In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored infurmation during a period is taken as interval numbers, and the interval multi-attribute deci...In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored infurmation during a period is taken as interval numbers, and the interval multi-attribute decision-making method is employed to predict the performance of aeroengine, The synthetic weights of interval numbers are obtained by calculating deviation degree and possibility degree. As an example of application, 5 performance parameters monitored on 10 CF6 aeroengines of China Eastern Airlines Co., Ltd are adopted as decision attributes to verify the algorithm. The obtained synthetic ranking result shows the effectiveness and rationality of the proposed method in reflecting the performance stares of aeroengins.展开更多
This paper deals with a multidimensional examination of the infrastructural, technical/technological, operational, economic, social, and environmental performances of high-speed rail （HSR） systems, including their o...This paper deals with a multidimensional examination of the infrastructural, technical/technological, operational, economic, social, and environmental performances of high-speed rail （HSR） systems, including their overview, analysis of some real-life cases, and limited （analytical） modeling. The infrastructural performances reflect design and geometrical characteristics of the HSR lines and stations. The technical/technological performances relate to the characteristics of rolling stock, i.e., high-speed trains, and supportive facilities and equipment, i.e., the power supply, signaling, and traffic control and management system（s）. The operational performances include the capacity and productivity of HSR lines and rolling stock, and quality of services. The economic per- formances refer to the HSR systems＇ costs, revenues, and their relationship. The social performances relate to the impacts of HSR systems on the society such as congestion, noise, and safety, and their externalities, and the effects in terms of contribution to the local and global/country social- economic development. Finally, the environmental performances of the HSR systems reflect their energy consumption and related emissions of green house gases, land use, and corresponding externalities.展开更多
Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a ...Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.展开更多
The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions t...The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.展开更多
The decision-making units(DMUs)in the modern service industries may produce desirable outputs and undesirable outputs.For the decision makers,some outputs may be more desired than others although all of them are desir...The decision-making units(DMUs)in the modern service industries may produce desirable outputs and undesirable outputs.For the decision makers,some outputs may be more desired than others although all of them are desirable.Considering these characteristics,this work combines the data envelopment analysis(DEA)and the multiple attributes decision-making(MADM)method,to make a reasonable and comprehensive performance evaluation for DMUs.Specifically,three DEA-based models are modified to obtain more reasonable efficiency scores for DMUs.The MADM method is used to determine the weights of outputs based on the preference ratings within the outputs.The efficiency scores are then multiplied by the aggregated outputs quantities to obtain the comprehensive performance scores for evaluation.The effectiveness of the proposed models is demonstrated by extensive numerical experiments.展开更多
Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty...Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.展开更多
Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very impor...Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems.Three methods are commonly used for assessing the weights of criteria:objective,subjective,and integrated methods.In this study,an objective approach is proposed to assess the weights of criteria,called SPCmethod(Symmetry Point of Criterion).This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making.The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example.To validate the developed method,we used our numerical example and calculated the weights of criteria by CRITIC,Entropy,Standard Deviation and MEREC methods.Comparative analysis between these methods and the SPC method reveals that the developedmethod is a very reliable objective way to determine the weights of criteria.Additionally,in this study,we proposed the application of SPCmethod to evaluate the efficiency of themulti-criteria partitioning algorithm.The main idea of the evaluation is based on the following fact:the greater the uniformity of the weights of criteria,the higher the efficiency of the partitioning algorithm.The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.展开更多
With an accelerating increase of business benefits produced from big data analytics (if used appropriately and intelligently by businesses in the private and public sectors), this study focused on empirically identify...With an accelerating increase of business benefits produced from big data analytics (if used appropriately and intelligently by businesses in the private and public sectors), this study focused on empirically identifying the big data analytics (BDA) attributes. These attributes were classified into four groups (i.e., value innovation, social impact, precision, and completeness of BDA quality) and were found to influence the decision-making performance and business performance outcomes. A structural equation modeling analysis using 382 responses from a BDA related to practitioners indicated that the attributes of representativeness, predictability, interpretability, and innovativeness as related to value innovation greatly enhanced the decision-making confidence and effectiveness of decision makers who make decisions using big data. In addition, individuality, collectivity, and willfulness, which are related to social impact, also greatly improved the decision-making confidence and effectiveness of the same decision makers. This shows that the value innovation and social impact, which have received relatively less attention in previous studies, are the crucial attributes for BDA quality as they influence the decision-making performance. Comprehensiveness, factuality, and realism, which are linked to completeness, also have similar results. Furthermore, the higher the decision-making confidence of the decision makers who used big data was, the higher the financial performance of their companies. In addition, high decision-making confidence using big data was found to improve the nonfinancial performance metrics such as customer satisfaction and quality levels as well as product development capabilities. High decision-making effectiveness with big data was also shown to improve the nonfinancial performance metrics.展开更多
文摘In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored infurmation during a period is taken as interval numbers, and the interval multi-attribute decision-making method is employed to predict the performance of aeroengine, The synthetic weights of interval numbers are obtained by calculating deviation degree and possibility degree. As an example of application, 5 performance parameters monitored on 10 CF6 aeroengines of China Eastern Airlines Co., Ltd are adopted as decision attributes to verify the algorithm. The obtained synthetic ranking result shows the effectiveness and rationality of the proposed method in reflecting the performance stares of aeroengins.
文摘This paper deals with a multidimensional examination of the infrastructural, technical/technological, operational, economic, social, and environmental performances of high-speed rail （HSR） systems, including their overview, analysis of some real-life cases, and limited （analytical） modeling. The infrastructural performances reflect design and geometrical characteristics of the HSR lines and stations. The technical/technological performances relate to the characteristics of rolling stock, i.e., high-speed trains, and supportive facilities and equipment, i.e., the power supply, signaling, and traffic control and management system（s）. The operational performances include the capacity and productivity of HSR lines and rolling stock, and quality of services. The economic per- formances refer to the HSR systems＇ costs, revenues, and their relationship. The social performances relate to the impacts of HSR systems on the society such as congestion, noise, and safety, and their externalities, and the effects in terms of contribution to the local and global/country social- economic development. Finally, the environmental performances of the HSR systems reflect their energy consumption and related emissions of green house gases, land use, and corresponding externalities.
文摘Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.
基金Supported by the National Natural Science Foundation of China(61590923,61422303,21376077)
文摘The performance evaluation of the process industry, which has been a popular topic nowadays, can not only find the weakness and verify the resilience and reliability of the process, but also provide some suggestions to improve the process benefits and efficiency. Nevertheless, the performance assessment principally concentrates upon some parts of the entire system at present, for example the controller assessment. Although some researches focus on the whole process, they aim at discovering the relationships between profit, society, policies and so forth, instead of relations between overall performance and some manipulated variables, that is, the total plant performance. According to the big data of different performance statuses, this paper proposes a hierarchical framework to select some structured logic rules from monitored variables to estimate the current state of the process. The variables related to safety and profits are regarded as key factors to performance evaluation. To better monitor the process state and observe the performance variation trend of the process, a classificationvisualization method based on kernel principal component analysis(KPCA) and self-organizing map(SOM) is established. The dimensions of big data produced by the process are first reduced by KPCA and then the processed data will be mapped into a two-dimensional grid chart by SOM to evaluate the performance status. The monitoring method is applied to the Tennessee Eastman process. Monitoring results indicate that off-line and on-line performance status can be well detected in a two-dimensional diagram.
基金This work was supported by Science and Technology Foundation of Jiangxi Educational Committee[grant number GJJ190287].
文摘The decision-making units(DMUs)in the modern service industries may produce desirable outputs and undesirable outputs.For the decision makers,some outputs may be more desired than others although all of them are desirable.Considering these characteristics,this work combines the data envelopment analysis(DEA)and the multiple attributes decision-making(MADM)method,to make a reasonable and comprehensive performance evaluation for DMUs.Specifically,three DEA-based models are modified to obtain more reasonable efficiency scores for DMUs.The MADM method is used to determine the weights of outputs based on the preference ratings within the outputs.The efficiency scores are then multiplied by the aggregated outputs quantities to obtain the comprehensive performance scores for evaluation.The effectiveness of the proposed models is demonstrated by extensive numerical experiments.
基金supported by the Shanghai Science and Technology Committee (22511105500)the National Nature Science Foundation of China (62172299, 62032019)+2 种基金the Space Optoelectronic Measurement and Perception LaboratoryBeijing Institute of Control Engineering(LabSOMP-2023-03)the Central Universities of China (2023-4-YB-05)。
文摘Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.
文摘Information about the relative importance of each criterion or theweights of criteria can have a significant influence on the ultimate rank of alternatives.Accordingly,assessing the weights of criteria is a very important task in solving multi-criteria decision-making problems.Three methods are commonly used for assessing the weights of criteria:objective,subjective,and integrated methods.In this study,an objective approach is proposed to assess the weights of criteria,called SPCmethod(Symmetry Point of Criterion).This point enriches the criterion so that it is balanced and easy to implement in the process of the evaluation of its influence on decision-making.The SPC methodology is systematically presented and supported by detailed calculations related to an artificial example.To validate the developed method,we used our numerical example and calculated the weights of criteria by CRITIC,Entropy,Standard Deviation and MEREC methods.Comparative analysis between these methods and the SPC method reveals that the developedmethod is a very reliable objective way to determine the weights of criteria.Additionally,in this study,we proposed the application of SPCmethod to evaluate the efficiency of themulti-criteria partitioning algorithm.The main idea of the evaluation is based on the following fact:the greater the uniformity of the weights of criteria,the higher the efficiency of the partitioning algorithm.The research demonstrates that the SPC method can be applied to solving different multi-criteria problems.
文摘With an accelerating increase of business benefits produced from big data analytics (if used appropriately and intelligently by businesses in the private and public sectors), this study focused on empirically identifying the big data analytics (BDA) attributes. These attributes were classified into four groups (i.e., value innovation, social impact, precision, and completeness of BDA quality) and were found to influence the decision-making performance and business performance outcomes. A structural equation modeling analysis using 382 responses from a BDA related to practitioners indicated that the attributes of representativeness, predictability, interpretability, and innovativeness as related to value innovation greatly enhanced the decision-making confidence and effectiveness of decision makers who make decisions using big data. In addition, individuality, collectivity, and willfulness, which are related to social impact, also greatly improved the decision-making confidence and effectiveness of the same decision makers. This shows that the value innovation and social impact, which have received relatively less attention in previous studies, are the crucial attributes for BDA quality as they influence the decision-making performance. Comprehensiveness, factuality, and realism, which are linked to completeness, also have similar results. Furthermore, the higher the decision-making confidence of the decision makers who used big data was, the higher the financial performance of their companies. In addition, high decision-making confidence using big data was found to improve the nonfinancial performance metrics such as customer satisfaction and quality levels as well as product development capabilities. High decision-making effectiveness with big data was also shown to improve the nonfinancial performance metrics.
