Multi-criteria Decision Support System Research Articles

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.

The Sustainable Home Health Care Process Based on Multi-Criteria Decision-Support

The increase in life expectancy has led to a growing demand for Home Health Care (HHC) services. However, some problems can arise in the management of these services, leading to high computational complexity and time-consuming to obtain an exact and/or optimal solution. This study intends to contribute to an automatic multi-criteria decision-support system that allows the optimization of several objective functions simultaneously, which are often conflicting, such as costs related to travel (distance and/or time) and available resources (health professionals and vehicles) to visit the patients. In this work, the HHC scheduling and routing problem is formulated as a multi-objective approach, aiming to minimize the travel distance, the travel time and the number of vehicles, taking into account specific constraints, such as the needs of patients, allocation variables, the health professionals and the transport availability. Thus, the multi-objective genetic algorithm, based on the NSGA-II, is applied to a real-world problem of HHC visits from a Health Unit in Bragança (Portugal), to identify and examine the different compromises between the objectives using a Pareto-based approach to operational planning. Moreover, this work provides several efficient end-user solutions, which were standardized and evaluated in terms of the proposed policy and compared with current practice. The outcomes demonstrate the significance of a multi-criteria approach to HHC services.

Suitable sites identification for potential rainwater harvesting (PRWH) using a multi-criteria decision support system (MCDSS)

Suitable sites identification for potential rainwater harvesting (PRWH) using a multi-criteria decision support system (MCDSS)

Decision support system for the early stage of green building envelope design considering energy and maintainability

ABSTRACT Green buildings (GB) show performance gaps despite a plethora of materials, technologies and stringent certification systems. India is predicted to shape the global energy demand shortly; hence, its recent GB mandate of Energy Conservation Building Code (ECBC) will contribute significantly to the global Agenda 2030. However, its success depends on a holistic relook beyond the myopic design approach focused on energy efficiency. Therefore, a multi-criteria decision support system (DSS) based on the Best-Worst method was developed. It integrates ECBC and BIM-based energy simulation while facilitating maintainability performance prediction in the early phase of design of the GB envelope. The top five options out of 72 common envelope combinations for wall, fenestration and roof materials were chosen for energy performance. The DSS is flexible to accumulate other decision-making criteria as per project-specific requirements. Its efficacy was analysed with a GB located in a warm-humid climatic zone of India. Here, the material availability criterion was also added. The framework provides probing insights and facilitates decision-makers to act appropriately in the early design phase to ensure efficient performance in the long run.

Modeling wetland restoration scenarios in Gavkhooni International Wetland

One of the challenges for managers is to ensure the long‐term sustainability of wetlands by protecting their ecological values against natural and human pressures. This study has evaluated the past vulnerability status of Gavkhooni International Wetland, Isfahan, Iran, using a combination of GIS and multicriteria spatial decision support systems (MC‐SDSS) processes, to propose restoration scenarios. Three scenarios were developed from criteria established using field studies and expert opinion: (1) wetland restoration to preserve existing water bodies, (2) determination of susceptible regions as habitat replacement for aquatic communities, and (3) a combination of the two previous scenarios. Finally, a map was developed classifying the regions into five categories by area of priority. The northern regions of the wetland were prioritized based on the results of the first and the second scenarios. The third scenario provided the highest opportunity to obtain an optimistic state, that is, normal conditions, for the wetland marginal regions, especially in the central region (between the east and west). In a pessimistic state and optimistic state of the third scenario, almost all parts of the center to the south of the wetland show the lowest priority restoration scenarios. According to the different scenarios, the central regions of the wetland are less affected by agricultural activities and human settlements. In general, the results show that the Gavkhooni Wetland is unstable based on functional, environmental, social and economic conditions. According to the criteria and the MC‐SDSS model, the government can adopt and implement the necessary policies and programs to improve the condition of the wetland.

Suitability modelling: an application to forest road development planning in Peninsular Malaysia

The development of modelling applications with Geographic Information System (GIS) has been widely used for planning and analysis. It is a vital technology that can be integrated with multi-criteria evaluation decision support system for problems solving process. This paper presents the model development for suitability forest road planning for the forestry department of Peninsular Malaysia’s application. Multiple significance preferences factors concerned with suitable forest road allocation were selected for the model development, such as slope constraint, elevation factor, the buffer zone of the river, and distance from the existing road. In conclusion, this model is developed to help forest managers sustainably manage the forest, particularly in commencing the forest harvesting operation, which has been widely reported as the main cause of negative environmental impact.

APPLICATION OF CHOOSING BY ADVANTAGE (CBA) TO SELECT MOST SUSTAINABLE PROJECT, METRO EXTENSION CASE STUDY

Sustainable construction aims to minimize harm and maximize value by achieving a balance between social, economic, technical, and environmental aspects, commonly known as the pillars of sustainability. In recent years, the construction industry has drawn increasing criticism due to its low productivity and high rates of energy consumption, generation of wastes, and greenhouse gas emissions. Traditionally, projects are selected based on return on investment and economic impact, whereas social and environmental considerations have received relatively little attention. When all pillars of sustainability are considered during the selection, taking a decision becomes more complicated. As such, in many cases, the owner opts not to employ a formal decision-making process in determining the most sustainable alternatives. This paper developed a multi-criteria Decision Support System (DSS) to aid stakeholders in deciding on the most sustainable option among many alternatives. Choosing by Advantage (CBA) has been used in this paper for developing a decision-making model considering the four pillars of sustainability. Multiple social, economic, technical, and environmental criteria are identified and evaluated through a case study. The selection of metro rail project from three alternative routes was assessed considering sustainability criteria using the CBA model. The case study stands as a valuable point of reference for identifying the best possible alternative for comparable future projects. Moreover, the developed DSS is expected to enhance objectivity and consistency in selection and to aid in making better decisions in terms of sustainability for any similar construction projects.

An intelligent fuzzy parameterized multi-criteria decision-support system based on intuitionistic fuzzy hypersoft expert set for automobile evaluation

In every decision-making scenario, the role of parameters is pertinent but it becomes critical for the situations when parameters are ambiguous to be assessed by the experts so this sort of uncertainty is judged by assigning a fuzzy membership-grade to each parameter. The existing literature on fuzzy parameterization is unable to provide an appropriate model which may cope with hypersoft setting, multi-decisive opinions of experts and intuitionistic setting collectively. This shortcoming leads to the motivation of this study. In this paper, fuzzy parameterized intuitionistic fuzzy hypersoft expert set (FPIFHsES) is characterized which is capable to address the insufficiencies of existing models like fuzzy parameterized intuitionistic fuzzy soft expert set (FPIFSES) for the consideration of multi-argument approximate function. With the entitlement of this function, FPIFHsES tackles the real-life scenario where each attribute is meant to be further classified into its respective sub-attribute valued disjoint set. The FPIFHsES is more flexible and the reliable with the deep analysis of attributes in the decision-support system. The characterization of FPIFHsES is accomplished by employing theoretic, axiomatic, and algorithmic approaches. In order to validate the proposed model, an algorithm is proposed to study its role in decision-making while dealing with a real-world scenario.

DETERMINING PREFERENCES IN RECOMMENDER SYSTEMS BASED ON COMPARATOR IDENTIFICATION TECHNOLOGY

The subject of research in the article is the process of ranking objects in the lists of recommender systems. The goal of the work is to increase the efficiency of recommender systems by improving the method of determining preferences between objects in lists using the theory of multi-criteria decision-making. The following tasks are solved in the article: review and analysis of the current state of the problem of identifying advantages between objects and their ranking in the lists of recommender systems; analysis of filtering methods used in recommendation systems; decomposition of the decision support problem for selection of objects; development of a combined method for ranking objects in the lists of recommender systems, combining the procedures for selecting a subset of Pareto-optimal objects, structural-parametric synthesis of a scalar multi-criteria estimation model, and evaluating the entire set of selected objects. The following methods are used: mathematical modeling, systems theory, utility theory, decision theory, optimization and operations research. Results. Based on the results of the analysis of the modern methodology for ranking objects in the lists of recommendation systems, the possibility of increasing their efficiency has been established. To take into account factors difficult to formalize, the knowledge and experience of users, it is proposed to implement the determination of preferences between objects using the theory of multi-criteria decision making. The problem of forming lists of recommendation systems is decomposed into the tasks of selecting a subset of Pareto-optimal objects, structural-parametric synthesis of a scalar multi-criteria estimation model, and evaluating a set of selected objects. A combined method for ranking options has been developed that combines the procedures of ordinalistic and cardinalistic ordering technologies and allows one to correctly reduce the subsets of objects included in the lists of recommendations. Conclusions. The developed method for determining preferences expands the methodological foundations for automating the development and operation of recommendation systems, other multi-criteria decision support systems, allows for the correct reduction of the set of non-dominated objects for the final choice, taking into account factors that are difficult to formalize, knowledge and user experience. The practical use of the obtained results due to more economical method of forming lists when adding new objects will allow to decrease the time and capacity complexity of the procedures for providing recommendations, and due to taking into account of set of weighted local indexes and allocation of set of non-dominated objects - to increase quality of given recommendations.

Mangrove monitoring in Lothian Island using airborne hyperspectral AVIRIS-NG data

Constant monitoring of the mangroves is important for understanding their health species assemblage and undertaking policies to rehabilitate or restore them. In this study, an attempt was made to classify and detect health and carbon stock in mangroves through a Remote Sensing perspective. The AVIRIS-NG dataset with high spectral (5 nm) and spatial (5 m) resolution proved to be capable of identifying different species of the same mangrove genus along with other morphological features. Using Spectral Angle Mapper (SAM) classification technique, 75% accuracy has been achieved over 12 separate classes. The health of the Lothian Island mangroves was evaluated by using eight different vegetation indices, namely ACI, ARI, MARI, ARVI, CAI, CARI, Chl-red-edge, and REPI. The vegetation indices were normalized and then used as input in a Multi-Criteria Decision Support System (MCDSS) model. The MCDSS model output health map clearly identifies the degraded and healthy mangroves on Lothian Island. The Lothian Island mangroves were found to be degraded in most of the region, which should be of major concern. Above Ground Biomass (AGB), which is another indicator of health, was calculated using Field measured data and AVIRIS-NG hyperspectral data. The highest, lowest, and mean biomass recorded in field transact is 507.19 and 7.39 and 126.43 tonnes, respectively. These three parameters, namely species assemblage of mangroves, their health, and AGB combined can provide much insight into the ecosystem health of Lothian Island.

APPLICATION OF CHOOSING BY ADVANTAGE (CBA) TO SELECT MOST SUSTAINABLE PROJECT, METRO EXTENSION CASE STUDY

Sustainable construction aims to minimize harm and maximize value by achieving a balance between social, economic, technical, and environmental aspects, commonly known as the pillars of sustainability. In recent years, the construction industry has drawn increasing criticism due to its low productivity and high rates of energy consumption, generation of wastes, and greenhouse gas emissions. Traditionally, projects are selected based on return on investment and economic impact, whereas social and environmental considerations have received relatively little attention. When all pillars of sustainability are considered during the selection, taking a decision becomes more complicated. As such, in many cases, the owner opts not to employ a formal decision-making process in determining the most sustainable alternatives. This paper developed a multi-criteria Decision Support System (DSS) to aid stakeholders in deciding on the most sustainable option among many alternatives. Choosing by Advantage (CBA) has been used in this paper for developing a decision-making model considering the four pillars of sustainability. Multiple social, economic, technical, and environmental criteria are identified and evaluated through a case study. The selection of metro rail project from three alternative routes was assessed considering sustainability criteria using the CBA model. The case study stands as a valuable point of reference for identifying the best possible alternative for comparable future projects. Moreover, the developed DSS is expected to enhance objectivity and consistency in selection and to aid in making better decisions in terms of sustainability for any similar construction projects.

Retailer Segmentation via NeXClass Ordered Multi-criteria Classification Methodology and System

NexClass is a multi-criteria methodology and decision support system developed to support multi-criteria classification into predefined nominal (non-ordered) categories. In this paper we introduce an extension of NexClass methodology at classification problems ordinal (ordered) categories. For each category an entrance threshold is defined by decision maker, which indicates its boundary with respect to the evaluation criteria. Then, assignment of alternatives into the classes is based on the concept of non-exclusivity. This defines at what degree an alternative can be included into a specific category. Alternatives are evaluated according to the evaluation criteria, and non-excluding degrees are calculated for each category. Finally, alternatives are assigned into the category for which non-excluding degree gets the minimum value. The NexClass decision support system implements the above classification algorithm, providing a user-friendly interface. This work presents the methodology and the classification model along with a real world application to retailer segmentation. Findings show that the methodology is valid and can be used for segmentation problems in marketing or relevant domains.

AI-based production and application of English multimode online reading using multi-criteria decision support system.

Reading and writing English have greater significance in learning oral English and comprehensive skills. Artificial Intelligence (AI) is important in many aspects of our lives, including education, healthcare, business, and so on. AI has allowed for significant advancements in the educational system. It has quickly risen to the top of the list of the most rapidly expanding educational technology disciplines. Through its creation, AI has contributed to the creation of new educational and knowledge techniques that are currently being researched across a wide range of fields. Chatbots, Robots’ Assistant, Vidreader, Seeing AI, Classcraft, 3D holograms, and other AI-based programmes were developed to assist both teaching staff and students in using and improving the educational system. In the sphere of education, AI is focusing on sentimentalized artificial learning aids and smart instruction systems. The primary goal and objective of the education business is to construct an intelligent education system, which is now possible thanks to the development of teaching assistant robots, smart classrooms based on AI, and English teaching assistance, among other things. Artificial Intelligence techniques may now be employed at all stages of learning to improve the educational system. During the COVID-19 illness, students and teachers took their education and instruction online in a variety of ways. Learning can be done digitally so that folks do not fall behind in their education. The proposed study has considered multi-criteria decision support systems (MCDM) for AI-enabled production and application of English multimode online reading. This study has offered the application of the super decision tool to facilitate the experimental work. As a result of this, researchers will be able to find and design new solutions to the subject.

Collaborative development of composite indices from qualitative value judgements: The EURO-HEALTHY Population Health Index model

Promoting participation and combining evidence with expert knowledge in constructing composite indices that can be meaningfully interpreted are key open challenges identified in performance measurement literature. To address them, this article proposes a sociotechnical decision-aid process, under a collaborative value-modelling framework, rooted in both process consultation and multicriteria value measurement principles. It consists of two phases (structuring and model building), each one developed in a sequence of non-face-to-face (Web-Delphi) and face-to-face (decision conferencing) group processes, designed to allow for increased participation. Performance indicators are selected and structured in a value-tree (phase one), upon which a group hierarchical additive value-function model is built (phase two), supported by the MACBETH multicriteria decision-support system. Collaborative questioning protocols allow to elicit and reconcile individual qualitative value judgements, in a Web-Delphi platform. The collective knowledge thus acquired is used in decision conferencing with a core group to define value functions and weights. Extensive sensitivity and robustness analyses serve to test the requisiteness of the model, giving rise to a shared structure of composite indices. The proposed approach was shaped within the EURO-HEALTHY project, to create a Population Health Index intended to be a tool enabling policymakers to analyse population health and related inequalities at the regional level across the European Union, and design improvement policies. Many stakeholders and experts from different intervention and knowledge areas, and located across Europe, participated in the different modelling activities, as detailed in this article. Research paths derived from learnings acquired are suggested to further improve process effectiveness and flexibility.