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Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm
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作者 Xixing Li Qingqing Zhao +4 位作者 Hongtao Tang Xing Guo Mengzhen Zhuang Yibing Li Xi Vincent Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第5期1390-1408,共19页
To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rul... To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs. 展开更多
关键词 flexible job shop scheduling composite dispatching rule improved genetic programming algorithm deep reinforcement learning
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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer
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作者 Hongliang Zhang Yi Chen +1 位作者 Yuteng Zhang Gongjie Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1459-1483,共25页
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke... The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality. 展开更多
关键词 Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-LEARNING
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Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem
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作者 Wei Li Xiangfang Yan Ying Huang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第5期1283-1299,共17页
With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds... With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems. 展开更多
关键词 Ant Colony Optimization(ACO) job shop Scheduling Problem(JSP) knowledge learning cooperative guidance
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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem
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作者 Liang Zeng Junyang Shi +2 位作者 Yanyan Li Shanshan Wang Weigang Li 《Computers, Materials & Continua》 SCIE EI 2024年第1期375-392,共18页
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ... The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem. 展开更多
关键词 Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover
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An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem
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作者 Zhaolin Lv Yuexia Zhao +2 位作者 Hongyue Kang Zhenyu Gao Yuhang Qin 《Computers, Materials & Continua》 SCIE EI 2024年第2期2337-2360,共24页
Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been... Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms. 展开更多
关键词 Flexible job shop scheduling improved Harris hawk optimization algorithm(GNHHO) premature convergence maximum completion time(makespan)
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The Influence of Preschool Teachers’Social Skills on Job Burnout:A Moderated Mediation Model
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作者 Yuanqing He Xinyue Yu +1 位作者 Yu Xia Yanhua Cao 《International Journal of Mental Health Promotion》 2024年第6期463-474,共12页
Background:Teacher burnout is a serious issue in the field of education,particularly in early childhood education,where teachers face high levels of work stress and emotional labor,leading to emotional exhaustion and ... Background:Teacher burnout is a serious issue in the field of education,particularly in early childhood education,where teachers face high levels of work stress and emotional labor,leading to emotional exhaustion and job burnout.However,past research has not sufficiently explored the mechanisms of social skills,empathy,and mindfulness in mitigating teacher burnout.Therefore,this study aims to investigate the relationship between preschool teachers’social skills,empathy,and mindfulness with job burnout,in order to provide theoretical basis and practical guidance for reducing teacher burnout.Methods:This research utilized a convenience sampling approach to target preschool teachers for a questionnaire survey.A total of 1109 questionnaires were collected.To ensure the quality of the data,we excluded questionnaires that were not carefully filled out in terms of lie scale questions,those with abnormal demographic variables,and outliers identified based on response time.Ultimately,901 valid questionnaires were obtained,achieving a valid response rate of 81.2%.Participants’levels of social skills,empathy,mindfulness,and job burnout were assessed using the Social Skills Scale(SKS),Empathy Scale(Measure of Empathy,ME),Mindful Attention Awareness Scale(MAAS),and the Maslach Burnout Inventory-Educators Survey(MBI-ES),respectively.Data analysis was conducted using SPSS.Results:After controlling for gender,age,teaching experience,educational level,grade taught,and location of the kindergarten,the study found:(1)There is a negative correlation between preschool teachers’social skills and the level of job burnout(r=−0.238);(2)Empathy has a dual-track effect on job burnout,where cognitive empathy negatively affects job burnout(r=−0.245),while emotional empathy has a positive effect(r=0.045);(3)Cognitive empathy partially mediates the relationship between social skills and job burnout(β=−0.124);(4)Mindfulness significantly impacts social skills,cognitive empathy,and job burnout(r=0.278;r=0.286;r=−0.539),and plays a moderating role in the mediation model(β=0.003;β=−0.023).Conclusion:These findings provide theoretical support for the development of burnout prevention and intervention strategies targeted at preschool teachers.They also point out new directions for future research and potential intervention targets,suggesting that enhancing preschool teachers’social skills and cognitive empathy,as well as increasing their mindfulness level,can help them cope with work-related stress and emotional labor,thereby alleviating job burnout. 展开更多
关键词 Social skills job burnout EMPATHY MINDFULNESS preschool teachers
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The Emergence of Loving Pedagogy in the L2 Affective Domain:How Affective Pedagogy Accounts for Chinese EFL Teachers’Professional Success and Job Satisfaction?
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作者 Yi Li Xinpeng Wang 《International Journal of Mental Health Promotion》 2024年第3期239-250,共12页
The success of teachers in professional environments has a desirable influence on their mental condition.Simply said,teachers’professional success plays a crucial role in improving their mental health.Due to the inva... The success of teachers in professional environments has a desirable influence on their mental condition.Simply said,teachers’professional success plays a crucial role in improving their mental health.Due to the invaluable role of professional success in teachers’mental health,personal and professional variables helping teachers succeed in their profession need to be uncovered.While the role of teachers’personal qualities has been well researched,the function of professional variables has remained unknown.To address the existing gap,the current investigation measured the role of two professional variables,namely job satisfaction and loving pedagogy,in Chinese EFL teachers’professional success.To do this,three validated scales were provided to 1591 Chinese EFL teachers.Participants’answers to the questionnaires were analyzed using the Spearman correlation test and structural equation modeling.The data analysis demonstrated a strong,positive link between the variables.Moreover,loving pedagogy was found to be the positive,strong predictor of Chinese EFL teachers’job satisfaction and professional success.The findings of the current inquiry may help educational administrators enhance their instructors’professional success,which in turn promotes their mental and psychological conditions at work. 展开更多
关键词 Professional success mental health psychological condition loving pedagogy job satisfaction EFL teachers
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Post-COVID-19 Challenges for Full-Time Employees in China: Job Insecurity, Workplace Anxiety and Work-Life Conflict
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作者 Tianfei Yang Xianyi Long 《International Journal of Mental Health Promotion》 2024年第9期719-730,共12页
Background:Though the COVID-19 pandemic recedes,and our society gradually returns to normal,Chinese people’s work and lifestyles are still influenced by the“pandemic aftermath”.In the post-pandemic era,employees ma... Background:Though the COVID-19 pandemic recedes,and our society gradually returns to normal,Chinese people’s work and lifestyles are still influenced by the“pandemic aftermath”.In the post-pandemic era,employees may feel uncertainty at work due to the changed organizational operations and management and perceive the external environment to be more dynamic.Both these perceptions may increase employees’negative emotions and contribute to conflicts between work and life.Drawing from the ego depletion theory,this study aimed to examine the impact of job insecurity during the post-pandemic era on employees’work-life conflicts,and the mediating effect of workplace anxiety in this relationship.Besides,this study also considered the uncertainty of the external macro environment as a boundary condition on the direct and indirect relationship between job insecurity and work-life conflicts.Methods:A two-wave questionnaire survey was conducted from October to December 2023 to collect data.MBA students and graduates from business school with full-time jobs are invited to report their perception of job insecurity,work anxiety,perceived environment uncertainty,and work-life conflict.This resulted in 253 valid responses.Data analysis was performed using the SPSS,Amos,and PROCESS.Results:The results showed that:(1)Employees’job insecurity would directly intensify the work-life conflict(B=0.275,p<0.001,95%CI[0.182,0.367]).(2)Employees’workplace anxiety mediates the relationship between job insecurity and work-life conflict(B=0.083,p<0.001,95%CI[0.047,0.130]).(3)The mediating effect of workplace anxiety between job insecurity and work-life conflict exists when perceived environmental uncertainty is high(B=0.049,95%CI[0.011,0.114]),while vanishes when perceived environmental uncertainty is low(B=0.024,95%CI[−0.005,0.068]).Conclusion:Job insecurity combined with perceived environmental uncertainty in the postpandemic era fuels employees’workplace anxiety and work-life conflicts.Post-pandemic trauma lingers,necessitating urgent attention and response. 展开更多
关键词 job insecurity workplace anxiety work-life conflict perceived environmental uncertainty post-COVID-19 ego depletion theory
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Deep Reinforcement Learning Solves Job-shop Scheduling Problems
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作者 Anjiang Cai Yangfan Yu Manman Zhao 《Instrumentation》 2024年第1期88-100,共13页
To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transfo... To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time. 展开更多
关键词 job shop scheduling problems deep reinforcement learning state characteristics policy network
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A Study of the Relationship between Emotion Regulation and Job Burnout among Junior High School Novice English Teachers
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作者 Jiawen You Jia Lu +1 位作者 Qixian Ren Xudong Gong 《Journal of Contemporary Educational Research》 2024年第8期46-51,共6页
This study aimed to examine the relationship between junior high school novice English teachers’emotion regulation and job burnout.To achieve this purpose,a survey consisting of various scales was administered to 133... This study aimed to examine the relationship between junior high school novice English teachers’emotion regulation and job burnout.To achieve this purpose,a survey consisting of various scales was administered to 133 primary school teachers selected from Yunnan Province in China.Statistical analyses revealed gender differences in job burnout and emotion regulation among these teachers and highlighted the association between these two variables.The findings established that male novice English teachers in junior schools generally experience lower levels of job burnout and possess better emotion regulation skills compared to their female counterparts.Additionally,a strong negative correlation was identified between job burnout and emotional regulation skills,indicating that the stronger the emotional regulation skills,the less likely novice English teachers are to experience job burnout.The study further emphasized caution in the use of cognitive reappraisal as an emotion regulation strategy,as it may have an adverse effect on mitigating job burnout.This study concluded with recommendations for providing junior high school novice English teachers with opportunities to develop and enhance their emotion regulation skills to reduce job burnout effectively. 展开更多
关键词 job burnout Emotion regulation Novice English teacher
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The Docking of Talent Training Strategies and Job Market in Higher Vocational Colleges Under the Background of Slow and Difficult Employment
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作者 Fang Guo Shujun Luo Weihan Li 《Journal of Contemporary Educational Research》 2024年第6期268-273,共6页
Students in higher vocational colleges are faced with difficult problems such as slow employment,which highlights the dislocation between education and the market.This study surveyed thousands of students and hundreds... Students in higher vocational colleges are faced with difficult problems such as slow employment,which highlights the dislocation between education and the market.This study surveyed thousands of students and hundreds of enterprises,and put forward docking strategies.The analysis shows that the mismatch between skills adaptation and literacy is the main cause of slow employment.To this end,research and design training programs,including curriculum restructuring,school-enterprise cooperation practice platforms,and employment-oriented quality improvement plans are imperative.At the same time,we will develop teaching modules for career planning to enhance competitiveness and ensure that the content is synchronized with industry standards.After the implementation,the employment rate of students increased by 15%within six months,the degree of interconnection increased by 20%,and the degree of enterprise recognition increased by 25%.The research effectively promotes the docking of higher vocational education with the market,and has far-reaching significance for easing slow employment. 展开更多
关键词 Higher vocational colleges Talent training strategy job market docking Vocational skills adaptation School-enterprise cooperation
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A Survey on Pharmacists’ Job Burnout and Satisfaction during the COVID-19 Era
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作者 Qirui Tai Mili Shi +2 位作者 Qiuyan Song Yijie Shi Huan Hu 《Pharmacology & Pharmacy》 2023年第8期239-251,共13页
Background: The COVID-19 outbreak negatively impacted pharmacists who provided basic medical services by inducing anxiety and depression, thus, leading to medical errors. Objective: This study aimed to investigate the... Background: The COVID-19 outbreak negatively impacted pharmacists who provided basic medical services by inducing anxiety and depression, thus, leading to medical errors. Objective: This study aimed to investigate the job burnout and satisfaction levels among hospital pharmacists during the period when China downgraded COVID-19 from a Category A disease to a Category B disease. Method: We selected pharmacists from several medical institutions in Yunnan Province as the subjects by using the general information questionnaire survey, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), and the Work Environment Scale-10 (WES-10). Results: After analyzing 461 questionnaires, the results showed that the age and marital status of the pharmacists displayed significant effects on their emotional exhaustion and sense of achievement, with younger pharmacists getting higher and lower scores for their tests on emotional exhaustion and sense of achievement, respectively (p Conclusion: Hence, it was concluded that the job burnout of pharmacists was at a low level during the period when China downgraded COVID-19 as a Category B disease from Category A. 展开更多
关键词 job Burnout COVID-19 PHARMACISTS job Satisfaction Questionnaire Investigation
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Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV 被引量:2
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作者 Qinhui Liu Nengjian Wang +3 位作者 Jiang Li Tongtong Ma Fapeng Li Zhijie Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第3期2073-2091,共19页
As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources... As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases. 展开更多
关键词 Segmented AGV flexible job shop improved genetic algorithm scheduling optimization
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A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling 被引量:2
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作者 CuiyuWang Xinyu Li Yiping Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第11期1849-1870,共22页
Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enabl... Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods. 展开更多
关键词 Multi-objective flexible job shop scheduling Pareto archive set collaborative evolutionary crowd similarity
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The Effects of Job Insecurity, Emotional Exhaustion, and Met Expectations on Hotel Employees’ Pro-Environmental Behaviors: Test of a Serial Mediation Model 被引量:1
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作者 Osman M.Karatepe Raheleh Hassannia +2 位作者 Tuna Karatepe Constanţa Enea Hamed Rezapouraghdam 《International Journal of Mental Health Promotion》 2023年第2期287-307,共21页
There are a plethora of empirical pieces about employees’pro-environmental behaviors.However,the extant literature has either ignored or not fully examined various factors(e.g.,negative or positive non-green workplac... There are a plethora of empirical pieces about employees’pro-environmental behaviors.However,the extant literature has either ignored or not fully examined various factors(e.g.,negative or positive non-green workplace factors)that might affect employees’pro-environmental behaviors.Realizing these voids,the present paper proposes and tests a serial mediation model that examines the interrelationships of job insecurity,emotional exhaustion,met expectations,and proactive pro-environmental behavior.We used data gathered from hotel customer-contact employees with a time lag of one week and their direct supervisors in China.After presenting support for the psychometric properties of the measures via confirmatory analysis in LISREL 8.30,the abovementioned linkages were gauged using the PROCESS plug-in for statistical package for social sciences.The findings delineated support for the hypothesized associations.Specifically,emotional exhaustion and met expectations partly mediated the effect of job insecurity on proactive pro-environmental behavior.More importantly,emotional exhaustion and met expectations serially mediated the influence of job insecurity on proactive pro-environmental behavior.These findings have important theoretical implications as well as significant implications for diminishing job insecurity,managing emotional exhaustion,increasing met expectations,and enhancing ecofriendly behaviors. 展开更多
关键词 Emotional exhaustion hotel customer-contact employees job insecurity met expectations pro-environmental behavior
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A flexible architecture for job management in a grid environment 被引量:2
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作者 LUAN Cui-ju SONG Guang-hua +1 位作者 ZHENG Yao ZHANG Ji-fa 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第1期95-105,共11页
Job management is a key issue in computational grids, and normally involves job definition, scheduling, executing and monitoring. However, job management in the existing grid middleware needs to be improved in terms o... Job management is a key issue in computational grids, and normally involves job definition, scheduling, executing and monitoring. However, job management in the existing grid middleware needs to be improved in terms of efficiency and flexibility. This paper addresses a flexible architecture for job management with detailed design and implementation. Frameworks for job scheduling and monitoring, as two important aspects, are also presented. The proposed job management has the advantages of reusability of job definition, flexible and automatic file operation, visual steering of file transfer and job execution, and adaptive application job scheduler. A job management wizard is designed to implement each step. Therefore, what the grid user needs to do is only to define the job by constructing necessary information at runtime. In addition, the job space is adopted to ensure the security of the job management. Experimental results showed that this approach is user-friendly and system efficient. 展开更多
关键词 Grid job management job definition reuse Steering of job transfer job space
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An Effective Neighborhood Solution Clipping Method for Large-Scale Job Shop Scheduling Problem
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作者 Sihan Wang Xinyu Li Qihao Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第11期1871-1890,共20页
The job shop scheduling problem(JSSP)is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems.It is a well-known NP-hard problem,when the number of jobs increa... The job shop scheduling problem(JSSP)is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems.It is a well-known NP-hard problem,when the number of jobs increases,the difficulty of solving the problem exponentially increases.Therefore,a major challenge is to increase the solving efficiency of current algorithms.Modifying the neighborhood structure of the solutions can effectively improve the local search ability and efficiency.In this paper,a genetic Tabu search algorithm with neighborhood clipping(GTS_NC)is proposed for solving JSSP.A neighborhood solution clipping method is developed and embedded into Tabu search to improve the efficiency of the local search by clipping the search actions of unimproved neighborhood solutions.Moreover,a feasible neighborhood solution determination method is put forward,which can accurately distinguish feasible neighborhood solutions from infeasible ones.Both of the methods are based on the domain knowledge of JSSP.The proposed algorithmis compared with several competitive algorithms on benchmark instances.The experimental results show that the proposed algorithm can achieve superior results compared to other competitive algorithms.According to the numerical results of the experiments,it is verified that the neighborhood solution clippingmethod can accurately identify the unimproved solutions and reduces the computational time by at least 28%. 展开更多
关键词 job shop scheduling MAKESPAN Tabu search genetic algorithm
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Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches
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作者 Bao Rong Chang Hsiu-Fen Tsai Yu-Chieh Lin 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第2期783-815,共33页
Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput.This paper aims to enhance the capability... Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput.This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems.First,integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing,which reduces the search scope of the database and dramatically speeds up data searching.Next,exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first,which reduces the average waiting time of job execution.As a result,the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing,significantly improving the speed of data retrieval up to 53%and increasing system throughput by 53%.On the other hand,the proposed job scheduling algorithmdefeats both first-in-first-out andmemory-sensitive heterogeneous early finish time scheduling algorithms,effectively shortening the average waiting time up to 5%and average weighted turnaround time by 19%,respectively. 展开更多
关键词 Stacked sparse autoencoder Elasticsearch distributed indexing data retrieval deep neural network job scheduling
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Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship
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作者 Yeonjee Choi Hyun Suk Hwang Chang Soo Kim 《Computers, Materials & Continua》 SCIE EI 2023年第3期4901-4916,共16页
A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated ... A small and medium enterprises(SMEs)manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities.The optimal job shop scheduling is generated by utilizing the scheduling system of the platform,and a minimum production time,i.e.,makespan decides whether the scheduling is optimal or not.This scheduling result allows manufacturers to achieve high productivity,energy savings,and customer satisfaction.Manufacturing in Industry 4.0 requires dynamic,uncertain,complex production environments,and customer-centered services.This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform.The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors.The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors:early delivery date and fulfillment of processing as many orders as possible.The genetic algorithm(GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem(JSSP)by comparing with the real-world data from a textile weaving factory in South Korea.The proposed platform will provide producers with an optimal production schedule,introduce new producers to buyers,and eventually foster relationships and mutual economic interests. 展开更多
关键词 Manufacturing platform job shop scheduling problem(JSSP) genetic algorithm optimization textile process
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An analysis of association of personality traits with job burnout among nurses working in hospital
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作者 Hossein AKBARI Masoud MOTALEBI KASHANI +2 位作者 Sedighe DEHGHANI BIDGOLI Masoumeh Koochaki NASRABADI Fahimeh KARAMALI 《Journal of Integrative Nursing》 2023年第1期43-49,共7页
Objective:The objective of the present study is to explore the effects of personality traits on job burnout among hospital nurses.Materials and Methods:This cross-sectional research was done during 2019-2020 at Kashan... Objective:The objective of the present study is to explore the effects of personality traits on job burnout among hospital nurses.Materials and Methods:This cross-sectional research was done during 2019-2020 at Kashan Shahid Beheshti Hospital.The data analysis procedures included descriptive statistics and the partial least squares-based structural equation modeling.The participants were 150 nursing professionals.A questionnaire indicating information on demographics,burnout(measured using the Maslach Burnout Inventory with three dimensions of depersonalization,emotional exhaustion,and personal accomplishment),and personality profile(measured employing the neuroticism extraversion openness five-factor inventory including extroversion,conscientiousness,agreeableness,neuroticism,and openness to experience dimensions)was used to gather the required data.Results:The results of the study showed that the validity and reliability of the measurement model were desirable(factor load higher than 0.5,the Cronbach’s alpha value and the composite reliability are>0.7).Structural model showed statistically drastic,negative relationship between the nurses’burnout levels and neuroticism(β=0.722)and openness to experience(β=0.437).However,the relationship was significantly positive between the nurses’burnout levels and conscientiousness(β=0.672),agreement(β=0.594),and extraversion(β=0.559)(P<0.03).Conclusions:The present study helped the recognition of burnout among nurses working in hospitals and approved the effects of personality features on the burnout experience. 展开更多
关键词 Hospital nursing job burnout nurses personality traits professional burnout
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