Dynamic Job Research Articles

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

The Role of Continuing Education in Lifelong Learning: A Quantitative Study in Two Universities in China

This quantitative study explores the role of continuing education (CE) in fostering lifelong learning among students in two universities in China: Hunan University of Science and Technology and Changsha University of Science and Technology. The research investigates students' attitudes, perceptions, and engagement levels towards continuing education, aiming to provide insights into the effectiveness of existing CE programs and identify areas for improvement. A structured questionnaire was administered to 300 undergraduate and graduate students, utilizing Likert scale responses. Results indicate a balanced representation across gender identities, with varying levels of engagement across academic years and fields of study. The majority of respondents report prior experience with CE, indicating proactive engagement in lifelong learning activities. Findings also reveal a generally positive outlook towards lifelong learning, with high levels of agreement on the importance of self-directed learning, adaptability, and openness to new ideas. Engagement with CE activities is predominantly positive, with strong participation in industry conferences, online courses, and additional certifications. Participants perceive CE as positively influencing academic performance, career prospects, and personal growth. The study underscores the significance of integrating CE initiatives into educational frameworks to support holistic student development and promote lifelong learning. It emphasizes the importance of addressing barriers to access and enhancing skills development within CE programs to empower students for success in today's dynamic job market.

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

Impacts of NEP 2020 on Vocational Education and Emerging role of counsellors

The National Education Policy (NEP) 2020 in India has brought about significant reforms aimed at transforming the educational landscape, particularly in the realm of vocational education. This paper provides an in-depth analysis of the impact of NEP 2020 on vocational education and the emerging roles of counsellors within this context. NEP 2020 emphasizes the integration of vocational education into mainstream curriculum from the secondary level onwards, aiming to equip students with practical skills and job-oriented training. This shift necessitates a revaluation of the role of counsellors, who are now tasked with guiding students towards diverse career paths and facilitating their transition into the workforce. Additionally, NEP 2020 underscores the importance of providing holistic support to students, including psychometric assessments, career guidance, and skill development. The paper examines the challenges and opportunities presented by NEP 2020 in implementing vocational education initiatives and highlights the crucial role of counsellors in supporting students' career aspirations and personal development. Furthermore, it discusses the need for counsellor training programs to adapt to the changing educational landscape and equip counsellors with the skills and knowledge required to effectively fulfil their emerging roles. Overall, this paper provides valuable insights into the transformative potential of NEP 2020 in shaping vocational education and the evolving responsibilities of counsellors in guiding students towards success in the dynamic job market of the 21st century.

Federated deep reinforcement learning for dynamic job scheduling in cloud-edge collaborative manufacturing systems

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory's scheduling performance by sharing general scheduling knowledge among heterogeneous factories under the consideration of data privacy protection remains challenging. To this end, this paper proposes to solve the dynamic job scheduling in the context of CCMS with a novel federated deep reinforcement learning (FDRL) approach. Within each factory, the scheduling objective involves minimising the makespan and energy consumption, accounting for machine warm-up procedures and real-time dynamics. To handle heterogeneous policy structures, we aggregate their hidden parameters through FDRL, with states, actions, and rewards designed to facilitate the aggregation. The two-phase algorithm, comprising iterative local training and global aggregation, trains the scheduling policies. Constraint items are introduced to the loss functions to smooth local training, and the global aggregation considers production scales and obtained objectives. The proposed approach enhances the solution quality and generalisation of each factory's scheduling policy without exposing original production data. Numerical experiments conducted on sixty scheduling instances validate the superiority of the proposed approach compared to twelve dynamic scheduling methods. Compared to independently trained DRL-based approaches, the proposed FDRL-based approach achieves up to an 8.9% reduction in makespan and a 22.3% decrease in energy consumption through knowledge sharing.

An Auction-Based Approach for Multi-Agent Uniform Parallel Machine Scheduling with Dynamic Jobs Arrival

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents. All agents are self-interested and rational with the aim of maximizing their own objectives, resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information. Within the context, a centralized scheduling approach is unfeasible, and a decentralized approach is considered to deal with the targeted problem. This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’ preferences under incomplete information. For this purpose, a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed. In the proposed approach, a dynamic auction procedure is established for dynamic jobs participating in a real-time auction, and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination. In addition, an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently. A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids. Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on large-scale problems with numerous consumer agents and jobs. A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Dynamic Job Shop Scheduling Problem With New Job Arrivals Using Hybrid Genetic Algorithm

Dynamic Job Shop Scheduling Problem With New Job Arrivals Using Hybrid Genetic Algorithm

Dynamic Jobs of Ladies in the Development of Consumable and Restorative Mushrooms of Focal India as a Pay Age Movement

Dynamic Jobs of Ladies in the Development of Consumable and Restorative Mushrooms of Focal India as a Pay Age Movement

Dynamic Job Market Signaling and Optimal Taxation

Dynamic Job Market Signaling and Optimal Taxation

Workload-Aware Scheduling of Real-Time Jobs in Cloud Computing to Minimize Energy Consumption

Cloud computing is a powerful paradigm that can provide high-quality computation services to customers. Because its energy consumption has a large effect on its service price, this study investigates how to minimize the energy consumption while achieving adequate response times for requested computations. Such a problem is formulated as a nonlinear integer program. By deriving a state transition equation, this problem is transformed into an unconventional 0-1 knapsack problem, and dynamic programming is then used to solve it. In addition to this solution, we develop an energy-efficient job accommodation scheme that can manage dynamic jobs with varying frequencies throughout a day. Unlike existing studies that abruptly switch off old virtual machines and create new ones for upcoming jobs, this scheme tries to accommodate them with current virtual machines, and new virtual machines are not created unless necessary. Conversely, when the workload declines, jobs on energy-inefficient servers are moved to other servers, such that some energy-inefficient servers can be switched off to save energy. This scheme adjusts the computing power adaptively and smoothly without lowering the system’s quality of service. Experimental results demonstrate that the proposed solution outperforms a particle swarm optimizer and two other heuristics in terms of accommodating jobs and saving energy consumption.

Forward Singapore: Strengthening SkillsFuture

This practitioner note provides an overview of developments in the SkillsFuture movement. Particularly, it details how innovations in the skills ecosystem are shifting lifelong learning to a higher gear and enabling greater ownership of one’s skills and career health. Frequent tech disruptions, shortened half-life of skills and knowledge, and new job roles emerging every day mean our workforce must retool at scale and at speed. To be nimble and adaptable enough to support the timely reskilling and upskilling of Singapore’s 2.5 million resident workforce, SkillsFuture Singapore is working with training, industry, and tripartite partners to enhance the understanding of what skills we need as a national workforce, and use this knowledge as an advantage to stay ahead of the curve. However identifying skills in demand is not enough in this dynamic job market. To achieve agility and responsiveness of the skills ecosystem, there are collective efforts with industry leaders, training providers, and skills-tech providers to strengthen the skills demand-supply coordination. As part of the Forward Singapore agenda, this collective awareness of skill trends and enhanced demand-supply coordination will ultimately strengthen Singapore’s competitive advantage, and workforce mobility and resilience.

Large-Scale Dynamic Scheduling for Flexible Job-Shop With Random Arrivals of New Jobs by Hierarchical Reinforcement Learning

As the intelligent manufacturing paradigm evolves, it is urgent to design a near real-time decision-making framework for handling the uncertainty and complexity of production line control. The dynamic flexible job shop scheduling problem (DFJSP) is frequently encountered in the manufacturing industry. However, it is still challenging to obtain high-quality schedules for DFJSP with dynamic job arrivals in real-time, especially facing thousands of operations from a large-scale scene with complex contexts in an assembly plant. This paper aims to propose a novel end-to-end hierarchical reinforcement learning framework for solving the large-scale DFJSP in near real-time. In the DFJSP, the processing information of newly arrived jobs is unknown in advance. Besides, two optimization tasks, including job operation selection and job-to-machine assignment, have to be handled, which means multiple actions must be controlled simultaneously. In our framework, a higher-level layer is designed to automatically divide the DFJSP into sub-problems, i.e., static FJSPs with different scales. And two lower-level layers are constructed to solve the sub-problems. In particular, one layer based on a graph neural network is in charge of sequencing job operations, and another layer based on a multi-layer perceptron is used to assign a machine to process the job operations. Numerical experiments, including offline training and online testing, are conducted on several instances with different scales. The results verify the superior performance of the proposed framework compared with existing dynamic scheduling methods, such as well-known dispatching rules and meta-heuristics.

Multitask Linear Genetic Programming with Shared Individuals and its Application to Dynamic Job Shop Scheduling

Multitask genetic programming methods have been applied to various domains, such as classification, regression, and combinatorial optimization problems. Most existing multitask genetic programming methods are designed based on tree-based structures, which are not good at reusing building blocks since each sub-tree passes its outputs to only one parent. It may limit the design and performance of knowledge sharing in multitask optimization. Different from tree-based genetic programming, building blocks in linear genetic programming can be easily reused by more than one parent. Besides, existing multitask genetic programming methods always allocate each individual to a specific task and have to duplicate genetic materials from task to task in knowledge transfer, which is inefficient and often produces redundancy. Contrarily, it is natural for a linear genetic programming individual to produce multiple distinct outputs, which enables each linear genetic programming individual to solve multiple tasks simultaneously. With this in mind, we propose a new multitask linear genetic programming method that transfers knowledge via multi-output individuals (i.e., shared individuals among tasks). By integrating different solutions into one multi-output individual, the proposed method efficiently reuses common knowledge among tasks and maintains distinct behaviors for each task. The empirical results show that the proposed method has a significantly better test performance than state-of-the-art multitask genetic programming methods. Further analyses verify that the new knowledge transfer mechanism can adjust the transfer rate automatically and thus improves its effectiveness.

Catalyzing India's Progress: Integrating Youth Empowerment, Skill-Based Education, and Economic Development Initiatives

India, home to the world's largest youth population, recognizes its youth demographic as a pivotal 'window of opportunity' for national growth and development. The challenge lies in proactively addressing the multifaceted needs of this demographic to harness its potential effectively. This paper explores the intersection of youth, skill-based education, and national development, emphasizing the critical role of skill-based education in equipping the youth for a dynamic job market and fostering comprehensive socio-economic development. The National Education Policy 2020 serves as a transformative milestone, envisioning a holistic education system aligned with 21st-century goals. Key features include universal access to schooling, vocational integration, and targeted literacy goals, reflecting a commitment to nurturing a skilled and enlightened youth. The Micro, Small, and Medium Enterprises (MSME) sector emerges as a crucial player in inclusive industrial development, providing substantial employment opportunities and contributing significantly to the nation's economic fabric. The state of Odisha aligns with the national vision, undertaking initiatives contributing to the overarching goal of youth development and community engagement..The impact of these endeavors not only contributes to economic growth but also lays the groundwork for a socially conscious and globally competitive nation.

Ethics in Artificial Intelligence

The contemporary discourse is saturated with discussions on artificial intelligence (AI). A mere two months after the introduction of ChatGPT, a January 2023 survey revealed that 89 percent of students were utilizing it for various academic tasks such as essay composition, paper outlining, and completing take-home tests. Traditionally, humanities courses emphasized cognitive skills like reading, researching, and articulating thoughts in eloquent prose. However, the landscape has evolved, enabling the application of sophisticated prompt engineering and AI models for tasks such as formulating questions, summarizing intricate research articles, presenting the pros and cons of an argument, and crafting essays with citations. This paradigm shift extends beyond academia, impacting the dynamic job market and prompting significant changes in higher education. Contrary to earlier beliefs that automation primarily threatened manual labor, the recent transformation encompasses intellectual tasks across diverse sectors. White-collar jobs, once considered secure for college graduates, are now susceptible to automation, affecting areas ranging from coding to essential humanities skills highly valued by employers. Notably, an OpenAI study revealed that 80 percent of US workers could witness at least a 10 percent automation of their tasks.

Self-directed learning and job exploration among Nepalese youth: South Asian perspective

The study examined the Nepalese youth’s self-directed learning and job-seeking characteristics. It collected primary data from fresh graduates of Nepal’s leading universities to understand self-directed learning motivations and job search behavior. It focused on graduates enrolled in different job preparatory programs to improve their chances of securing employment in 2022. Using a five-point Likert scale, the survey covered general information, self-learning, and job-searching. The purposive sample field survey in Kathmandu Valley from July to December 2022 targeted 500 respondents and yielded 327 completed surveys for analysis. Data analysis shows a substantial correlation between self-directed learning and job-seeking among Nepalese young people (β = 0.877, CR = 18.203, p < 0.01). A substantial positive correlation was identified between self-monitoring, adaptability, and job-seeking activity (β = 0.927, p = 0.000). Likewise, this study found a substantial positive correlation between motivation and job-seeking activity (β = 0.887, p < 0.00), and goal setting and planning have a significant favorable effect on job-seeking behavior (β = 0.445, p < 0.00). Such findings contribute valuable insights into the interplay of self-learning elements and their implications for the job-seeking behavior of young individuals in the Nepalese context. The importance of promoting self-directed learning and emphasizing fundamental attributes has grown significantly as young individuals navigate the dynamic job marketplace and seek assistance in integrating into employment. Acknowledgment The authors would like to express profound gratitude and appreciation to the University Grant Commission (UGC) Nepal for supporting this research (FRG-78/79-Mgmt 02).

Region-aware dynamic job scheduling and resource efficiency for load balancing based on adaptive chaotic sparrow search optimization and coalitional game in cloud computing environments

Uneven distribution of workloads represents a significant challenge in cloud data centers, negatively impacting the efficient utilization of resources. To address this problem and optimize the performance-to-profit (PP) ratio, we introduced the Regional Awareness dynamic Scheduling Algorithm (RASA) through a three-phase approach. In the initial phase, tasks are categorized based on critical parameters like CPU usage, memory utilization, and I/O operations, using a task classification model. The second phase involves estimating the CPU, memory, and I/O capacities of each node. Subsequently, a unique merge-and-split-based coalitional game-theoretic process is employed to group nodes into clusters. In the final phase, we have improved task placement by introducing an enhanced Sparrow Search Algorithm (ISSA) to overcome the slow convergence and local optimization issues found in the traditional SSA. We have incorporated the Tent chaotic mapping approach to enhance both global and local search efficiency during this stage. Additionally, we have fine-tuned fair load distribution using the Cauchy mutation process. We performed simulations using the CloudSim toolkit, and the results underscore the RASA algorithm’s superiority in terms of how quickly it converges its precision in task placement and its ability to distribute workloads fairly. Our proposed algorithm achieves a 9% reduction in latency overhead, a 14% decrease in processing time, a 15% reduction in workload imbalance, a 19% decrease in energy consumption, and a 26% decrease in idle periods. Furthermore, it enhances resource availability and efficiency by 22% and 27% respectively, while simultaneously boosting service throughput by 32%.

Curvempirical Transform for Multimodal Fusion of Brain Images

Aims: Medical imaging requires special operating procedures and can cause mis-images that occur when someone is getting imaged, which can lead to inaccurate results Background: Adaptive illustration of the signal is imperative in signal processing. Empirical Wavelet Transform (EWT) is a new-fangled adaptive signal decomposition technique. Objective: Brain image fusion understands a dynamic job in medical imaging applications by assisting radiologists in detecting the variation in CT and MR images. Methods: This paper presents a fusion of filter banks of CT-MR image modalities of the Brain using the Empirical Curvelet Transform and Hybrid technique. In the hybrid technique filter banks of CT curvelet-MR little wood and CT little wood -MR curvelet were fused. The images were preprocessed using the Top Hat transform technique. The evaluation was performed based on the performance evaluation parameter. PSNR and SSIM are considered performance evaluation parameters Results: It has been observed that the results of fused filter banks using the curvelet technique show remarkable results in terms of PSNR and SSIM. The fused results show 29.10 dB PSNR and 0.819 SSIM. Conclusion: It has been observed that the fusion using only curvelet results in a 47.25% improvement in comparison with CT curvelet-MR little wood and a 42.68% improvement in comparison with CT little wood -MR curvelet.

Integrated optimization of production scheduling and maintenance planning with dynamic job arrivals and mold constraints

Effective management of production often requires harmonizing maintenance and production activities. However, decsions on production scheduling and machine maintenance are usually made independently, leading to potential overlaps and inefficiencies. This paper joinly investigates the production scheduling and maintenance planning for a production machine subject to different types of jobs that arrive randomly. A unique emphasis is placed on the prerequisite of loading specific molds prior to job execution, an element often overlooked in previous studies. Maintenance considerations employ the reliability/availability framework. The overarching goal is the creation of an integrated schedule that minimizes the weighted sum of total costs and the machine's maximum unavailability. To address this intricate challenge, a novel hybrid differential evolution and genetic algorithm (DE-GA) is proposed, complemented by a solution refinement strategy. Performance evaluations indicate that DE-GA methodology consistently outperforms Gurobi solver and four other prevalent algorithms across different test instances.

Instance-Rotation-Based Surrogate in Genetic Programming With Brood Recombination for Dynamic Job-Shop Scheduling

Genetic programming has achieved great success for learning scheduling heuristics in dynamic job shop scheduling. In theory, generating a large number of offspring for genetic programming, known as brood recombination, can improve its heuristic generation ability. However, it is time-consuming to evaluate extra individuals. Phenotypic characterisation based surrogates with K-nearest neighbours have been successfully used for genetic programming to preselect only promising individuals for real fitness evaluations in dynamic job shop scheduling. However, sample individuals used by surrogate are from only the current generation, since the fitness of individuals across generations are not comparable due to the rotation of training instances. The surrogate cannot accurately estimate the fitness of an offspring that is far away from all the limited sample individuals at the current generation. This paper proposes an effective instance rotation based surrogate to address the above issue. Specifically, the surrogate uses the samples extracted from individuals across multiple generations with different instances. More importantly, we propose a fitness mapping strategy to make the fitness evaluated by different instances comparable. The results show that the GP with brood recombination and the proposed surrogate can significantly improve the quality of scheduling heuristics. The results also reveal that the proposed algorithm has successfully reduced the number of omitted promising offspring due to the higher accuracy of the surrogate. The samples in the new surrogate spread better in the phenotypic space, and the nearest neighbour tends to be closer to the predicted offspring. This makes the estimated fitness more accurate.