Traditional Job Research Articles

Application of Intelligent Collaboration Technology in Skill Training and Job Docking of Data Tagger

With the rapid development of artificial intelligence and machine learning technology, the role of data tagger has become more and more important, responsible for providing high-quality annotated data for training algorithms, but traditional skills training and job docking methods cannot meet the needs of the rapid development of the industry. This paper studies the application of intelligent collaboration technology in improving the skill training efficiency of data taggers and promoting its effective docking with the post. By using the literature review method, in-depth interview method, and case analysis method, this paper analyzes the current challenges faced by data taggers, including skill gaps, insufficient training resources, and rapid changes in market demand. The results show that the application of intelligent collaboration technology can not only improve the skill training efficiency of data taggers but also promote effective docking between them and their positions, bringing a positive impact to the data tagging industry.

Human Resource Development (HRD) Meets Human Resource Management (HRM): A Skills Based Agenda for a FRAGILE World Order

Problem HRD and HRM can sometimes operate in silos or at cross-purposes. The increasingly FRAGILE world order confronting business (i.e., Fragmenting boundaries; Resurgent populism; Accelerated digitalisation; Growing inequality; Inverted populations; Loss of Trust; and the Ecological crisis) mandates greater collaborative engagement across HRD and HRM. Solution Following a detailed review of the skills-based implications of the FRAGILE world order, the article unpacks the nascent skills-based agenda, detailing the transition from traditional job to task-based understanding, and outlining some examples of what it can look like in practice. Ultimately, to effectively navigate the FRAGILE world order organizations must have a clear understanding of both their current skill demands and deficiencies, and equally skills required of the future. Stakeholders This conceptual article will be of interest to HRD and HRM professionals, but equally hold broader insights for organizations and policy makers engaged in navigating the FRAGILE world order through a skills-based agenda.

Recommending Career Transitions to Job Seekers Using Earnings Estimates, Skills Similarity, and Occupational Demand

This paper describes a career recommendation algorithm that uses government administrative data to help job seekers discover new careers that similar job seekers have successfully switched to in the past. Algorithm development was motivated by workers and contractors who were displaced by the COVID-19 economic crisis and by workers in declining industries seeking new careers in growing ones. Traditional job boards available through state government websites list all available jobs but do little to remove uncertainty associated with moving to a new industry or occupation. Our recommendation algorithm can lower this uncertainty. It uses causal machine learning techniques and administrative data on the universe of individual-level employment histories and earnings to identify career transitions that have resulted in increased earnings and employment for previous job seekers. We combine these estimates with measures of skill similarity across occupations, derived from natural-language processing of millions of full-text job descriptions, and with occupational demand, as measured by nightly job posting volume. The algorithm parses applicant resumes and returns recommended careers that use similar skills, have available jobs, and are estimated to lead to higher earnings and employment. We have implemented our algorithm in production workforce development systems in five U.S. states.

Multi-level guided evolution algorithm for solving fuzzy flexible job shop problem

Traditional flexible job shop scheduling problems (FJSP) are mostly limited to deterministic environments. In actual production, some uncertain factors lead to changes in job processing time. When solving multi-objective fuzzy flexible job shop scheduling problems (MOFFJSP), the existing evolutionary algorithms do not fully utilize information transfer between levels during the population evolution process. Therefore, a multi-level guided evolutionary (MLGE) optimization method is proposed to solve MOFFJSP. In the proposed MLGE method, decomposition and dominance techniques are combined to balance the diversity and convergence performance of the algorithm. Meanwhile, the idea of the Jaya algorithm approaching good individuals and distancing poor individuals is introduced. Combined with improved Jaya operator operations, it is used to guide individual evolution, preserving and inheriting solutions that perform well in terms of convergence and diversity. Finally, numerous experiments are carried out to evaluate the effectiveness of MLGE. The results show that MLGE can provide promising results for MOFFJSP. Additionally, it shows how MLGE outperforms other comparison algorithms in terms of convergence and variety of solutions.

A Bibliometric and Literature Review on Unconventional Work Arrangements: Moonlighting

Traditional job security is no longer guaranteed in today’s rapidly changing job market. The COVID-19 crisis has made this even more evident, leading many workers to look for additional jobs to meet their personal needs and secure a stable source of income. The COVID-19 crisis reshaped the job landscape, and unconventional work arrangements like moonlighting have gained popularity. This study focuses on ‘moonlighting’, where individuals take on extra jobs to secure their financial future. We conducted comprehensive research utilising both bibliometric and literature reviews. Our investigation involved analysing 177 relevant documents identified from the Scopus database spanning 45 years. Biblioshiny, an RStudio software, analyses and visualises connections between papers and research studies through network diagrams. The findings reveal ‘Jean Kimmel’ as the most productive and efficient author, and the United States stands at the forefront of moonlighting studies, with their research studies leading the way. Applied economics is the major source in the field of moonlighting. Our contribution to the area of moonlighting in India is new and scarcely researched and published. This concept in India is specifically of concern in the IT industry, where moonlighting is catching fire because of legality issues.

Path Planning Algorithm of Orchard Fertilization Robot Based on Multi-Constrained Bessel Curve

Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot’s driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots.

Low-Carbon Flexible Job Shop Scheduling Problem Based on Deep Reinforcement Learning

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 completion times, they often overlook the energy consumption of machines. To address this gap, this paper introduces a novel solution utilizing deep reinforcement learning. The study begins by defining the Low-carbon Flexible Job Shop Scheduling problem (LC-FJSP) and constructing a disjunctive graph model. A sophisticated representation, based on the Markov Decision Process (MDP), incorporates a low-carbon graph attention network featuring multi-head attention modules and graph pooling techniques, aimed at boosting the model’s generalization capabilities. Additionally, Bayesian optimization is employed to enhance the solution refinement process, and the method is benchmarked against conventional models. The empirical results indicate that our algorithm markedly enhances scheduling efficiency by 5% to 12% and reduces carbon emissions by 3% to 8%. This work not only contributes new insights and methods to the realm of low-carbon manufacturing and green production but also underscores its considerable theoretical and practical implications.

A STUDY ON COMPETENCY MAPPING

Competency mapping has emerged as a crucial tool for aligning individual skills with organizational objectives. In today's rapidly evolving business landscape, organizations are continually challenged to enhance employee performance to remain competitive and achieve their strategic goals. The study focuses on competency mapping in HYUNDAI WIA with 217 respondents. This abstract outlines a comprehensive initiative aimed at competency mapping and its profound impact on employee performance. The conceptual foundations of competency mapping, exploring its evolution from traditional job descriptions to dynamic skill-based models aligned with contemporary business needs. The study investigates how organizations identify, assess, and leverage competencies to enhance employee performance, drive talent management strategies, and achieve competitive advantages. The findings of this study have practical implications for HR practitioners, organizational leaders, and policymakers seeking to optimize human capital management strategies and drive sustainable business growth in a rapidly evolving global landscape. The study aims to highlight certain behavioral characteristics that influence competency mapping performance. This study encompasses vital part of behavioral characteristics enhancing the performance of competency mapping as a strategic tool for organization. Key Words—Behavioral Traits, Competence Mapping, Competence, Employee Performance

A STUDY ON COMPETENCY MAPPING WITH REFERENCE TO HYUNDAI WIA

Competency mapping has emerged as a crucial tool for aligning individual skills with organizational objectives. In today's rapidly evolving business landscape, organizations are continually challenged to enhance employee performance to remain competitive and achieve their strategic goals. The study focuses on competency mapping in HYUNDAI WIA with 217 respondents. This abstract outlines a comprehensive initiative aimed at competency mapping and its profound impact on employee performance. The conceptual foundations of competency mapping, exploring its evolution from traditional job descriptions to dynamic skill-based models aligned with contemporary business needs. The study investigates how organizations identify, assess, and leverage competencies to enhance employee performance, drive talent management strategies, and achieve competitive advantages. The findings of this study have practical implications for HR practitioners, organizational leaders, and policymakers seeking to optimize human capital management strategies and drive sustainable business growth in a rapidly evolving global landscape. The study aims to highlight certain behavioral characteristics that influence competency mapping performance. This study encompasses vital part of behavioral characteristics enhancing the performance of competency mapping as a strategic tool for organization. Key Words—Behavioral Traits, Competence Mapping, Competence, Employee Performance

Dynamic Intelligent Scheduling in Low-Carbon Heterogeneous Distributed Flexible Job Shops with Job Insertions and Transfers.

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method.

Analysing the Impact of Industrial Revolution 4.0 on Traditional Occupations and Community Adaptation Efforts in Bali

This research investigates the impact of Industrial Revolution 4.0 on traditional occupations in Bali, employing a quantitative approach with Structural Equation Modeling and Partial Least Squares (SEM-PLS) analysis. A diverse sample of 250 individuals engaged in agriculture, crafts, and tourism provides insights into the multifaceted nature of Bali's traditional job sectors. The measurement model assessment confirms the reliability and validity of latent variables, including technological adoption, changes in employment patterns, income levels, and community adaptation efforts. The structural model analysis reveals a positive influence of technological adoption on changes in employment patterns and income levels, indicating transformative opportunities within traditional occupations. However, challenges arise in maintaining income equity amidst shifts in employment patterns. The study also highlights the proactive response of communities, with technological adoption positively correlating with effective adaptation efforts. The model fit and sensitivity analysis affirm the robustness of the findings. This research contributes nuanced insights for policymakers and community leaders navigating the intersection of tradition and technological progress in Bali's evolving socio-economic landscape.

Structured shadowing as a pedagogy

In this article, we introduce and discuss the potential benefits of structured shadowing, a distinct pedagogy in which the action-proximity of traditional unstructured job shadowing is supplemented by carefully designed pre-, intra- and post-shadowing pedagogical support. We suggest that structured shadowing is a promising yet under-utilized and overlooked pedagogy to enrich management learning and education. Drawing on an interview-based evaluation study of several cohorts of final-year undergraduates in a UK business school, we find that structured shadowing helps students to establish meaningful connections between theory and managerial practices, better appreciate management’s complexities and dispel existing myths and preconceptions. It also allows them to reflect on the types of managers they imagine or aspire to be and helps to model management as a reflective activity. Based on our teaching experience and our results, we argue that structured shadowing offers valuable lessons for our field. It helps to address the challenges of substance, contextual understanding and reflection, which we identify as central to current management education debates. We also acknowledge that while structured shadowing is a powerful resource, it demands significant investment and potential trade-offs, and may reflect certain professional privileges.

Transgenic Algorithm Applied to the Job Shop Rescheduling Problem

Context: Job sequencing has been approached from a static perspective, without considering the occurrence of unexpected events that might require modifying the schedule, thereby affecting its performance measures. Method: This paper presents the development and application of a genetic algorithm to the Job Shop Rescheduling Problem (JSRP), a reprogramming of the traditional Job Shop Scheduling Problem. This novel approach seeks to repair the schedule in such a way that theoretical models accurately represent real manufacturing environments. Results: The experiments designed to validate the algorithm aim to apply five classes of disruptions that could impact the schedule, evaluating two performance measures. This experiment was concurrently conducted with a genetic algorithm from the literature in order to facilitate the comparison of results. It was observed that the proposed approach outperforms the genetic algorithm 65% of the time, and it provides better stability measures 98% of the time. Conclusions: The proposed algorithm showed favorable outcomes when tested with well-known benchmark instances of the Job Shop Scheduling Problem, and the possibility of enhancing the tool's performance through simulation studies remains open.

Crossmediale Neuausrichtung des Südwestrundfunks. Change Management, Kommunikation und Weiterbildung im strategischen Unternehmenswandel

In the digital age public broadcasters must align their structures and content cross-medially. Whether they succeed in this cross-media transformation depends, among other things, on the success of their change management. How Südwestrundfunk (SWR) communicates cross-media change to its employees and which cross-media training courses exist at SWR is examined in this article using a multi-method design consisting of participatory editorial observations and expert interviews with managers from several hierarchical levels. The results show that SWR attaches great importance to transparency and offers its employees a broad repertoire of further training measures, such as traditional workshops, peer-to-peer training, and job rotation.

Beyond traditional interviews: Psychometric analysis of asynchronous video interviews for personality and interview performance evaluation using machine learning

With the advent of new technology, traditional job interviews have been supplemented by asynchronous video interviews (AVIs). However, research on psychometric properties of AVIs is limited. In this study, 710 participants completed a mock AVI responding to eight personality questions (Extraversion, Conscientiousness). We collected self- and observer reports of personality, interview performance ratings, attractiveness, and AVI meta-information (e.g., professional attire, audio quality). Then, we automatically extracted the words, facial expressions, and voice characteristics from the videos and trained machine learning models to predict the personality traits and interview performance. Our algorithm explained substantially more variance in observer reports of Extraversion and Conscientiousness (average R2 = 0.32) and interview performance (R2 = 0.44), than self-reported Extraversion and Conscientiousness (average R2 = 0.12). Consistent with Trait Activation Theory, the explained variance in personality traits increased when participants responded to trait-relevant, compared to trait-irrelevant, questions. The test-retest reliability of our algorithm was somewhat stable over a time period of seven months, but lower than desired reliability standards in personnel selection. We examined potential sources of bias, including age, gender, and attractiveness, and found some instances of algorithmic bias (e.g., gender differences were often amplified in favor of women).

Personalized Job Opportunity Finder powered by Web Scraping

"Personalized Job Opportunity Finder powered by Web Scraping" research presents a comprehensive exploration the development and implementation of an innovative Job Portal, aiming to redefine the traditional job search experience. The project leverages cutting-edge techniques and integration with prominent communication tools. The experimental setup involves meticulous web scraping from renowned job portals, Google and Apple, using Puppeteer and Cheerio for data extraction. The user interface is thoughtfully designed, featuring an intuitive registration form and a dynamic home page that showcases personalized job recommendations. The research delves into the advantages of scraping job data from top companies, showcasing its efficacy compared to traditional methods such as partnerships and direct job postings. Big companies such as Google, Apple and Meta don't use partnerships for most of its hiring, that makes it difficult for candidates who aspire careers at such companies. Direct partnerships may have delays in updating job listings, whereas scraping allows for more immediate access to new postings.

Status of Sanitation Workers: A Case Study of Kathmandu Valley

Sanitation refers to the provision of services for the safe disposal of urine and feces. It also refers to the maintenance of hygienic conditions through different services such as garbage collection and waste water disposal. It is necessary to provide a healthy environment for people as well as protect the natural resources. A sanitation worker is a person which responsible for cleaning and maintaining the environment through sanitation services. This study aims to explore the socio-economic status of sanitation workers and to examine their livelihood means that help their living. For this study, both quantitative and qualitative exploration approaches are applied. The observation, interview as well as case studies are applied to the study. The findings of the study concluded that sanitation occupation is a traditional job for employment and reducing poverty in the civic area but sanitation workers also have to face numerous problems and they’ve different pitfalls which are nominated as the vulnerable environment of sanitation workers. This study helps the government and non-government agencies to make programs and mitigation measures.

Организация мелкосерийного производства деталей на принципах модульной технологии

It is emphasized that the traditional job shop production is characterized by low efficiency due to the use of superuniversal machining facilities for their underemployment of performance capabilities at work and also low-productivity of process flowsheet when machining part surfaces as a sequential surfaced job, the organizational form of production in the form of sections by type of machine tools, i.e. a section of lathes, a section of milling machines, etc. It results in large travel length of workpieces in the machining process at work. The desire to increase the efficiency of production by organizing launch of batch workpieces that are formed in a constructive similarity, such as structures, parts of rotation body types, etc., does not have a significant effect, because the task is just minimizing the run time spent on changing fixtures and reinstalling workpieces, but not taking into account an optimal sequence of parts in the batch. For increasing the efficiency of small-scale production, the module technology is proposed. The main idea of it is to represent the part not as a network of separate surfaces, but as surface modules, where the surface module is understood as a combination of surfaces and the part performs its proper function with the help of this module. This enables a good specialization of workplaces that are organized not for processing methods, but for manufacturing the appropriate groups of surface modules. The latter make it possible to take advantage of a more productive process flowsheet for machining part surfaces. It is sequential parallel surface job that is used. When organizing production, it becomes possible to use the layout of workplaces in line according to the in-line method, which will significantly reduce the part travel length at workplaces.

Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.

Multi-objective green scheduling of integrated flexible job shop and automated guided vehicles

The traditional flexible job shop scheduling problem (FJSP) ignores transportation issues or merely introduces a time lag for transportation tasks while assuming an infinite number of transportation resources. With the development of intelligent manufacturing, automated guided vehicles (AGVs), which are the key transportation equipment for manufacturing enterprises, have been widely used for their high flexibility and stability. In addition, the increase in energy consumption and the trend of green manufacturing make it critical to take into account energy-related objectives in the decision-making of scheduling. Therefore, the multi-objective green scheduling problem of integrated flexible job shop and AGVs (MOGSP-IFJS&AGVs) is addressed in this paper. To solve this problem effectively, the multi-objective mixed-integer programming (MMIP) model is formulated to minimize total energy consumption and makespan simultaneously. An efficient heuristic algorithm (EHA) is designed to solve the MMIP model. In the EHA, one solution encoding scheme and corresponding greedy insertion decoding method considering the selection of AGVs are presented. To acquire a high-quality initial population, the population initialization method balancing the processing time and energy consumption is designed. Further, a local search strategy is presented to enhance the quality of solutions and accelerate the convergence speed of the EHA. Experiment results of 45 test instances indicate that the EHA can obtain better solutions than that of comparison algorithms, which confirms the effectiveness of the EHA for solving the MOGSP-IFJS&AGVs.