Multi-criteria Decision Support Research Articles

Decision process for industry 5.0

ABSTRACT Industry 5.0 is an emerging paradigm that enhances the established Industry 4.0. Automation, digitalisation, big data, and artificial intelligence are becoming complemented by human-centricity, active cooperation between people and machines, and decision-making based on human expertise. This research introduces a generic decision-making process for Industry 5.0. It defines the underlying principles to incorporate multi-criteria decision models, decision procedures, and decision support technology into business and production processes for Industry 5.0. We justify the motivation to aggregate holistic objectivedecisions, which are inferred automatically from big data, with subjective judgements and knowledge of human decision-makers to guarantee unbiased actions. We draw parallels with the aggregation-disaggregation paradigm and constructive learning to address the human-centric specification of preferences in cooperation with technology-driven entities. We assess the relevance and efficiency of decision support for Industry 5.0 with a SWOT analysis. Finally, we apply the introduced generic process to the use case of the flexible electricity supply market.

A Research Challenging Vision Concerning Waste of Agricultural Management in a Bio-Based Circular Economy: A Review Article

Agricultural waste represents an enormous reservoir of underutilized biomass resources, which may even pose environmental and economic risks. Residual resources of this nature can be transformed into bioenergy and bio-based products through cascading conversion processes, thereby fitting the criteria of a circular economy. Significant challenges are examined through a transdisciplinary lens, with an emphasis on the European context. Due to the seasonality, regionality, and complexity of agricultural residue management chains, environmental and economic repercussions are challenging to quantify. It is discussed how to develop multi-criteria decision support instruments that can be implemented in the earliest phases of research. The technological advancement of Anaerobic Digestion (AD), a highly developed conversion technology, is examined in the context of seasonal and geographical variations in refuse feedstock. Utilizing agricultural byproducts to manufacture high-value compounds is a significant challenge that is examined in this article, with innovative cascading conversion processes that are both eco-efficient and cost-effective (bio-refinery concept) taken into consideration. Furthermore, industrial ecology examines the promotion of businesses based on agricultural residues in order to foster local synergy between various industrial and agricultural value chains. In order to optimize the management of materials and knowledge fluxes and facilitate a holistic approach, the connection of stakeholders to encourage resource exchange and cross-sector collaboration at appropriate geographic scales is discussed.

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

Groundwater potential zone mapping of middle Andaman using multi-criteria decision-making and support vector machine

Groundwater is a vital resource crucial to human survival worldwide and plays a significant role in addressing climate change. The multi-criteria decision method and support vector machine technique were used to demarcate the groundwater potential zones (GWPZ) of Middle Andaman. A total of 11 factors including geology, geomorphology, LULC map, lineament density, distance from Active Fault, groundwater level, distance from the sea, slope, drainage density, distance from major earthquakes, and distance from epicenter hotspots were used to calculate GWPZ. Spatially distributed maps/thematic layers of all of the above factors were generated using remotely sensed data as well as ground data. Each factor was assigned weights based on preceding global and regional research and the influence of each layer on groundwater. Finally, all thematic layers were used for multi-criteria decision analysis to create a map depicting the GWPZ of Middle Andaman. A support vector machine (SVM) tool was used to demarcate the potential zone based on the signature obtained from overlay analysis. The resulting map was divided into four categories: very high, high, low, and very low groundwater potential zones. SVM classification result showed that most of Middle Andaman's areas come under the low to very low GWPZ (63.4%), followed by high GWPZ (27%), and 10.6% of very high GWPZ. SVM classification resulted in overall accuracy, kappa coefficient, and the area in the ROC curve of 91.66%, 0.87, and 0.64 respectively. The analysis revealed a correlation between each factor and the Multi-Criteria Decision Method (MCDM) and Support Vector Machine (SVM), indicating that the majority of geological and geomorphological features are linked to lower groundwater potential. Further, the result was compared with groundwater fluctuation data of the middle Andaman. Finally, this study provides an expedient approach to delineating the GWPZ for practical management and use of the area's groundwater resources to maintain the island's sustainability.

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.

Multicriteria-decision support for master plan scheduling: urban regeneration of an industrial area in Northern Italy

Urban regeneration programs involve a series of intricate activities that utilize human, material, and financial resources, executed at varying times and in diverse manners. Within this complex scenario, a major challenge lies in effectively managing concurrent developments across multiple buildings and infrastructure projects, while also harmonizing the interests of all parties engaged in urban regeneration initiatives. This paper introduces a multi-step strategy designed to enhance and streamline urban management amid the multifaceted decision-making processes characteristic of urban regeneration efforts. Specifically, the multi-criteria decision-making model ELECTRE TRI-B is employed to sort the array of basic tasks within a complex transformation endeavor into priority groups based on various evaluative criteria. To ascertain the significance of these criteria and rank each task according to the interests of the stakeholders likely to be impacted, a panel of specialists, including construction experts, economists, and environmental assessors, is convened. Following this, project actions are organized in a Gantt chart reflecting the construction priority determined by the evaluation model. This approach facilitated the temporal organization of master plan activities, incorporating insights from a broad spectrum of experts including municipal technical staff, professionals, and the community at large. The findings indicate that this comprehensive and interactive approach to performance measurement enabled the handling of extensive datasets and the refinement of financial, economic, environmental, and social resources, all while adhering to logical constraints and urban policy demands.

Multi-criteria decision support model for selecting the appropriate construction management firm

Construction management (CM) companies serve as representatives for clients, aiding them in overseeing construction projects throughout the project’s entire life cycle. Opting for the right CM firm enhances the likelihood of a successful project completion. Existing research has highlighted a lack of decision support models for CM firms. This study introduces a multi-criteria decision support model employing the Analytic Hierarchy Process (AHP) for the selection of suitable Construction Management firms. The primary objective of this model is to guide decision-makers in choosing the most suitable CM firm, ensuring that the provider offers the necessary services from project initiation to closure. Additionally, this research seeks to standardize the CM firm selection process based on key criteria, which were identified through a literature review. The significance of these criteria was assessed via a survey distributed to construction professionals in the United Arab Emirates (UAE). The survey collected feedback by having respondents make pairwise comparisons of the identified criteria. The data, consisting of 52 survey responses, were analysed using Expert Choice software and utilized to construct the AHP-based model. The findings reveal that the most crucial factors for CM firm selection are prior experience in similar projects, financial stability, proficiency in Building Information Modelling (BIM), the qualifications and capabilities of key staff members, ongoing project experience, and expertise in risk management. The study also presents a four-stage framework that decision-makers can follow when selecting the appropriate CM firm, and it includes a case study to illustrate the application of the proposed model.

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%.

BopSkyline: Boosting privacy-preserving skyline query service in the cloud

With the widespread adoption of cloud computing, there has been great popularity of storing and querying databases in the cloud. However, such service outsourcing also entails critical data privacy concerns, as the cloud providers are generally not in the same trust domain as the data owners/users and could even suffer from data breaches. In this paper, different from most existing works that propose security designs for keyword search, we focus on secure realizations of advanced skyline query processing, which plays an important role in multi-criteria decision support applications. We propose BopSkyline, a new system framework for privacy-preserving skyline query service in cloud computing. BopSkyline is designed to not only ensure the confidentiality of outsourced databases, skyline queries, and query results, but also conceal data patterns (like the dominance relationships among database tuples) and search access patterns that may indirectly lead to data leakages. Notably, through a delicate synergy of key ideas on secure database shuffling and differentially private database padding, BopSkyline achieves a significant performance boost over the state-of-the-art. Extensive experiments demonstrate that compared with the state-of-the-art prior work, BopSkyline is up to 4.7× better in query latency and achieves up to 99.38% cost savings in communication.

Multi-criteria decision support model for material and supplier selection in the construction industry

Existing material and supplier selection practices in the construction industry are time-consuming and error-prone due to reliance on human judgment and personal communication with a limited number of suppliers. To address this problem, the present paper focuses on developing a new Material and Supplier Selection Model (MSSM) to identify the best selection of construction materials and suppliers based on: (1) unique characteristics of construction projects such as project location, quantity of work, and material/product specifications; (2) characteristics of suppliers and vendors such as price, delivery time, and quality; and (3) characteristics of materials such as performance, and environmental impacts. The model is integrated with updatable databases of materials and suppliers that include: (1) suppliers’ data such as name, location, and lead time; and (2) materials data such as model, specifications, and costs. The developed MSSM is implemented using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method due to its capability of executing computations for problems containing both qualitative and quantitative criteria. An example project for material and supplier selection is used to demonstrate the MSSM use and document its new capabilities. The present model is expected to support contractors, construction managers, and decision-makers in selecting the best suppliers and materials based on specified criteria such as cost, time, and environmental impacts.

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.

Multi-criteria decision support models under fuzzy credibility rough numbers and their application in green supply selection

As the increasing environmental issues, various companies have take initiatives to produce green products or to select green suppliers which maximize the business performance and minimize the environmental pollution. The real numbers data have imbiguity and uncertainty due to described by classical tools. Therefore, we consider a new type of fuzzy set, fuzzy credibility rough sets. In fuzzy credibility rough set has credibility membership of positive membership and they reduced the imbiguity in data information. In this paper we have defined a new set called fuzzy credibility rough set (FCRS), after that we defined Frank operational laws for FCRS information. Using these operational laws, we defined a series of aggregation operators that is fuzzy credibility Frank rough weigthed averaging aggregation operators, fuzzy credibility Frank rough ordered weigthed averaging aggregation operators, fuzzy credibility Frank rough hybrid weigthed averaging aggregation operators and its basic properties like boundedness, monotonicity and idempotency.As there is no work which is based on Frank norms aggregation operators under FCRS information. So, we defined a series of aggregation operators that can help us to collect the data for various green suppliers management.We developed a new set called fuzzy credibility rough set (FCRS). We developed a new Frank norms operational laws under FCRS information. We developed a series of aggregation operators. We developed and extend various steps of GRA, VIKOR and TOPSIS method under FCRS information. We explained the application of our proposed work to a real life decision making problem (green supplier management).All the proposed work is to applied to real life decision making problems (green supplier management) to find the best optimal result. Firstly we can collect the data from the decision makers using the proposed aggregation operators and then we applied all the steps of developed method to find the solution in case of green supplier management.

Technological innovations in the LNG sector: A review: Assessing recent advancements and their impact on LNG production, transportation and usage

This paper thoroughly explores the recent technological innovations in the Liquefied Natural Gas (LNG) sector, examining their impact on production, transportation, storage, and usage. The study delves into the evolution of the global LNG market, emphasizing the critical role of technological advancements in shaping the industry's future. The aim is to analyze these innovations, assess their environmental and economic impacts, and explore the influence of geopolitical and market dynamics. By employing a comprehensive literature review, the paper utilizes a multi-criteria decision support framework to select relevant technological innovations and assess their impacts. This methodology integrates environmental, social, policy, and economic dimensions, offering a holistic view of the LNG sector's technological landscape. The findings reveal significant advancements in LNG production technologies, such as green ammonia and biogas, enhancing sustainability and efficiency. Innovations in LNG transportation, including optimized carrier fleets and storage systems, have revolutionized the industry, improving economic viability and safety. The emergence of Floating LNG (FLNG) technologies marks a pivotal development, unlocking new gas monetization possibilities. The study also highlights the environmental benefits of these innovations, including reduced greenhouse gas emissions and their synergy with renewable energy systems. The paper concludes that while technological advancements offer substantial benefits, they also present challenges, including high investment costs and regulatory complexities. It recommends continued innovation, strategic collaborations, and supportive regulatory frameworks to fully exploit the potential of LNG technologies. As the global energy landscape evolves, the LNG sector is poised to play a crucial role in the transition to a more sustainable and efficient energy future.

A Multi-Criteria Decision Support Model for Restaurant Selection Based on Users' Demand Level: The Case of Dianping.com

The Internet, by offering a variety of information sources such as online reviews, aids people in selecting restaurants. However, it also prolongs their decision-making process due to the need to integrate information across multiple criteria. Existing decision support models for choosing satisfactory restaurants overlook users' varying demand levels for each aspect of the restaurant, making the process less efficient. This paper aims to develop a multi-criteria decision support model for users to efficiently and accurately rank and select restaurants based on their demand level for various restaurant aspects. The 2-tuple linguistic ordered weighted averaging (2LOWA) aggregation operator is applied for the first time to aggregate user ratings, generating linguistic ratings that mirror the diverse levels of user demand for restaurant service, food, and environment. The importance weights (IW) method is introduced to calculate linguistic weights, thereby obtaining customized 2T-SFE composite scores under various user demand scenarios. The proposed model's applicability is demonstrated using a dataset comprising over 3.7 million reviews sourced from Dianping.com. The results show multiple personalized restaurant rankings with more linguistically understandable composite scores, enabling users to efficiently choose a suitable restaurant based on their preferences and requirements. Moreover, a list of restaurants satisfying most users with different demand levels can be generated by assessing their frequency of appearance in the top 10 restaurants across over 340 scenarios established by the proposed model. This contributes to offering more reliable and comprehensive restaurant recommendations and rankings, ultimately increasing customer satisfaction in the selection process.

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

Recent advances in multi-criteria decision analysis: A comprehensive review of applications and trends

In recent years, multi-criteria decision support methods have become widely used research tools by both scientists and practitioners. Theoretical works involving new multi-criteria methods and developments of existing methods immediately find applications in areas of business practice. This paper attempts to sort out the areas of application of MCDA methods. For this purpose, the relevant literature from 2018–2023 was reviewed. The inclusion selection criteria were defined as addressing multi-criteria practical issues, open accessibility, comprehensive research methodology and findings, and the use of an MCDA method in the evaluation process. Detailed research identified that the main areas of use of MCDA methods are healthcare, energy management, supplier selection, and transportation. Due to the timeliness and importance of the problem, a detailed study of the use of MCDA methods in sustainability issues was separated. The paper contributes contributions to both theory and economic practice. The article provides a series of recommendations both on the methodological side of the problems to be solved and shows the practical prism of individual decision models, offering ready-to-use decision models, and providing a comprehensive review of recent MCDA advancements, fostering informed and reliable decision-making. Additionally, this review sheds light on the latest developments in MCDA, emphasizing trends in Decision Support Systems (DSS) and prominent application areas in contemporary research.

Interpreting the results of InterCriteria Analysis: Pareto principle at work

The present short note aims to propose a new, alternative, way to interpret the results of the intuitionistic fuzzy sets-based method for multicriteria decision support named InterCriteria Analysis. Given an <math>m \times n</math> dataset of multiple (''m'') objects evaluated numerically against multiple (''n'') criteria, the ICA method generates an <math>n \times n</math> table of intuitionistic fuzzy pairs <math>\langle \mu_{i,j}, \nu_{i,j} \rangle, \ i, j \in {1, 2, \ldots, n}</math> where the given pair indicates the extent of relation between the corresponding pair of criteria <math>C_i, C_j</math>. Traditionally, the interpretation of these intuitionistic fuzzy pairs regarding the extent of positive or negative dependence between two criteria (or, respectively, the lack of such) requires that two threshold values, both in the [0,1] interval too, are used. Now we propose to use only one such threshold value belonging to the [0,1] interval, for instance a minimal threshold of the degree of membership, while the other threshold {would} be essentially related to the size of the subset of intercriteria pairs being shortlisted for interpretation, rather than their degree of non-membership. We justify that the proposed approach, inspired by the Pareto Principle, in certain cases yields better results than the traditionally used one..

Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings

This paper proposes a novel multi-criteria decision support framework for climate change-sensitive evaluation of building thermal comfort performance in European buildings. The proposed framework considers various comfort categories based on building use and condition, comfort models based on building operation, sensitivity to climate change, key performance indicators, and their thresholds. The proposed framework is then implemented using a passive house-certified high-performance timber dwelling near Brussels as a case study. The thermal comfort in the free-running reference timber dwelling is assessed using a whole-building energy performance simulation model with a static threshold of 26°C for the bedrooms and an adaptive threshold for the other occupied zones. The analysis found an increase in indoor overheating degree by 1.7°C and ambient warmness degree by 6.5°C during heat waves from the current scenario to the end of the century scenario. However, the reference timber dwelling effectively suppressed the impacts of climate change with varying degrees of success towards the end of the century. The proposed framework is intended to support decision-makers in effectively evaluating performance during the early stages of building design. The study highlights the need for further research on building performance assessment techniques and guidelines in a changing climate.

Multi-criteria decision support methods for the selection of the optimum contractor: A case study of a passenger transfer centre

Purpose: The main purpose of the paper is to apply the TOPSIS method for selecting the optimal contractor for a passenger transfer center.
 Methodology: The methodology is based on the use of TOPSIS, which determines the distance of decision alternatives from the ideal and anti-ideal solutions using evaluation criteria.
 Results: Two different contractor rankings were obtained depending on the weights of the criteria. With equal weights, the best contractor is number 5, while with weights determined by formula (8), the best is number 4.
 Theoretical Contribution: The paper contributes to the field of investment project management by demonstrating how the TOPSIS method can aid in decision-making for optimal contractor selection, thereby reducing the risk of erroneous decisions.
 Practical Implications: The practical implications of this research are significant for investment projects, as the demonstrated methodology can be directly applied to the contractor selection process, potentially leading to more successful project outcomes.