Large Job Research Articles

Some Properties of the Optimal Decomposition Conditions for the Single Machine Tardiness Problem

We consider the well-known optimal decomposition algorithm for the tardiness problem. The algorithm presents conditions which determine the positions a job could occupy in an optimal sequence. This resulted in optimal solutions for up to 100 jobs. We analyze these conditions and present simplified conditions. We then study a more recent Rule which when combined with these conditions resulted in optimal solutions for up to 500 jobs. We present several properties under which this recent rule is satisfied. We provide mathematical proofs for our properties. We believe that our study will enable more theoretical research in this field and will eventually enable optimal solutions for very large job sets.

Multi-robot multi-station welding flow shop closed-loop rescheduling with deep reinforcement learning and improved artificial bee colony algorithm

Multi-robot multi-station manufacturing system has gained significant popularity in welding workshop for processing large jobs. The implementation of cooperative welding robots effectively enhances the efficiency and flexibility of the system. However, robot failures also bring challenges for rescheduling to determine the rescheduling timing and re-assign appropriate robots for the affected processes so as to minimize the losses in performance and the maximum completion time. Distinct from open-loop rescheduling approach, this paper proposes a closed-loop rescheduling method via deep reinforcement learning and improved artificial bee colony algorithm. This method addresses two crucial questions: when to trigger a rescheduling, and how to provide a rescheduling scheme. For the first issue, current methods make decisions according to indicators derived from expert experience, failing to adapt to the dynamic environment. Therefore, we propose a deep reinforcement learning method to learn the adaptive trigger policy for the trigger agent under real-time running state. For the second issue, we present an improved artificial bee colony algorithm to re-assign robots without changing processing sequence and feed the performance back to trigger agent. Specially, a variable local search method is incorporated to avoid falling into local optimum. Finally, the proposed method is applied to solve the instances with different scales and a practical case. The experimental results of both demonstrate that the proposed closed-loop rescheduling method can greatly reduce the deviation of the total completion time and the maximum completion time and have superior performance over other algorithms.

Strongly Tail-Optimal Scheduling in the Light-Tailed M/G/1

We study the problem of scheduling jobs in a queueing system, specifically an M/G/1 with light-tailed job sizes, to asymptotically optimize the response time tail. This means scheduling to make \mathbfP [T > t], the chance a job's response time exceeds t, decay as quickly as possible in the t \to \infty limit. For some time, the best known policy was First-Come First-Served (FCFS), which has an asymptotically exponential tail: \mathbfP [T > t] \sim C e^-γ t . FCFS achieves the optimal *decay rate* γ, but its *tail constant* C is suboptimal. Only recently have policies that improve upon FCFS's tail constant been discovered. But it is unknown what the optimal tail constant is, let alone what policy might achieve it. In this paper, we derive a closed-form expression for the optimal tail constant C, and we introduce *γ-Boost*, a new policy that achieves this optimal tail constant. Roughly speaking, γ-Boost operates similarly to FCFS, but it pretends that small jobs arrive earlier than their true arrival times. This significantly reduces the response time of small jobs without unduly delaying large jobs, improving upon FCFS's tail constant by up to 50% with only moderate job size variability, with even larger improvements for higher variability. While these results are for systems with full job size information, we also introduce and analyze a version of γ-Boost that works in settings with partial job size information, showing it too achieves significant gains over FCFS. Finally, we show via simulation that γ-Boost has excellent practical performance.

Improving batch schedulers with node stealing for failed jobs

SummaryAfter a machine failure, batch schedulers typically re‐schedule the job that failed with a high priority. This is fair for the failed job but still requires that job to re‐enter the submission queue and to wait for enough resources to become available. The waiting time can be very long when the job is large and the platform highly loaded, as is the case with typical HPC platforms. We propose another strategy: when a job fails, if no platform node is available, we steal one node from another job , and use it to continue the execution of despite the failure. In this work, we give a detailed assessment of this node stealing strategy using traces from the Mira supercomputer at Argonne National Laboratory. The main conclusion is that node stealing improves the utilization of the platform and dramatically reduces the flow of large jobs, at the price of slightly increasing the flow of small jobs.

Analysis of Several Factors Affecting Labor Force Absorption in East Java Province for the Years 2017 - 2022

Evaluating the government's success in economic development is essential to assess how effectively the government can address unemployment and enhance job opportunities. To ensure that the workforce is optimally absorbed, it must be accompanied by the creation of new and abundant job opportunities. Labor issues are a common concern in developing countries like Indonesia, which has a large population. A significant population size leads to a substantial labor force, resulting in a high number of job seekers and unemployed individuals, which in turn leads to relatively low incomes. This research aims to analyze several factors influencing labor force absorption in East Java from 2017 to 2022. This research method utilizes secondary data, which consists of a 6-year panel data from 2017 to 2022. The analytical method employed in this research is multiple linear regression. The results of this research indicate that the minimum wage and economic growth do not have a significant impact on labor force absorption in East Java Province. On the other hand, the average years of schooling have a considerable influence on labor force absorption in East Java Province. This research serves as valuable input for determining the direction and future development strategies and can be used as a reference for predicting the labor force conditions in East Java. The imbalance between the large labor force and available job opportunities in East Java highlights the need for the government to focus more on labor force absorption in the province. These three factors can serve as a guide for the government in addressing labor force issues, aiming to optimize labor force absorption and consequently boost economic growth in East Java Province.

Employment and well-being after plant closure: Survey evidence from Switzerland on the mid and long run.

How does plant closure affect the employment and well-being of displaced workers? This article presents the results of two surveys of workers at five manufacturing plants two and 11 years after mass layoffs. After two years, two-thirds of displaced workers had been re-employed, one in five workers was still unemployed, and one in 10 workers had retired. A decade after the plant closures, unemployment had fallen below 5%. Overall, post-displacement outcomes in Switzerland were more favourable than in other European plant closures. However, age disparities loomed large. Older workers struggled to find new jobs and often had to accept large wage cuts and unstable jobs. In particular, many workers in their late forties and early fifties were hit hard as they were too young to benefit from early retirement, but too old to start over.

SEH: Size Estimate Hedging Scheduling of Queues

For a single server system, Shortest Remaining Processing Time (SRPT) is an optimal size-based policy. In this article, we discuss scheduling a single-server system when exact information about the jobs’ processing times is not available. When the SRPT policy uses estimated processing times, the underestimation of large jobs can significantly degrade performance. We propose an index-based policy with a single parameter, Size Estimate Hedging (SEH), that only uses estimated processing times for scheduling decisions. A job’s priority is increased dynamically according to an SRPT rule until it is determined that it is underestimated, at which time the priority is frozen. Numerical results suggest that SEH has desirable performance for estimation error variance that is consistent with what is seen in practice.

Local Second-Order Møller-Plesset Theory with a Single Threshold Using Orthogonal Virtual Orbitals: Theory, Implementation, and Assessment.

It has long been clear that electron correlation methods exhibit unphysical compute scalings with molecular size, which has motivated the development of local correlation methods to discard effectively zero contributions in a controlled way to yield an approximate correlation energy. The ideal local correlation method should have a single numerical threshold that controls the dropping of terms with the ability to have that threshold set small enough so that the correlation energy is reproduced to enough significant figures such that the result is chemically identical. This work reports such a method for the second-order Møller-Plesset (MP2) theory. The theory, implementation, and testing of this local MP2 theory are reported. Thresholds ranging from 10-5 to 10-8 and basis sets ranging from split valence plus polarization through to quadruple-ζ are assessed for local MP2 calculations on a range of molecules, including linear chains and molecules with two- and three-dimensional character. The implementation is shared memory parallel via OpenMP and yields roughly 50% parallel efficiency with 16 cores for a large job. Considerable efforts were made to minimize memory demands, which increased as thresholds were tightened. A variety of relative energy calculations are presented as a function of threshold to provide some guidance to users on how to obtain adequate precision at a low compute cost. It is particularly clear that derivative properties require tighter thresholds in order to achieve an adequate precision.

Online Incremental Learning Based on Crowdsourcing For Indonesian Ontology Relation Extraction

Ontology is one form of structured representation of knowledge. Ontology is widely used and developed in information retrieval because of its ability to represent knowledge in a form that machines and humans can understand. With the increasing scale and complexity of ontology, there are more significant challenges in identifying extra-logical errors. Ontological development methods mostly use machine learning, which is at risk of missed extra-logical errors. To handle it, crowdsourcing is used, i.e. dividing a large job into several small jobs and hiring the masses to complete it. Data processing is usually done offline to take advantage of crowdsourcing, and batches are converted into online and incremental. Online incremental learning directly arranges an iterative model after a change is made by ensuring that the knowledge that has been obtained before is maintained. This study built an interactive medium to present the initial relationship between concept pairs. Crowdsourcing participants were asked to validate the relationship repeatedly until a specified accuracy value was reached. This study found that the crowdsourcing process was able to improve the model used in the relationship extraction process, from F1-Score 87.2% to 89.8%. Improvements using crowdsourcing achieve the same result as improvements by experts. Thus, crowdsourcing can correct extra-logical errors appropriately as an expert. In addition, it was also found that offline incremental learning using Random Forest resulted in higher model accuracy than incremental online learning using Mondrian Forest. The accuracy of the Random Forest model has a final accuracy of 90.6%, while the accuracy of the Mondrian Forest model is 89.7%. From these results, it was concluded that incremental online learning cannot provide better results than offline incremental learning to improve the meronymy relationship extraction process.

Thermal Artificial Bee Colony Algorithm for Large Scale Job Shop Scheduling Problems

Thermal Artificial Bee Colony Algorithm for Large Scale Job Shop Scheduling Problems

RINX: A system for information and knowledge extraction from resumes

A resume is a detailed source of information about the candidate which summarizes the personal details, education, career history, project experience, certifications, trainings, awards, and any other achievements. For large organizations or job portals which receive thousands of resumes for recruitment or profile creation, it is not possible to manually go through each resume and identify the important information. Hence, there is a need for a system that automatically extracts the information of interest from the resumes. Such automatic extraction of information from resumes is very challenging because resumes are unstructured documents with a wide range of variations in terms of format, style, and contents. In this paper, we describe RINX (Resume INformation eXtraction) which is an end-to-end system for automatic extraction of information from resumes. RINX heavily utilizes traditional approaches like linguistic patterns and gazettes for information extraction. RINX also complements these traditional approaches with state-of-the-art machine learning and deep learning based techniques. We further describe a few knowledge extraction techniques as well as several real-life use-cases based on the information extracted from a large repository of resumes.

Keep Your Distributed Data Warehouse Consistent at a Minimal Cost

Large data warehouses store interdependent tables that are updated independently in response to business logic changes or late arrival of critical data. To keep the warehouse consistent, changes to upstream tables need to be propagated to downstream tables in a timely fashion. However, a naive change propagation algorithm can cause many unnecessary updates or recalculations of downstream tables, which drives up the cost of data warehouse management. In this paper, we describe our solution that can ensure the eventual consistency of the data warehouse while avoiding unnecessary table updates. We also show that the optimal trade-off between computational cost reduction and meeting data freshness constraints can be found by solving a dynamic programming problem. The proposed solution is currently in production to manage the YouTube Data Warehouse and has reduced update requests by 25% by eliminating non-trivial duplicates. These requests would have been carried out by large batch jobs over big data. Eliminating them has led to a proportionate reduction in computing resources. One key advantage of our approach is that it can be used in a heterogeneous, distributed data warehouse environment where the operator software may not have complete control over the query processors. This is because our approach only relies on having dependency information for tables and can operate on the post-state of data sources.

Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning

Production flow is becoming increasingly complex since manufacturers must react quickly to changing markets demands and diverse customer requirements. In order to ensure production efficiency, it is essential to have an adequate scheduling system capable of managing diverse process flows and handling unforseen changes. In this paper, we present an approach leveraging Digital Twins (DTs) and Deep-Q-Learning to perform integrated process planning and scheduling for service-based production. DTs of production assets provide live information about their physical entities for our approach to perform live decision-making based on the current operation conditions. We use Deep-Q-Learning which is a deep Reinforcement Learning (RL) algorithm to perform integrated process planning and scheduling. We present two RL-designs that deal with different situations of live decision-making. We have evaluated the learning efficiency and scalability of the RL-designs on a virtual aluminum cold rolling mill developed by the SMS Group,11https://www.sms-group.com/plants/cold-rolling-mills-for-aluminum. in the context of the BaSys 4.2 project.22https://www.eclipse.org/research/projects/basys_42/. The results show that the first RL-design is suitable for deriving schedules for individualized production with small lots where process plans must be re-calculated frequently, while the second RL-design is optimal for production with large job quantities where jobs arrive continuously.

Social norms and gendered occupational choices of men and women: Time to turn the tide?

AbstractWe analyze the relationship between social gender norms and adolescents' occupational choices by combining regional votes on constitutional amendments on gender equality with job application data from a large job board for apprenticeships. The results show that adolescent males in regions with stronger traditional social gender norms are more likely to apply for typically male occupations. This finding does not hold for females, suggesting that incentivizing men to break the norms and choose gender‐atypical occupations (e.g., in healthcare) can be even more effective in accelerating advancement toward gender equality in the labor market than incentivizing women to choose STEM occupations.

How attractive are internship advertisements to prospective applicants? A mixed-method examination through the lens of employer branding

PurposeThe purpose of this study was to investigate attractiveness of internship advertisements to the prospective applicants through the lens of employer branding. By giving attention to internship attractiveness, universities and companies expand current collaborations around internship provision to enhance student internship experiences, satisfaction and employment prospects and to foster long-term sustainability of internship programs.Design/methodology/approachThe study used a mixed-methods design employing a concurrent triangulation strategy. The authors conducted a content analysis of 94 internship listings published in the largest Russian job portal, HeadHunter, and collected surveys from 274 prospective interns.FindingsThe study shows a mismatch between employer branding strategies and prospective interns' perceptions of employer attractiveness. Companies emphasize economic value and visual identity as well as functional attributes, while prospective interns prioritize development value and symbolic attributes. The findings suggest internship advertisements do not appeal to potential applicants.Research limitations/implicationsThe results are limited to business students from specific universities in Russia, so further research is needed to include students with different majors and from other countries. The study is also limited to advertisements available via a large online job portal. A comparative analysis of effectiveness of various communication channels for internship promotion could yield useful results.Practical implicationsCareer center advisors, academic program directors and internship coordinators should work with company leaders, human resources and marketing decision-makers, and recruitment specialists to better understand interns' drivers of attraction and audit the internship advertisements. Employers could use the study results to strengthen recruiting messages and, hence, attractiveness of the employer brand to potential interns.Originality/valueThe study contributes by applying the employer branding theory to an understudied population of interns, hence providing new insights into internships as collaboration between universities and companies. By focusing on internship positions, which differ from full-time permanent positions across many aspects, including the positions' duration, pay, supervision and purpose, the study assists in understanding the distinct drivers of attraction of interns, which are missing from published research on the topic.

A Novel Resource Scheduler for Resource Allocation and Scheduling in Big Data Using Hybrid Optimization Algorithm at Cloud Environment

Big Medical Data (BMD) is generated by cellular telephones, clinics, academics, suppliers, and organizations. Collecting, finding, analyzing, and managing the big data to make people's lives better, comprehending novel illnesses, and treatments, predicting results at initial phases, and making real-time choices are the actual issues in healthcare systems. Dealing with big medical data in resource scheduling is a major issue that aims to offer higher quality healthcare services. Hadoop MapReduce has been widely used for parallel processing of large data tasks and efficient job scheduling. The number of big data tasks is constantly growing; it is becoming more essential to minimize their energy usage to reduce the environmental effect and operating expenses. Hence to overcome these disadvantages, we propose a novel resource scheduler for big data using a Hybrid 2-GW Optimization Algorithm (H2-GWOA). We employ the Improved GlowWorm Swarm Optimization Algorithm (IGSOA) and Mean GreyWolf Optimization Algorithm (MGWOA) for optimizing the MapReduce framework in heterogeneous big data. The CloudSim platform was used for the simulations. The performance of the proposed scheduler is proved to be better than the conventional methods in terms of metrics like latency, makespan, resource utilization, skewness, and Central Processing Unit (CPU) consumption.

On the Stochastic and Asymptotic Improvement of First-Come First-Served and Nudge Scheduling

Recently it was shown that, contrary to expectations, the First-Come-First-Served (FCFS) scheduling algorithm can be stochastically improved upon by a scheduling algorithm called Nudge for light-tailed job size distributions. Nudge partitions jobs into 4 types based on their size, say small, medium, large and huge jobs. Nudge operates identical to FCFS, except that whenever a small job arrives that finds a large job waiting at the back of the queue, Nudge swaps the small job with the large one unless the large job was already involved in an earlier swap. In this paper, we show that FCFS can be stochastically improved upon under far weaker conditions. We consider a system with 2 job types and limited swapping between type-1 and type-2 jobs, but where a type-1 job is not necessarily smaller than a type-2 job. More specifically, we introduce and study the Nudge- K scheduling algorithm which allows type-1 jobs to be swapped with up to K type-2 jobs waiting at the back of the queue, while type-2 jobs can be involved in at most one swap. We present an explicit expression for the response time distribution under Nudge- K when both job types follow a phase-type distribution. Regarding the asymptotic tail improvement ratio (ATIR), we derive a simple expression for the ATIR, as well as for the K that maximizes the ATIR. We show that the ATIR is positive and the optimal K tends to infinity in heavy traffic as long as the type-2 jobs are on average longer than the type-1 jobs.

Fairness constraint efficiency optimization for multiresource allocation in a cluster system serving internet of things

SummaryMassive, diverse, and high‐frequency Internet of Things (IoT) applications pose challenges to the operation of cluster systems that serve it. Fair and efficient multidimensional resource allocation is of great significance to the sustainable operation of these systems. However, most of the existing cluster multiresource allocation optimization researches focus too much on the fairness of resource allocation and ignore the efficiency. The unbalanced use of multidimensional system resources reduces the effective utilization of system resources, which seriously affects the service quality of IoT applications. In this paper, we define the multiresource fair and efficient sharing optimization as a fairness‐constrained efficiency optimization problem, which is from dynamics, discrete resources, and heterogeneous perspectives according to the characteristics of cluster system in practical. Moreover, we present a dynamic efficiency‐aware multiresource fair allocation algorithm, DEF, which can improve the ability of the cluster system to serve diverse IoT applications. In the algorithm, large jobs schedule to the servers that expect the least remaining resources. Simulations performed using Google cluster‐usage traces show that DEF can improve system resource utilization and guarantee the fairness of sharing among users.

Improving Urgency-Based Backlog Sequencing of Jobs: An Assessment by Simulation

When order release is applied, jobs are withheld in a backlog from where they are released to meet certain performance targets. The decision that selects jobs for release is typically preceded by a sequencing decision. It was traditionally assumed that backlog sequencing is only responsible for releasing jobs on time, whereas more recent literature has argued that it can also support load balancing. Although the new load-based rules outperform time-based rules, they can be criticized for requiring workload information from the shop floor and for delaying large jobs. While some jobs will inevitably be delayed during periods of high load, we argue that this delaying decision should be under control of management. A simulation study of a wafer fab environment shows that a time-based rule matches the performance of more complex load-based backlog sequencing rules that have recently emerged. The new rule realizes the lowest percentage of tardy jobs if the lower bound that distinguishes between early and urgent jobs is set appropriately. It provides a simpler means of improving release performance, allowing managers to delay jobs that have adjustable due dates.

Интернет как средство поиска работы и персонала

In order to determine the real level of interaction between applicants and employers using Internet resources within the framework of the electronic labor market, an analysis of the allocated communication channels in the labor market was carried out. It turned out that today most large companies and job seekers in the labor market resort to the use of Internet resources. Meanwhile, the study of modern realities proves that this tool is not fully disclosed in practice.
 The results of the study revealed the following: companies use the capabilities of their own sites in different ways; the use of modern specialized work sites is built into the strategy of filling vacancies not by all organizations, while the functionality of the sites themselves is limited; despite the high attendance of applicants for social networks, the mechanism of using the latter as a channel of interaction in the labor market is at the formation stage.