Decision Method Research Articles

Short-term high-efficiency decision-making method for tertiary voltage control considering short-term power fluctuations

Abstract With the continuous increase in grid-connected new energy sources and new types of intermittent loads, the short-term fluctuation amplitude of source-load power is continuously increasing, which increasingly affects the effectiveness of tertiary voltage control decisions. To address this issue, this paper proposes a short-term efficient decision-making method for tertiary voltage control considering short-term power fluctuations. Firstly, the decision-making cycle of tertiary voltage control is shortened, effectively enhancing its adaptability to short-term source-load power fluctuations. Secondly, to address the computational burden brought by the shortened decision cycle, this paper fully considers the temporal characteristics of short-term load fluctuations in practical power grid nodes and proposes two linearization methods for tertiary voltage control. Specifically, these methods include a three-point linear estimation method for small fluctuation periods and a sensitivity linearization optimization method for large fluctuation periods. By shortening the decision cycle and linearizing decision calculations, the efficiency of tertiary voltage control decision-making is greatly improved. Based on actual data from a provincial-level power grid in the northwest region of China, the effectiveness of the proposed methods is verified through simulation analysis.

Urban Competitiveness Assessment Using the Integration of the Global Power City Index and Multicriteria Decision-Making Methods

Urban Competitiveness Assessment Using the Integration of the Global Power City Index and Multicriteria Decision-Making Methods

Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles.

The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.

Evaluation of Community Regeneration Planning and Resilience Based on Fuzzy Group Decision-Making Method

Evaluation of Community Regeneration Planning and Resilience Based on Fuzzy Group Decision-Making Method

Verifying Welding Processes Selection for a Case Study Using an Analytic Hierarchical Process

This paper aims to develop a framework for differentiating the selection of welding processes using specialized knowledge-based expert systems, which are particularly useful in dealing with issues that require complex decision-making. A program was created and programmed using the process information maps (PRIMAs) matrix in Visual Basic Access for selecting welding processes in a study. The program is in two stages. In the first stage, it excludes non-candidate operations according to several criteria, the most important of which is the type of metal and its thickness. The second stage is arranging operations using multi-criteria methods for decision-making through research to resolve complicated problems involving multiple factors. The hierarchical analysis process (AHP) was used. It is considered one of the most frequently used multi-criteria decision-making methods. The selection of analytic hierarchical process. AHP criteria depend on experience and knowledge rather than specific data for selecting alternatives and determining weights. The methodology is straightforward, easy to comprehend, and applicable to various domains needing intricate decision-making. The program results are compared with those of a previously published case to choose the best welding processes for a study case, which is a home radiator, and results matching the candidate processes were discovered.

Multi-objective optimization of series-parallel system with mixed subsystems failure dependencies using NSGA-II and MOHH

In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.

An intelligent decision method of quality risk in electromagnet assembly process based on digital twin and q-rung dual hesitant fuzzy number

In order to solve the problem of online risk analysis of assembly process, an intelligent decision-making method of electromagnet assembly risk based on Digital Twin (DT) was proposed. Firstly, a DT model of electromagnet assembly process is constructed, and the potential failure modes are collected in real time at the physical layer. Secondly, in the virtual layer, real-time potential failure modes are obtained through information synchronization and transmitted to the application layer. Then, in the application layer, the optimized Failure Mode and Effects Analysis (FMEA) method is used to analyze the risk of failure mode, and the risk value of failure mode is fed back to the virtual layer. In the improved FMEA method, the q-Rung Dual Hesitant Fuzzy Number (q-RDHFN) is used as the evaluation language. The weight of experts is calculated by Bidirectional Projection Method (BPM), and the comprehensive weight of risk factors is calculated by Best and Worst Method (BWM) method, entropy weight method and Criteria Importance Through Intercriteria Correlation (CRITIC) method. Finally, the physical layer is adjusted to reduce the probability and risk of failure mode occurrence. An example of risk intelligent decision making is given to verify the objectivity and practicability of the new method.

Transformer-Based Sequence Modeling Short Answer Assessment Framework

Automated subjective assessment presents a significant challenge due to the complex nature of human language and reasoning characterized by semantic variability, subjectivity, language ambiguity, and judgment levels. Unlike objective exams, subjective assessments involve diverse answers, posing difficulties in automated scoring. The paper proposes a novel approach that integrates advanced natural language processing (NLP) techniques with principled grading methods to address this challenge. Combining Transformer-based Sequence Language Modeling with sophisticated grading mechanisms aims to develop more accurate and efficient automatic grading systems for subjective assessments in education. The proposed approach consists of three main phases: Content Summarization: Relevant sentences are extracted using self-attention mechanisms, enabling the system to effectively summarize the content of the responses. Key Term Identification and Comparison: Key terms are identified within the responses and treated as overt tags. These tags are then compared to reference keys using cross-attention mechanisms, allowing for a nuanced evaluation of the response content. Grading Process: Responses are graded using a weighted multi-criteria decision method, which assesses various quality aspects and assigns partial scores accordingly. Experimental results on the SQUAD dataset demonstrate the approach’s effectiveness, achieving an impressive F-score of 86%. Furthermore, significant improvements in metrics like ROUGE, BLEU, and METEOR scores were observed, validating the efficacy of the proposed approach in automating subjective assessment tasks. Doi: 10.28991/HIJ-2024-05-03-06 Full Text: PDF

An optimization framework for achieving optimal hydrocyclone's performance aligning with decision-makers' preferences

Previous hydrocyclone optimization often neglected interactions among key performance objectives, which limits hydrocyclone wide applications to meet increasingly diverse industry demands. This study proposes an optimization framework to identify the most suitable hydrocyclone design and operating conditions with conflicting key performance objectives. An advanced multi-objective evolutionary algorithm (PICEA-g) is employed to generate Pareto-optimal solutions that capture trade-offs among multiple conflicting objectives. A novel data-driven predictive algorithm, INFO-ELM, is introduced to establish nonlinear relationships between key variables and performance objectives, thereby accelerating the search for Pareto-optimal solutions by PICEA-g. Furthermore, a multi-criteria decision-making method (TOPSIS) is utilized to determine the optimal solution based on decision-makers' preferences, ensuring consistency between preference information and decision outcomes. The framework's effectiveness is validated across various separation scenarios using two decision-making strategies. This study offers a comprehensive approach to address trade-offs in hydrocyclone optimization, widening its application in diverse separation scenarios.

Multi-position two-phase person-position matching decision-making with intermediary participation

With the rapid development in society, intermediaries play an increasingly important role in person-position matching decision-making. To obtain reasonable enterprise-intermediary matching and enterprise-candidate matching schemes under multiple positions, preferences of enterprises, headhunting companies and candidates should be fully considered from a two-phase perspective. Thus, a two-sided matching decision-making method under the probabilistic linguistic environment is proposed to solve the multi-position two-phase person-position matching problem. First, related theories of probabilistic linguistic term sets and two-sided matching are given. On this basis, the problem of multi-position two-phase person-position matching under the probabilistic linguistic environment is described. The decision-making process is divided into the following two phases: In the first phase, satisfactions of enterprises and headhunting companies are calculated considering subject expectations based on prospect theory. Then, considering the maximization of satisfactions of two-sided subjects, the many-to-many two-sided matching model between enterprises and headhunting companies is established and solved to obtain the optimal many-to-many matching scheme. In the second phase, according to evaluation matrices and expectation matrices, satisfactions of enterprises and candidates under multiple positions are calculated considering intermediary evaluations. Then, considering the maximization of satisfactions of enterprises and candidates, one-to-many matching models and one-to-one stable matching models under multiple positions are developed and solved to obtain the optimal person-position matching schemes under multiple positions. Finally, an example of the person-position matching with intermediary participation is used to illustrate the feasibility and effectiveness of the proposed method.

Sub-watershed Prioritization Based on Watershed Hydrological Security Using Different Multi-Criteria Decision-Making Methods

Sub-watershed Prioritization Based on Watershed Hydrological Security Using Different Multi-Criteria Decision-Making Methods

Analysis of risk influential factors of marine pilots during the embarkation and disembarkation

Due to the complex geographical conditions within the port waters, it is necessary to take pilotage operations. The embarkation and disembarkation (E&D) of marine pilots is the riskiest phase of pilotage work, with accidents occurring frequently. In order to examine the key Risk Influential Factors (RIFs) of E&D accident of marine pilots, in this study, a novel complex network model of RIFs of marine pilots during the E&D process is proposed. Firstly, based on the E&D accident investigation report of marine pilots, the causal chain of E&D accidents is extracted, and the RIFs leading to E&D accidents are identified. Secondly, a complex network model of RIFs of marine pilots during the E&D is constructed based on the complex networks theory, and the network reliability is verified. Finally, by comprehensive use of topology characteristics analysis, robustness analysis and module analysis, the key RIFs of pilot E&D accidents are identified. The research results show that pilot and management factors have important impact on pilot safety, ‘insufficient pilot ladder strength' is the most critical RIF. It should be focused on RIFs with high connectivity between modules and within modules, and the risk evolution can be blocked through the control of those RIFs. Abbreviations: BNs: Bayesian Networks; CDM: Critical Decision Method; DEMATEL: Decision-making Trial and Evaluation Laboratory; E&D: Embarkation and Disembarkation; FSA: Formal Safety Assessment; RIFs: Risk Influential Factors; RPD: Recognition-Primed Decision; SHERPA: Systematic Human Error Reduction and Prediction Approach; SOLAS: International Convention for the Safety of Life at Sea; VTS: Vessel Traffic Services; TECHR: Technique for Early Consideration of Human Reliability.

MATHEMATICAL MODELS AND ALGORITHMS IN LOGISTICS: REAL- WORLD APPLICATIONS AND AI IMPACT

Have you ever pondered the importance of mathematics in the field of logistics? Have you also reflected on whether a solid understanding of mathematical principles is crucial for achieving success in a logistics profession? This article explores the intersection of mathematics and logistics, shedding light on the indispensable role mathematics plays within the industry. By exploring practical applications, it provides insights into the mathematical concepts integral to the daily activities of logistics professionals. Whether aspiring to enter the field or carrying out further studies in logistics, this article offers a foundational understanding of mathematics' importance in logistics. In addition, we have also shown a case in which technological development and artificial intelligence are applied, where we have employed logistic regression, k-nearest neighbors, and random forest classifiers on data collected from Amazon to analyze customer reviews. Our results demonstrate that logistic regression outperformed the others, achieving an accuracy rate of 86.75%. This result is suggestive of the importance of this technique in building customer relations, particularly in their use in decision-making methods.

Cost-benefit analysis in UK hotels: A hybrid SOCP-MCDM approach

Performance evaluation has been an important topic of concern for tourism industry practitioners as well as academic researchers, and its investigation in the UK hotel sector is paramount because this industry has been experiencing a higher level of competition. The present study contributes to the previous literature on hotel performance evaluation in general by proposing an innovative hybrid method combining the second-order cone programming (SOCP) method and multi-criteria decision-making (MCDM) method to estimate the performance of the UK hotel sector. The innovation lies in the synergistic combination of SOCP and MCDM methodologies, enabling a comprehensive assessment of hotel performance by managing a non-linear optimisation. Overall, this hybrid method benefits from the ability to be more flexible in addressing complex operational issues and provide more accurate results. This research provides a cost-benefit analysis within the proposed method, suggesting important policy implications in the tourism industry.

A multi-objective decision-making method for small modular reactor operation based on A3C algorithm

Small modular reactors (SMRs) are ideal energy sources for remote areas, but they still require operators to make decisions. Due to the harsh working environment, it is difficult to resume operation of small modular reactors once an accident occurs in unmanned application scenarios. Therefore, a multi-objective operation scheme that ensures safety and takes into account the operational task requirements is particularly important. We proposed an artificial intelligence approach for multi-objective optimization and decision-making for SMRs. We used a multi-objective extension of the Asynchronous Advantage Actor-Critic (A3C) algorithm to learn a comprehensive optimization function reflecting the importance of each objective under different tasks. We also used a proxy deep neural network (DNN) simulating the reactor dynamics and speeding up the training process. We tested our approach on a simulated SMR under different scenarios, such as normal operation and fault occurrence. The results showed that our approach can perform well under different conditions and avoid local optima. The verification results showed that the operation limits are not violated in the transient process. Our approach solves a challenging problem and provides a valuable insight for the field of SMRs. It can also be applied to other complex systems that require multi-objective optimization and decision-making.

Interval-valued fermatean fuzzy Aczel-Alsina geometric aggregation operators and their applications to group decision-making

Developing new aggregation operators on various classes of fuzzy sets and their generalizations is important in modelling real-life decision-making problems. Interval-valued Fermatean fuzzy sets (IVFFs) generalize the idea of interval-valued Pythagorean fuzzy sets (IVPFS) play a crucial role in modelling problems involving inadequate information. Decision-making problems modelled using IVFFNs require different score functions and aggregation operators on the set of IVFFNs. This study mainly focuses on establishing a few interval-valued Fermatean fuzzy (IVFF) aggregation operators by integrating the Aczel-Alsina (AA) operations to deal with group decision-making (GDM) problems. In this work, first, we discuss various Aczel-Alsina-based IVFF operations such as AA sum, AA product, and AA scalar multiplication for proposing a few new aggregation operators for the IVFF environment based on the new IVFF operations. Secondly, we introduce a few operators, including the interval-valued Fermatean fuzzy Aczel-Alsina (IVFFAA) weighted geometric operator, the IVFFAA ordered weighted geometric (IVFFAAOWG) operator, and the IVFFAA hybrid geometric (IVFFAAHG) operator. Various important properties such as idempotency, boundness, and monotonicity have also been studied. Thirdly, we establish multi-criteria group decision-making (MCGDM) method for solving real-life decision-making problems. Fourthly, we solve a model GDM problem to show the applicability and efficacy of our proposed MCGDM method, which utilizes the IVFFAAWG operator. Further, a sensitivity analysis is performed to ensure better performance, and finally, a comparative study of our method is done by comparing our proposed MCGDM approach with different existing methods.

An improved hierarchical deep reinforcement learning algorithm for multi-intelligent vehicle lane change

Lane change is a pivotal technology in autonomous driving, playing a significant role in improving traffic conditions. The control task of lane change becomes exceptionally complex for coordination of multiple intelligent vehicles, especially in unpredictable situations. To address this issue, the paper proposes a novel multi-intelligent vehicle lane change method (MVLC) based on Deep Reinforcement Learning (DRL). The MVLC features a hierarchical framework that includes a lower-layer lane change controller and an upper-layer reinforcement learning decision-making method. In the lower layer, a lane change controller is designed to execute motion control for a single vehicle, consisting of a lateral controller utilizing dueling double deep Q-network to make lane change decision and a longitudinal controller that employs Intelligent Driver Model (IDM) to follow the preceding vehicle. In the upper layer, a model-free DRL method is used for simultaneous control of multiple intelligent vehicles. To learn the optimal policy, a comprehensive reward function is designed. Finally, experiments are conducted in mixed traffic to valid the effectiveness of the proposed method. Results show that MVLC achieves secure and timely lane changes for multiple vehicles. Therefore, the method is expected to have significant potential for improving overall traffic efficiency and mitigating traffic congestion.

A new extension of the EDAS method in a fuzzy environment for group decision-making

The complexity of the decision-making problems being analysed has led to the development of multiple multi-criteria decision-making (MCDM) methods. One of the more recent methods belonging to this group is the evaluation based on distance from average solution (EDAS) method. To date, it has found extensive use in solving real-world decision-making problems and has seen many extensions to input data types other than real numbers. One of these is the EDAS method for group decision-making in a fuzzy environment. This method aggregates individual evaluations of decision-makers into a group evaluation using the arithmetic mean. This may result in equal group ratings despite the variety of individual ratings, making it difficult or even impossible to rank alternatives because the EDAS algorithm will be blocked. The paper proposes a new fuzzy extension of EDAS called the PFEDAS method for group decision-making. The main difference between the proposed method and the original one is that at the initial stage the individual decision matrices are not aggregated into a group matrix but are transformed into matrices of alternatives. As a result, the new PFEDAS method is based on the initial data instead of their averaged values which allows a more accurate comparison of alternatives. Using a numerical example, the PFEDAS method is compared with other similar methods known from the literature.

Renewable Energy Technology Selection for Hotel Buildings: A Systematic Approach Based on AHP and VIKOR Methods

This study provides an assessment of renewable energy technology utilization in hotel buildings, which are significant structures in terms of energy consumption. The aim of the study is to determine suitable renewable energy technologies (RETs) for hotel buildings by defining criteria for evaluating RETs, assessing the relative importance of these criteria, and proposing a multi-criteria decision-making framework to solve the problem of selecting the most appropriate RETs during the design stage. The alternatives for RETs and the criteria for their evaluation are gathered through a literature review and expert consultations. Eight fundamental RETs used in hotel buildings (such as heat pumps, solar panels, biomass boilers, etc.) are examined, and nine selection criteria are analyzed. According to the weights determined by the Analytic Hierarchy Process (AHP) method, the initial investment cost is the most influential decision criterion, with a weight of 0.314. As a result of applying the AHP and VIKOR (Multi-Criteria Optimization and Compromise Solution) methods for technology selection, photovoltaic panels emerge as the top-ranked choice. This comprehensive evaluation provides stakeholders in the building production process of hotel buildings with detailed analyses and multi-criteria decision-making methods for selecting RETs.

A ranking framework for the selection of IoT cloud platforms using hybrid multi-attribute decision-making method

PurposeThe Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.Design/methodology/approachThe optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.FindingsA case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.Originality/valueThe proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.