Decision Method Research Articles

The Intelligent Decision-making and Collaboration Mechanism Design of the Soccer Robot System

The robot soccer competition integrates technologies such as vision, wireless communication, control, multi-sensor fusion, strategy, and simulation, with a focus on the intelligent soccer robot path decision-making subsystem. This paper briefly analyzes decision-making methods such as role allocation, time-region control, and self-tactics, with a primary focus on the top-down hierarchical reasoning model, emphasizing robot collaboration and reflecting the concept of multi-agent cooperation. During the implementation process, the code and functions of the decision subsystem are described in detail, and examples are provided to demonstrate how to achieve robot decision-making, tactical applications, and movement. Additionally, the effectiveness and performance of the system are verified through algorithm testing.

Evaluation of Motorcycle Brands Using Multi-attribute Decision-Making Method Under Single-Valued Neutrosophic Cubic Hypersoft Set Environment

The process of motorcycles evaluation is intricate and influenced by several factors that are uncertain, indeterminate and vague. It is difficult to estimate to what extent the choice will satisfy all requirements and change with the times. The theoretical frameworks such as hypersoft sets, interval and single-valued neutrosophic sets are useful in managing such vagueness and uncertainty. This study introduces a novel theoretical framework; the single-valued neutrosophic cubic hypersoft set (svNCHSS), to mitigate the loss of useful data. In comparison to the frameworks mentioned above, the svNCHSS is more adaptable in handling ambiguous and unpredictable circumstances because it incorporates all of their limitations as a single model. The set-theoretic operations like P-union (⊔P)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\sqcup _{P})$$\\end{document}, P-intersection (⊓P)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$( \\sqcap _{P})$$\\end{document}, R-union (⊔R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$( \\sqcup _{R})$$\\end{document}, R-intersection (⊓R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\sqcap _{R})$$\\end{document}, AND- and OR-operations are investigated for svNCHSS environment. Two potent algorithms are proposed in the context of a decision-assisted mechanism that employs AND- and OR-operations for the evaluation of motorbikes taking into account multi-argument-based factors. To evaluate the suggested algorithms’ effectiveness, a prototype case study is used for validation. The computed results are then compared. The structural comparison reveals the flexibility of svNCHSS, making its aggregation operations a useful tool for a variety of industrial applications, including risk assessment, process improvement, and decision-making.

Management excellence practices in small- and medium-sized industries: proposition of an evaluation approach and application in an emerging country

PurposeThis paper proposes an evaluation approach for analyzing excellence management practices at a regional level based on experts’ assessment.Design/methodology/approachUsing the National Quality Foundation’s Management Excellence Model (MEG), 19 management excellence practices were identified and tailored for small- and medium-sized industries (SMIs). Data were collected through a survey with 25 experts who assessed these practices in the context of Brazilian manufacturing SMIs. The analysis utilized Fuzzy TOPSIS Class (FTC), and a sensitivity analysis was also conducted.FindingsThe proposed evaluation approach integrates a reference model for management excellence (MEG), an analysis method incorporating uncertainty (FTC), and results that enable decision-makers to make well-informed choices. Of the 19 practices assessed, 15 were classified as “unacceptable,” while four were rated as “regular,” and none as “expected.” Key challenges relate to practices in information security, knowledge, innovation and change management.Research limitations/implicationsThe study’s primary limitation is the use of non-probabilistic sampling and its focus on the Brazilian context. However, the extensive knowledge and experience of the experts in manufacturing SMIs and the need for studies addressing specific regional contexts should be noted.Practical implicationsThe findings reveal a significant gap between ideal management practices and the actual state of SMIs, especially in emerging markets.Originality/valueThis research introduces a novel approach that combines a reference model tailored for SMIs with a multicriteria decision-making method, offering valuable insights for decision-makers and guiding future research.

Optimal Routing of Gas Pipelines in Seismic Regions Using an Efficient Decision-Support Tool: A Case Study in Northern Greece

High-pressure gas pipelines are significantly vulnerable to earthquake-related geohazards (tectonic faulting, slope instabilities, and/or soil liquefaction phenomena). Avoiding geohazardous areas is not always techno-economically feasible, as it would increase the length and cost of the infrastructure. Conversely, crossing these areas may adversely affect the structural performance of the pipeline, leading to unfeasible mitigation measures. Thus, selecting cost-effective, safe, and resilient routing is crucial. This study presents a GIS-based decision-support tool for optimal routing, taking into account, among other criteria, earthquake-related geohazards. The proposed tool considers not only the aforementioned but also more complex earthquake-related geohazards, such as secondary fault ruptures that are non-parallel or even perpendicular to the main fault, which might have been overlooked during the design of existing pipelines. To validate its effectiveness, the present tool is applied in a real case study in northern Greece, where the aforementioned earthquake-related geohazards coexist. Through a GIS-based multi-criteria decision method, various scenarios are examined by assigning different weights to the adopted criteria, and several cost-minimized routes are derived. This tool could be highly beneficial for the pipeline industry since it can assist operators and stakeholders in selecting the optimal pipeline route in geohazardous areas.

Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks

Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce Neon (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles of comparable performance. We showcase the utility of this method for sequential decision-making by examining the problem of composite Bayesian Optimization (BO), where we aim to optimize a function f=g∘h, where h:X→C(Y,Rds) is an unknown map which outputs elements of a function space, and g:C(Y,Rds)→R is a known and cheap-to-compute functional. By comparing our approach to other state-of-the-art methods on toy and real world scenarios, we demonstrate that Neon achieves state-of-the-art performance while requiring orders of magnitude less trainable parameters.

Analisis Produk Terlaris Menggunakan Metode K-Means Clustering Pada “Toko Hartati”

This research aims to analyze the best-selling products at Toko Hartati using the K-Means Clustering method. K-Means Clustering is an unsupervised learning algorithm that is effective in grouping data based on certain similar characteristics. In this context, the data used includes the number of sales, product prices, and product categories. Through this analysis, it is hoped that insight can be gained regarding products that have the best sales performance, as well as sales patterns that can be used as a reference in stock management and marketing strategies. The data used in this research includes sales transactions during a certain period, with the aim of identifying product clusters based on sales patterns. The analysis results show the existence of two main product groups, where the first cluster contains products with high sales numbers, which can be classified as best-selling products, while the second cluster includes products with lower sales. These findings provide valuable information for the management of Toko Hartati in determining more targeted marketing strategies and more efficient stock management. This research suggests using the K-Means Clustering method in data-based decision making to improve sales performance in retail stores.

Prioritizing Watersheds for Intervention Design Using GIS and Remote Sensing

In many developing countries with poorly managed landscapes, soil erosion threatens the sustainability of water bodies. The main limitations of this study are the lack of daily sediment data, lithology, higher-resolution DEM data, and socioeconomic factors. Poor land use policy and resource management in the Upper Awash Sub-basin lead to soil erosion and sedimentation of hydrological infrastructure, Effective watershed prioritization requires integrating land use, hydrology, sediment load, and morphometric factors but often faces gaps, especially in the study area. This research aims to prioritise the Upper Awash Sub-Basin by its morphometric, land use and cover (LULC), and sediment yield characteristics. We used the integrated AHP-VIKOR multi-attribute decision-making method to prioritise watersheds, incorporating morphometry, LULC, and sediment load attributes in the simple matrix approach. The findings showed the following classes of erosion: exceedingly high (2722.14 km2), high (2524.46 km2), moderate (2205.48 km2), low (1611.43 km2), and extremely low (854.35 km2). Sub-watersheds WS6, WS8, WS10, WS13, and WS24 are the top priority for watershed management. The study ranked watersheds based on various attributes but encountered limitations such as the lack of daily sediment data, geological structure, and lithology. It can be concluded that this approach is very important to identify and categorize hotspots of soil erosion sub-watersheds for planners and decision-makers for conserving water and soil and for different environmental management purposes.

Multi-Criteria selection of Metals-based electrocatalysts for electrochemical ammonia synthesis

The present study employed the Analytic Hierarchy Process (AHP), a renowned method for multi-criteria decision-making, to prioritize electrocatalysts for ammonia synthesis. This process considered the merits and demerits of both noble and non-noble metals electrocatalysts. The evaluation of seven parameters, including economic, lifetime, yield, selectivity, Faradaic efficiency (FE) and potentiometry, determines the justification of alternatives. In addition, there are seven types of electrocatalysts obtained from noble metals electrocatalysts, including Au, Pt, Ru, Rh, Pd, Ir, and Ag, as well as transition metal oxide (TMO), transition metal nitride (TMN), transition metal carbide (TMCA), transition metal chalcogenide (TMc), and other transition metal-based compounds (OTMB), and single atoms (SAs), derived from non-noble metals electrocatalysts, were considered as viable alternatives in light of the objective. According to the results of the multi-criteria evaluation, the criteria of yield, economic value, and lifetime had global weights of 0.306, 0.231, and 0.172, respectively, indicating their significance. Moreover, based on the criteria, Ru with a global weight of 0.189 and non-nobel metals SAs with a global weight of 0.214 are considered the most favored electrocatalysts for ammonia synthesis compared to both noble and non-noble electrocatalysts. The decision-making process considers economic justification (price), reaction yield, lifetime, FE, selectivity and potential as essential criteria, respectively. Ultimately, by closely considering the outcomes of individual atoms, the problems encountered by each catalyst can be effectively addressed, leading to impressive performance.

Review on land suitability assessment and site selection for housing development

Land suitability analysis using the weighted overlay method is rational for urban decision-making. The main objective of this review is to identify the methods of proposing/producing suitable areas and to explore the criteria used for housing development. To select the best suitable land for housing development, different researchers apply a multi-criteria decision-making method through the AHP technique. As a result, suitability maps were usually produced. Hence, the distance far from the infrastructures and near from hazard areas were unsuitable for housing development. Land suitability assessment is vital for the good social well-being of the community.

A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare

Developing smart cities is vital for ensuring sustainable development and improving human well-being. One critical aspect of building smart cities is designing intelligent methods to address various decision-making problems that arise in urban areas. As machine learning techniques continue to advance rapidly, a growing body of research has been focused on utilizing these methods to achieve intelligent urban decision making. In this survey, we conduct a systematic literature review on the application of machine learning methods in urban decision making, with a focus on planning, transportation, and healthcare. First, we provide a taxonomy based on typical applications of machine learning methods for urban decision making. We then present background knowledge on these tasks and the machine learning techniques that have been adopted to solve them. Next, we examine the challenges and advantages of applying machine learning in urban decision making, including issues related to urban complexity, urban heterogeneity and computational cost. Afterward and primarily, we elaborate on the existing machine learning methods that aim to solve urban decision making tasks in planning, transportation, and healthcare, highlighting their strengths and limitations. Finally, we discuss open problems and the future directions of applying machine learning to enable intelligent urban decision making, such as developing foundation models and combining reinforcement learning algorithms with human feedback. We hope this survey can help researchers in related fields understand the recent progress made in existing works, and inspire novel applications of machine learning in smart cities.

Analyzing the Development of Capital Budgeting Methods in Investment Decision Making: A Systematic Literature Review

The purpose of this study is to analyze the development of capital budgeting methods in investment decision-making, focusing on the application and evaluation of various methods used in an increasingly complex and dynamic context. This research uses a literature review approach, where the researcher gathers references that discuss the same theme. The literature sources used were obtained from various journal publications with a focus on works published between 2019 and 2024. This approach aims to explore the latest insights regarding the concepts and applications of capital budgeting in relevant contexts. This study indicates that the development of capital budgeting methods in investment decision-making increasingly reflects the complexity and need to adapt to various external factors. In addition to financial aspects, social value and the strategic goals of a company have also become important considerations in evaluating the feasibility of a project. Methods such as NPV, IRR, and SROI are increasingly used to provide a more comprehensive picture of the financial and social impacts of investments. Therefore, capital budgeting continues to evolve to support sustainability and create value for stakeholders in an increasingly dynamic context.

Low-carbon consumption in extreme heat in eastern China: Climate change anxiety as a facilitator or inhibitor?

With the planet warming, extreme heat events are becoming increasingly frequent and hazardous. At the same time, reducing carbon emissions from consumer activities is crucial for addressing global warming. This study aims to explore how extreme heat affects low-carbon consumption behaviors (LCB), leading to a more comprehensive understanding of the relationship between climate change and human behavior. Guided by resource scarcity theory and the general strain theory. The laboratory experiment used the recall paradigm to induce extreme heat experiences (Study 1, n = 198), and we found that extreme heat impacts LCB through climate change anxiety: cognitive climate change anxiety tends to increase low-carbon behaviors, while affective climate change anxiety inhibits low-carbon behaviors. The field experiments conducted on actual extreme heat days with simulated purchasing decision-making methods (Study 2, n = 203) further confirmed the laboratory findings. An inverted U-shaped relationship between climate change anxiety and LCB was also revealed. In summary, the current study reveals the differential impact of cognitive and affective climate anxiety on LCB. This finding provides new insights for understanding the complex psychological mechanisms of extreme climate events on consumer behavior, which could also serve as an operational basis for intervention design and policy making.

A hybrid opinion dynamics model with leaders and followers fusing dynamic social networks in large-scale group decision-making

Objectives:In this study, our goal is to enhance consensus efficiency in complex decision-making scenarios by constructing a large-scale group decision-making (LSGDM) method that integrates dynamic social network (DSN) and opinion dynamics. To this end, we design a model that can effectively cluster experts and dynamically adjust the network structure to more accurately reflect the diversity and complexity of the actual decision-making process. Methods:Specifically, we first design an improved Louvain algorithm based on social influence to effectively cluster participants with similar opinions into the same community. Then, we utilize structural hole theory to distinguish opinion leaders and followers in the community, and construct a DSN updating mechanism based on opinion disagreement and trust relationship. Finally, we combine the advantages of the DeGroot and Hegselmann–Krause (HK) models and propose a hybrid opinion dynamics (HOD) model in the LSGDM framework, referred to as DSN-HOD-LSGDM. Findings:Experimental results demonstrate that the DSN-HOD-LSGDM model significantly enhances consensus-building efficiency across diverse decision-making communities. The model effectively tracks opinion evolution in complex networks, outperforming conventional methods in both adaptability and scalability. Novelty:In this study, we propose an improved Louvain algorithm and dynamic weight allocation mechanism based on influence index, and design a personalized opinion evolution mechanism combined with structural hole theory. By fusing opinion evolution and dynamic trust, we construct a new LSGDM consensus model that realizes the dynamic adjustment of the trust relationship between individuals.

Democratic Innovations and Political Participation. The Case of the Estonian Citizens’ Assembly (Rahvakogu)

The aim of this article is to examine the impact of the Estonian Citizens’ Assembly, convened in 2013, on the evolution of political participation in the country. This is a significant topic, given that Estonia offers relatively few opportunities for participation between parliamentary elections. The article presents a verification of the hypothesis, assuming that the Estonian Citizens’ Assembly has not resulted in notable alterations to the existing Estonian legislation, particularly with regard to the expansion of the catalogue of civic participation, both in the immediate and extended terms. The research presented in the article was conducted through the application of the decision-making method and an analysis of legal acts. The article is divided into three parts: the first part analyses the main issues related to the phenomenon of democratic innovation, including definitional issues and the process of its development; the second part locates the main circumstances of the convening of the Assembly, primarily in the context of the crisis of Estonian representative democracy caused by the “Silvergate” scandal. The procedure and stages of the holding of the Estonian Citizens’ Assembly were also presented. The third part was devoted to the results achieved and their impact on political participation in Estonia. The research findings permit the conclusion that the Estonian Citizens’ Assembly can be included in the catalogue of democratic innovations. This is due to the fact that it was an instrument for conferring a new role on citizens in the co-governance process, thereby creating new opportunities for political participation, decision-making and influence over representative democracy. Conversely, it is not feasible to assert that the Estonian Citizens’ Assembly has exerted a profound impact on the legal system, particularly in the context of introducing novel participatory instruments.

A fuzzy decision-making network model for offshore wind turbine selection based on simulated annealing algorithm

As global demand for renewable energy surges, offshore wind power has become an indispensable component in the transition towards a more sustainable energy landscape. Selecting appropriate wind turbines for offshore wind power facilities is a critical process influenced by various factors, including turbine parameters, environmental conditions, and economic indicators. Traditional decision-making methods often fall short in addressing the complexity and dynamic nature of real-world scenarios, relying heavily on expert experience. This study proposes a novel and scalable method for offshore wind turbine selection by leveraging a fuzzy decision-making network and a conditional probability table optimized through a simulated annealing algorithm. The method is structured into three key modules. It allows for learning from existing similar cases and transferring knowledge to the current selection task in the absence of expert opinion. It has proven its worth in a case study at the Rudong Wind Farm in Jiangsu Province, China. The results show that this method can account for the complexities inherent in turbine selection and serve as a more comprehensive and adaptable tool for decision-makers. This study contributes to the renewable energy sector by providing a novel, data-driven, and practical method for offshore wind turbine selection and other real-world related applications.

A Combined Compromise Decision Method with Large-Scale and Multi-granularity Probabilistic Linguistic Information

A Combined Compromise Decision Method with Large-Scale and Multi-granularity Probabilistic Linguistic Information

A Z-number-based three-way decision method with classification-based state determination for the evaluation of new energy enterprises

New energy enterprises are important promoting factors for sustainable development of modern society. Limited budgets of governments require that new energy enterprises should be efficiently evaluated before they are founded. Existing evaluation methods ignored the hesitation of experts to some alternatives. Although the three-way decision method has been applied widely as a method to evaluate alternatives, the determination of the state set has not been deeply discussed. To solve these challenges, this paper proposes a Z-number-based three-way decision method with classification-based state determination, which can assign alternatives with hesitation to a boundary region for further consideration and compute the conditional probability with a classification-based method. First, since traditional fuzzy sets cannot ensure the reliability of decision information, an evaluation matrix based on Z-numbers is constructed. Second, a fuzzy best-worst method is applied to determine the weights of criteria. Third, the conditional probability is computed based on classification-based state sets that are obtained by a sorting method. An example regarding the evaluation and selection of new energy enterprises demonstrates the validity and stability of the proposed method. The comparison analysis shows that our proposed method can divide alternatives into different regions efficiently and is less affected by the variation of parameters.

Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content

The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content (LWC). Through the relationship between soil water content (SWC) and LWC, the optimal irrigation amounts at the filling stage are determined. We utilized two-year field irrigation experiments (2022–2023). The successive projection algorithm (SPA) was applied to select sensitive bands of LWC. Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. The SPA-RF model was found to be the most effective, with determination coefficients (R²) of 0.95 and 0.96, root mean square errors (RMSE) of 3.00 % and 2.70 %, and normalized root mean square errors (NRMSE) of 6.47 % and 6.01 %, respectively. The SPA algorithm also improved the inversion efficiency of LWC. A significant positive correlation between SWC and LWC during the filling stage was observed, and a conversion model was developed for the pre-, mid-, and late-filling stages. The R² values for pre-, mid-, and late-filling stages were 0.75, 0.80, and 0.73, respectively, with corresponding RMSE values of 28.79 m³/ha 17.26 m³/ha, and 37.35 m³/ha. The results indicate a high consistency between the SWC estimated via hyperspectral inversion and the irrigation quota based on measured SWC, making the proposed method a valuable tool for optimizing irrigation during this critical growth phase. The method for estimating irrigation amounts during the filling stage, based on UAV hyperspectral imagery proposed in this study, offers valuable support for achieving precise irrigation decisions for winter wheat.

Application of hybrid multi-criteria decision-making method to the evaluation of corporate social responsibility: An example of the banking industry in China

Corporate social responsibility (CSR) is a popular research topic, but there have not been comprehensive discussions on CSR evaluation in the banking sector. The purpose of this study is to propose a two-stage analysis combining the modified Delphi method (MDM) and Analytic Network Process (ANP) in order to construct a model for evaluating banks’ CSR. First, we use MDM to select and determine the interdependence of the criteria and then employ ANP to obtain their weights and to rank the alternatives. The results show that 5 criteria and 18 sub-criteria need to be considered in CSR evaluation. The most important criterion and sub-criterion are bank governance and regulatory compliance, respectively. The evaluation model constructed herein can be taken as a decision-making guide for evaluating banking organizations’ CSR and to help promote CSR development in China’s financial industry.

Application of multi attribute decision making methods for the selection of throttling valve in refrigeration system

The throttling valve is an essential component of any refrigeration system and its proper functioning is crucial for the successful operation of the refrigeration cycle. Its primary function is to expand the pressurized hot refrigerant and convert it into low-pressure cold refrigerant while maintaining a constant enthalpy and controlling the flow rate of the refrigerant. However, selecting the appropriate throttling valve is challenging, and a wrong decision can harm the system's performance. To overcome this challenge, decision-makers rely on multi-attribute decision-making methods to select the best throttle valve from a wide range of high-quality options available in the market. In this study, the three selected decision-making methods viz: analytical hierarchy process, technique of order preference by similarity to ideal solution and preference selection index are used for ranking appropriate throttle valves. The seven alternatives and seven attributes are considered to determine the best ranks. The results are compared with solutions obtained using selected methods, and it is observed that the QCX-2034BUC throttling valve has been determined to be the best choice, and the QCX-1534BUC is the last choice. The results of the investigation can potentially enhance the process and performance of refrigeration systems. Thus, the present study assists decision-makers in evaluating and ranking the best solution for their refrigeration system.