This paper examines how AI at work impacts on-the-job learning, shedding light on workers’ reactions to the groundbreaking AI technology. Based on theoretical analysis, six hypotheses are proposed regarding three aspects of AI’s influence...
11 October 2022
(1) Background: Practicing physicians have not been in the focus of structured qualifications in basic digital competences so far. However, they are the current gatekeepers to implement digital technologies and need empowerment to proactively take pa...
Summer Youth Employment Programs (SYEPs) operate in most major U.S. cities and are known to build social–emotional and job skills in youth while reducing crime. Integrating STEM learning and summer employment offers a promising way to increase...
Employee skill development has become essential for organizations operating in dynamic, knowledge-based environments, particularly in developing countries, where on-the-job training (OJT) remains the most widely used training approach. Despite its pr...
Employee behavior plays an important role in the occurrence and prevention of incidents, affecting safety margins. In this study, we examine the potential impact of incidental learning on human behavior in the presence of variation in task design. In...
The civil and environmental engineering disciplines have identified the levels of knowledge about sustainability that are desirable for students to achieve as they graduate with a bachelor’s degree, as well as sustainability-related competencies to...
Personality represents enduring patterns, providing insights into an individual’s aptitude and behavior. Integrating these insights with learning tendencies shows promise in enhancing learning outcomes, optimizing returns on investment, and red...
Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of e...
18 February 2022
Public safety personnel (PSP) experience a disproportionately high number of on-the-job stressors compared to the general population. PSP develop self-initiated actions, or coping strategies, that either confront the situation (approach strategies) o...
Management systems containing procedures, checklists, and descriptions for how various tasks should be conducted are often used in high-risk industries. Much has been written about the judicial and technological concerns of management systems, but le...
This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement pro...
Currently the largest workforce in the workplace, the millennial generation is perceived as being different to the preceding generations in the workplace, namely, baby boomers and generation X. Millennials are also seen as the leadership pipeline, ye...
The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the cha...
The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often...
There are currently many job portals offering job positions in the form of job advertisements. In this article, we are proposing an approach to mine data from job advertisements on job portals. Mainly, it would concern job requirements mining from in...
Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boa...
The COVID-19 epidemic has not only triggered a worldwide health catastrophe, it has also affected learning processes all over the world. While COVID-19 was in its initial wave of transmission, educational systems around the world witnessed bitter exp...
To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable...
In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel a...
The dynamic job-shop scheduling problem is a complex and uncertain task that involves optimizing production planning and resource allocation in a dynamic production environment. Traditional methods are limited in effectively handling dynamic events a...
An extended flexible job scheduling problem is presented with characteristics of technology and path flexibility (dual flexibility), varied transportation time, and an uncertain environment. The scheduling can greatly increase efficiency and security...
The flexible job shop scheduling problem (FJSP), one of the core problems in the field of generative manufacturing process planning, has become a hotspot and a challenge in manufacturing production research. In this study, an improved self-learning g...
The purpose of the study was to examine the relationship between the school as a learning organisation (SLO) and teacher job satisfaction in general education in Latvia. This study also examined teacher job satisfaction depending on the school type (...
In a production environment, scheduling decides job and machine allocations and the operation sequence. In a job shop production system, the wide variety of jobs, complex routes, and real-life events becomes challenging for scheduling activities. New...
In large-scale, distributed high-performance computing systems, the increasing complexity of job scheduling has expanded along with the growth of computational resources and job diversity. While heuristic scheduling strategies with various optimizati...
Modern applications, such as smart cities, home automation, and eHealth, demand a new approach to improve cloud application dependability and availability. Due to the enormous scope and diversity of the cloud environment, most cloud services, includi...
Job shop scheduling problem (JSSP) is essential in the production, which can significantly improve production efficiency. Dynamic events such as machine breakdown and job rework frequently occur in smart manufacturing, making the dynamic job shop sch...
Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The ori...
As the focus on environmental sustainability sharpens, the significance of low-carbon manufacturing and energy conservation continues to rise. While traditional flexible job shop scheduling strategies are primarily concerned with minimizing completio...
This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasi...
Intelligent manufacturing promises to revolutionize production processes, and it is expected to enhance efficiency and productivity. In this paper, we study the job-shop scheduling problem, which is a key enabler to the realization of intelligent man...
The job scheduler plays a vital role in high-performance computing platforms. It determines the execution order of the jobs and the allocation of resources, which in turn affect the resource utilization of the entire system. As the scale and complexi...
In today’s fast-paced and evolving job market, salary continues to play a critical role in career decision-making. The ability to accurately classify job titles and predict corresponding salary ranges is increasingly vital for organizations see...
To address the scheduling problem of dynamic flexible job shop, this study proposes a hybrid scheduling method that integrates an adaptive genetic algorithm, dynamic target smoothing, and a deep Q-network (DQN). The scheduling process is formulated a...
Purpose: This study explored the potential long-term effects of academic-related variables, including academic satisfaction, college degree attainment, unmet academic accommodation needs, and demographic characteristics on the job and career satisfac...
Anticipating the demand for professional job market skills needs to consider trends such as automation, offshoring, and the emerging Gig economy, as they significantly impact the future readiness of skills. This article draws on the scientific litera...
The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization problem widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal...
The dynamic flexible job-shop problem (DFJSP) is a realistic and challenging problem that many production plants face. As the product line becomes more complex, the machines may suddenly break down or resume service, so we need a dynamic scheduling f...
The flexible job shop scheduling problem is a typical and complex combinatorial optimization problem. In recent years, the assembly problem in job shop scheduling problems has been widely studied. However, most of the studies ignore the learning effe...
The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. F...
This paper considers the single-group scheduling models with Pegels’ and DeJong’s learning effect and the single-group scheduling models with Pegels’ and DeJong’s aging effect. In a classical scheduling model, Pegels’ an...
With the rapid development of modern industrialization in our country and the continuous improvement of people’s living standards, the changing market has put forward new requirements for traditional manufacturing enterprises. The order product...
The job shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization challenge that plays a crucial role in manufacturing systems. Deep reinforcement learning has shown great potential in solving this problem. However, it still ha...
Dynamic scheduling for flexible job shops under machine breakdown is a complex and challenging problem due to its valuable application in real-life productions. However, prior studies have struggled to perform well in changeable scenarios. To address...
Background/Objectives: All countries face a shortage of qualified nurses. Based on the social cognitive career theory (SCCT), it is assumed that individual and environmental aspects are interlinked and determinants in career choice and vocational beh...
This study proposes an optimization framework based on Multi-agent Deep Reinforcement Learning (MADRL), conducting a systematic exploration of FJSP under dynamic scenarios. The research analyzes the impact of two types of dynamic disturbance events&m...
This study explores the relationships among transformational leadership, professional learning community (PLC) components, and teachers’ job satisfaction from Chinese principals’ perspective. A survey was conducted among 572 principals fr...
Information and Communication Technologies (ICT) provide different opportunities to students with intellectual disabilities and to professionals who work with them. However, few studies address the use of collaborative learning platforms and handheld...
This study examined how learning transfer and its antecedents impacted job competency among geriatric caregivers who underwent dementia training. The dementia training program of the National Health Insurance Service of South Korea was selected for t...
The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumpt...
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