existing systems, and emotions of inhabitants are rarely analysed. To close this gap, the present study proposes an innovative decision-making framework for sustainable renovation solutions, based on emotion recognition. The framework makes it possible to assess various renovation alternatives against sustainability criteria and real-time measurements of the emotional states of inhabitants. Based on the proposed framework, an intelligent multi-criteria decision support system was developed by integrating COPRAS and the facial action coding system, the method of automatic facial expression recognition, and the continuous calibration and participant methods. The system was tested in the case study of renovation solutions for a building located in Ukraine. The research results revealed that the proposed renovation solutions had a positive impact on the emotional state of inhabitants, especially when visual materials such as drawings were presented. Some case studies were analysed together with the application of decision system tools and building information modelling (BIM) subsystem integration as a multidiscipline application of various applied sciences for representation and data analysis. The authors of this research have been analysing human emotional, affective and physiological states for many years and collected over a billion of these data in Vilnius city during the H2020 ROCK, SAVAS and BIM4REN projects. Data acquired during measurements in Vilnius were used to determine correlations and trends for the case study. The proposed methodology and findings of the study can be useful for researchers who use the evaluation and analysis of human emotions when there is a need to choose appropriate renovation measures or find alternative solutions.

Multi-criteria decision support system for selecting the rainwater harvesting technique in wadi systems, Sinai, Egypt

Abstract Recently, some areas are exposed to water scarcity and floods, and rainwater harvesting (RWH) is a practical solution, particularly in arid and semi-arid regions. The Wadi Watier basin located in South Sinai, Egypt was selected to identify the appropriate RWH techniques. Five alternatives were proposed: a dam with a reservoir, a storage pond, an underground tank, wadi bed cultivation, or a Jessor system. The hydrological model was built using the WMS software to determine the runoff volume at the proposed RWH sites. Multiple criteria were identified and classified based on their suitability degree. Then, an analytic hierarchy process (AHP) was used to assign the effective weight of each evaluation criterion. The AHP revealed that the wadi slope and initial cost have the highest evaluation influence of 20%, followed by the wadi width of 15%. The rainfall volume, social acceptance, and cost revenue have moderate influences with weights from 8 to 11%. Finally, the storage ponds and underground tanks recorded the highest-suitability evaluations at the selected RWH sites, at 65.4 and 62.7%, respectively. The dam with a reservoir recorded 49.3%, which is considered medium suitability, while wadi bed cultivation and Jessor systems were classified as low-suitability evaluations with 42.7 and 41.7%, respectively.

A Hybrid Multi-Criteria Decision Support System for Selecting the Most Sustainable Structural Material for a Multistory Building Construction

In recent years, the performance of the construction industry has highlighted the increased need for better resource efficiency, improved productivity, less waste, and increased value through sustainable construction practices. The core concept of sustainable construction is to maximize value and minimize harm by achieving a balance between social, economic, technical, and environmental aspects, commonly known as the pillars of sustainability. The decision regarding which structural material to select for any construction project is traditionally made based on technical and economic considerations with little or no attention paid to social and environmental aspects. Furthermore, the majority of the available literature on the subject considered three sustainability pillars (i.e., environmental, social, and economic), ignoring the influence of technical aspects for overall sustainability assessment. Industry experts have also noted an unfulfilled need for a multi-criteria decision-making (MCDM) technique that can integrate all stakeholders’ (project owner, designer, and constructor) opinions into the selection process. Hence, this research developed a decision support system (DSS) involving MCDM techniques to aid in selecting the most sustainable structural material, considering the four pillars of sustainability in the integrated project delivery (IPD) framework. A hybrid MCDM method combining AHP, TOPSIS, and VIKOR in a fuzzy environment was used to develop the DSS. A hypothetical eight-story building was considered for a case study to validate the developed DSS. The result shows that user preferences highly govern the final ranking of the alternative options of structural materials. Timber was chosen as the most sustainable option once the stakeholders assigned balanced importance to all factors of sustainable construction practices. The developed DSS was designed to be generic, can be used by any group of industry practitioners, and is expected to enhance objectivity and consistency of the decision-making process as a step towards achieving sustainable construction.

A multi-criteria decision support system for the assessment of cities based on air quality indicators

A multi-criteria decision support system for the assessment of cities based on air quality indicators

Towards a multi-criteria decision support system for heating and cooling equipment selection in residential buildings

Towards a multi-criteria decision support system for heating and cooling equipment selection in residential buildings

A multi-criteria decision support system for the assessment of cities based on air quality indicators

A multi-criteria decision support system for the assessment of cities based on air quality indicators

A Spatial Decision Support System for Prioritizing Repair Interventions on Power Networks

The business continuity of services provided by Critical Infrastructures is vital in order to ensure the security, the economy and the public’s health of a Nation. Delays and bad recovery strategies after disasters or failures can lead to impairing impacts in terms of injury to people, environmental pollution and loss of time, money and resources. In such a context, the adoption of a spatial Decision Support System (DSS) might play a crucial role in order to help operators to adopt the best recovery strategy in the shortest possible time frame. Current approaches do not consider the problem of assigning an intervention location to a maintenance crew and do not account for the effective time needed for emergency intervention. In this paper we develop a novel spatial multi-criteria DSS methodology for prioritizing repair interventions on power networks. The multi criteria strategy is solved by the adoption of Incomplete Analytic Hierarchy Process (AHP) which computes holistic assignment costs as the result of the combination of multiple and possibly conflicting metrics of cost.Then, we use the holistic costs as the basis for a task assignment phase that is based on the Hungarian algorithm. The proposed strategy has been implemented as a module in the Decision Support System, namely Critical Infrastructure Protection Risk Analysis and Forecast (CIPCast), whose outputs are represented on a web-based Geographic Information System (GIS) platform. The effectiveness of the proposed multi-criteria strategy has been validated via a real case study on the Rome City electrical distribution network.

Artificial intelligence and change management in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives.

Given the increasingly significant role of small and medium-sized enterprises (SMEs) in the global economy and the ever more competitive markets in which these companies operate, SMEs' ability to adopt artificial intelligence (AI) technologies is of utmost importance. Due to constantly evolving social, environmental, and technological scenarios, the managers of these firms must increasingly focus on incorporating new tools such as AI into SME operations in order to enjoy their benefits. However, the subjectivity and complexity of this adaptation process makes integrated analyses of key factors challenging. The present study sought to develop a multi-criteria decision-support system that applies cognitive mapping and the decision-making trial and evaluation laboratory technique in a neutrosophic context. The main objective is to overcome the limitations of previous studies and models by structuring the decision problem and identifying and understanding which factors should be central to adaptation initiative analyses. A panel of experts in AI were recruited to facilitate the construction of an analysis system that takes into account indeterminacy in decision-making processes. The results were validated by both the panel members and project managers at COTEC Portugal-a leading think-and-action network that seeks to advance technology diffusion and business innovation cooperation. The proposed system's practical implications and benefits are also analyzed.