Decision Method Research Articles

A multi-phase lane change decision-making method for autonomous vehicles

A multi-phase lane change decision-making method for autonomous vehicles

PSI-SAW and PSI-MARCOS Hybrid MCDM Methods

This paper presents a study on the hybridization of Multi-Criteria Decision-Making (MCDM) methods: Preference Selection Index (PSI), Simple Additive Weighting (SAW), and Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The hybridization was conducted between the PSI and the other two methods, resulting in new methods, namely PSI-SAW and PSI-MARCOS. For each specific problem, applying these two hybrid methods to rank alternatives among the available options produces three different sets of rankings: one created by PSI, one by the hybrid PSI-SAW, and one by the hybrid PSI-MARCOS. The accuracy of the proposed models was tested in three different cases. The test results show that both proposed models exhibit high accuracy. This study provides users with highly accurate and useful methods for MCDM.

An integrated multi-attributive border approximation area comparison (MABAC) method for evaluating resilience and knowledge sharing of suppliers in pythagorean fuzzy environment

This paper aims to determine the resilience scores of suppliers by considering the importance of knowledge sharing and incorporating a set of criteria that affect the supply chain’s resilience, along with knowledge sharing. In order to achieve this goal, we propose a new multi-criteria decision-making (MCDM) method named MULTIMABAC to evaluate suppliers’ resilience. This method is based on the multi-attributive border approximation area comparison (MABAC) method. The new method consists of criteria weighting, expert weighting, and supplier evaluation. In the criteria weighting section, we extend the BWM (Best–Worst method) using Pythagorean fuzzy sets (PFSs). The weight of experts is determined by incorporating the average concept into the MABAC method. The resilience score of suppliers is calculated using an extended MABAC method with ideal positive and negative solutions. All indices are aggregated using the ordinal priority approach (OPA) to obtain a unique ranking. Additionally, we extend the MULTIMABAC method using PFSs to address uncertainty in supply chain decision-making. Results and sensitivity analysis demonstrate that the proposed method effectively facilitates knowledge sharing in selecting resilient suppliers. Therefore, organizations can use the proposed method to evaluate and choose resilient suppliers. Moreover, the MULTIMABAC method demonstrates robustness to variations in the weights of its constituent parameters.

A Model of Technological Inheritance for Methods of Technological Decision-Making in Manufacturing Operations in the Engine Industry

A Model of Technological Inheritance for Methods of Technological Decision-Making in Manufacturing Operations in the Engine Industry

Intelligent Emergency Assisted Decision-Making Method Based on Standard Digitalization: Hazardous Chemical Accidents in Industrial Parks

Contemporary society is confronted with multifaceted challenges, and the intricate interplay of interconnected factors significantly complicates emergency response efforts. Current practices rely on quick decisions by domain experts; however, the limitations of individual expertise and the urgency of crises hinder both precision and standardization. To address these issues, we propose a novel approach: an intelligent method for emergency decision making grounded in a standardized digital knowledge graph. First, our study examined the underlying theory of standardized digital transformation and event-chain evolution. This led to the construction of a knowledge graph encompassing standard emergency knowledge, as well as supplementary derivative data pertinent to event response. Second, through the application of semantic analysis and intention recognition of the decision target, coherent and interpretable query sentences for the decision system were crafted. These query sentences then served as a conduit for retrieving standard emergency knowledge relevant to the current emergency situation, as well as potential secondary disasters. The overarching goal is to provide emergency decision makers with effective support mechanisms that are both well informed and tailored to the specific demands of each situation.

Biobjective Optimization Method for Large-Scale Group Decision Making Based on Hesitant Fuzzy Linguistic Preference Relations With Granularity Levels

Biobjective Optimization Method for Large-Scale Group Decision Making Based on Hesitant Fuzzy Linguistic Preference Relations With Granularity Levels

Evaluation decision on green degree combination of typical welding processes for large-diameter thick plates

Abstract Aiming at the environmental impact of large-diameter thick plate manufacturing scenarios in the welding process, a comprehensive evaluation method combining AHP and TOPSIS is proposed to analyze and make decisions on the green welding process suitable for large-diameter thick plates. The optimal green welding process evaluation and decision-making index system for large-diameter thick plates is constructed. The system can be flexibly applied to different large-diameter plate welding scenarios by focusing on different optimization objectives, providing welders with a more suitable green welding process. The constructed model is applied to the seashore factory welding scenarios and general welding scenarios for comparative decision-making, and the effectiveness and feasibility of the proposed comprehensive decision-making evaluation method are verified by the actual data calculation results.

Multi-criteria decision-making model based on picture hesitant fuzzy soft set approach: An application of sustainable solar energy management

This study addresses the optimization of energy management within corporations to reduce expenditures and maximize profits. Focusing on proactive, coordinated, and systematic energy usage, the research emphasizes the importance of informed decisions through building management, energy audits, and equipment retrofits. The proposed concept of Picture Hesitant Fuzzy Soft Set (PHFSS) with Archimedean aggregation operators, featuring Einstein generators, is introduced. Various aggregation techniques, including PHFS weighted, weighted ordered, weighted geometric, ordered weighted geometric, and hybrid operators, are thoroughly examined. PHFSS is utilized to represent ambiguous information in decision-making processes. The study introduces a novel multi-criteria decision-making (MCDM) method to address the challenge of selecting optimal energy management sources, demonstrating its effectiveness through a large-scale numerical example. This approach surpasses the distance from average solution (EDAS) method in terms of effectiveness and reliability. The research contributes valuable insights into optimizing energy management, reducing costs, and mitigating environmental impact.

Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

The novel WASPAS method for roughness of bipolar fuzzy sets based bipolar fuzzy covering

The research employs Covering-based Rough Set and Fuzzy Set theories to handle uncertainty in data analysis. However, when dealing with data uncertainty and bipolarity in various scenarios, the Bipolar Fuzzy Set (BFS) theory proves advantageous by simultaneously managing positive and negative information. Other sets, such as traditional fuzzy sets, often fail to capture this duality, leading to less comprehensive data analysis. This study pioneers a new methodology called the Theory of Roughness of Bipolar Fuzzy Sets, integrating Fuzzy Covering, Monotone Fuzzy Covering, and Bipolar Fuzzy Covering to propose an innovative decision-making approach. This novel concept undergoes comprehensive structural analysis. By incorporating the bipolar fuzzy covering based bipolar fuzzy rough set ( BFCBFRS ) model into conventional decision-making methods like the WASPAS technique, the research introduces a fresh perspective to address Multi-Criteria Decision-Making ( MCDM ) challenges. The efficacy of this extended method is evaluated by applying it to agricultural diagnosis, demonstrating its superiority over existing approaches through comparative analysis.

Game-Theoretic Decision-Making Method and Motion Planning for Autonomous Vehicles in Overtaking

Game-Theoretic Decision-Making Method and Motion Planning for Autonomous Vehicles in Overtaking

Utilizing multi-criteria decision-making analysis and 3D visualization techniques for dam site selection and irrigation area identification in Gedeb River, Ethiopia

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

Optimization and ranking of the input parameter settings of sustainable grinding using cashew nut shell liquid as cutting fluid

The current work advocates the use of Cashew Nut Shell Liquid/Oil (CNSL), an oil extract of the leftover cashew nut shells, as a novel environment-friendly cutting fluid in sustainable machining operations. The tribological characteristics of CNSL obtained on a pin-on-disc tribometer are found to be better compared to the traditionally used cutting fluid. Experiments are conducted on the surface grinder with EN8 material, considering input parameters, such as cutting fluid type, grinder speed and grade, work speed, and depth of cut, with Surface Roughness (Ra) and Grinding Temperature (Temp) being the responses. Input parameter optimization is performed using Taguchi’s statistical models. A total of 36 investigative and six validation experiments are conducted, and a prediction model is proposed. When Ra and Temp are optimized simultaneously, the prediction value of Ra is 0.071 μm, and the corresponding value of Temp is 31.6 °C for which the experimental values are 0.072 μm and 32 °C respectively. This work also applies the TODIM (TOmada de Decisao Interativa Multicriterio, in the Portuguese language), a multi-attribute decision-making method for ranking the input parameter settings. The study reveals that the performance of CNSL is better than that of a traditional cutting fluid, and the TODIM method can be successfully applied to rank the input parameter settings.

Possibility of Renewable Energy Solutions Usage in Rural Areas of Western Balkans: Fuzzy-Rough Approach

Abstract Energy production, supply and consumption are global issue with many economic, environmental and social implications. Mentioned issue is even more expressed in remote rural areas, in particular in developing countries, as are the countries of the Western Balkans (WB). Renewable energy sources (RES) could represent optimal energy alternative for sustainable performing of agricultural and other activities, as well as for improving the current state of living conditions in rural communities. The main goal of research is to mark the most suitable RES alternative (six alternatives) for wider implementation in rural space of WB. The applied methodology framework implies experts’ opinion (engagement of eight experts) and the use of multi-criteria decision-making methods (MCDM), (specifically fuzzy-rough LMWA and fuzzy-rough CRADIS methods) under the predefined criteria (nine criteria). Derived results show that the implementation of the solar energy plants could play an optimal solution, while as the relatively unsuitable alternative could be marked the use of energy potential of watercourses. Gained final result, i.e. ranking order of the considered alternatives is additionally verified by the appliance of other MCDM methods, while the sensitivity analysis was also performed.

An Intelligent Strategy Decision Method for Collaborative Jamming Based on Hierarchical Multi-Agent Reinforcement Learning

An Intelligent Strategy Decision Method for Collaborative Jamming Based on Hierarchical Multi-Agent Reinforcement Learning

Research on investment project risk prediction and management based on machine learning

In the era of digital economy, digital transformation is an inevitable choice in line with current economic development and national policy trends. Enterprises use new generation digital technologies such as big data, blockchain, cloud computing, artificial intelligence, and financial technology to apply these technologies to various production and business activities. The research on project risk management has gradually introduced the method of intelligent decision-making, using technologies such as big data and artificial intelligence to identify and analyze risks. We use learning models and ensemble learning techniques to predict and manage the risks of investment projects. Through long short-term memory networks (LSTMs), we are able to effectively extract spatiotemporal features from historical investment data to predict future risk dynamics. In order to further improve the accuracy of prediction and the robustness of the model, we introduced the Gradient Booster (GBM) ensemble learning method. These integrated technologies not only optimize the overall performance of the model, but also enhance the adaptability and forecast accuracy of various market changes. In the experimental analysis, we compare the performance of multiple models on real-world investment datasets, and the results show that the ensemble learning method has significant advantages in risk prediction accuracy.

Research on group decision optimization and influence analysis in social network based on machine learning

With the rapid development of online social networks, the research on group decision-making in social networks has attracted extensive attention. Social networks facilitate interaction and behavior between individuals, businesses, and organizations. However, traditional group decision-making methods often ignore the social relationships between group members and fail to fully consider the impact of these relationships on subgroup division. In this work, we propose a novel model approach that combines graph neural networks and deep learning techniques to capture and analyze complex relational structures in social networks. The model uses node features and edge features to optimize the group decision-making process and effectively evaluate the influence between individuals through multi-layer network embedding and aggregation operations. Experimental analysis results show that the proposed method performs well in improving the accuracy and efficiency of decision-making, and significantly improves the quality of decision-making.

End-to-End Autonomous Driving Decision Method Based on Improved TD3 Algorithm in Complex Scenarios.

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability.

USING THE GEOSPATIAL MULTI-CRITERIA DECISION ANALYSIS MODEL AND METHODS FOR SOIL DEGRADATION RISK MAPPING

Modern methods of spatial analysis and modeling are increasingly being combined with decision-making methods and fuzzy set theory. The latter are actively integrated into the environment of Geographic Information Systems (GIS), such as well-known ones like ArcGIS or QGIS, in the form of separate tools, plugins, or Python scripts. Decision-making methods allow structuring the problem in geographical space, as well as taking into account the knowledge and judgments of experts and the preferences of the decision-maker in determining the priorities of alternative solutions. This paper provides a description of a geospatial multi-criteria decision analysis model, which allows addressing a wide range of ecological and socio-economic issues. An example of applying this model to map soil degradation risk in Ukraine is presented in the paper. According to the object-spatial approach, the properties of a territory are determined as the result of the action (impact) of a set of objects (processes) belonging to this territory. The territory is represented as a two-dimensional discrete grid, each point of which (local area) is an alternative. The set of local areas of the territory constitutes the set of alternatives. The representation of the territory model as a system of objects and relationships between them allows justifying the choice of a set of criteria (factors) for assessing soil degradation risk. Each criterion is a separate raster layer of the map. To build a hierarchical decision-making structure and calculate the importance coefficients of the criteria, the Analytic Hierarchy Process (AHP) method is used. To account for uncertainty in assessments and judgments of experts at the stages of standardization of alternative attributes by different criteria and aggregation of their assessments, expert membership functions for fuzzy sets and fuzzy quantifiers are applied. The particular feature of the proposed multi-criteria decision analysis model is its low computational complexity and ease of integration into the GIS environment.

Use of the Simple Multicriteria Decision-Making (MCDM) Method for Optimization of the High-Alloy Steel Cutting Processby the Abrasive Water Jet

In the case of advanced manufacturing technologies, which include Abrasive Water Jet Machining, optimization of control parameters is necessary to achieve appropriate efficiency and quality. One of the optimization methods used in the presented research is SAW, from the Multi-Criteria Decision Making (MCDM) group. In multi-criteria decision-making (MCDM) situations, the criterion weights are crucial components that have a big impact on the outcomes. A novel technique called MEREC (MEthod based on the Removal Effects of Criteria) was presented to find the objective weights of the criteria. The research covered cutting high-alloy steel using AWJ, under the Design of Experiment (DoE) within the L9 orthogonal table. Abrasive flow rate, pressure, and feed were selected as control parameters. The cutting depth (beneficial) and the roughness of the cut surface Sa (non-beneficial) were taken as the output parameters. The result of the research is the determination of the impact of individual control parameters and the determination of a set of control parameters from the point of view of efficiency and quality.