Job Wait Research Articles

Decision Support System in Determining the Job Waiting Period for Graduates of Unimed Using the Mamdani Method of Fuzzy Logic

During the job search period as an educated unemployed person, it takes time to be accepted into the world of work. The time required to look for a job is a matter of uncertainty. So, we need a system that can be used as a reference in determining the job waiting period for graduates. The graduate job waiting period is when someone has not found a job after graduation. Several factors influence this. In this research, the factors used as job waiting period variables are GPA, length of study and achievement. Every graduate certainly has a different job waiting period. This research aims to determine the job waiting period for Graduates of Unimed by designing a decision support system using the Fuzzy Mamdani method. This research uses quantitative methods. Data was obtained by distributing questionnaires to Graduates of Unimed. It is hoped that the decision support system for determining the job waiting period can encourage students, especially Universitas Negeri Medan students, to improve their quality before entering the job. With the help of Matlab software, a decision support system using the Mamdani method can be used to determine the waiting period, where the result is that the job waiting period for Graduates of Unimed is fast in the 0-3 month category.

Graduate income and profession linkage: Tracer study of public relations graduates

Background: Higher educational institutions must prepare graduates for careers by monitoring their performance. They must also evaluate academic relevancy to practical needs. The PR program at Universitas Padjadjaran strives to deliver the best education and monitor graduates’ performance. Purpose: This paper aims to study how graduates’ first income is associated with GPA, study duration, and job wait time. This study also linked public relations education with graduates’ current position in their work based on their self-assessment. Methods: Using tracer study data from the Career and Development Centre of Universitas Padjadjaran, exploratory data analysis was done to examine the characteristics of graduates based on the observed variables. The bivariate analysis investigated the association between income, GPA, study duration, and job wait time. Conclusion: Findings of this study show no statistical evidence of the relationship between first salary, GPA, and duration of the study. Exploratory data analysis shows that PR program graduates perform well when comparing the average income of undergraduates on a national level. Graduates’ self-assessment of study relevancy indicated that most graduates possess the relevant educational background and appropriate level of education for their current occupational position. As the statistical testing for this study was conducted post hoc, careful considerations need to be taken when taking the results at face value, considering potential flaws in the sampling frame and the power of the tests. Implications: The findings in this study would serve as a platform for future references on graduates or tracer study, more specifically public relations graduates, as it provides a starting point for measuring graduates’ performances. As the database provides more data with more graduates in future years, an updated finding of this particular study would be very important to measure how well graduates fare post-graduation.

SİMULASYON DESTEKLİ TESİS TASARIMI: BİR İMALAT SİSTEMİNDE UYGULANMASI

System simulation is a very useful method when it is not possible to make changes on the reel system or it is very costly. Simulation has been applied by researchers in many fields and successful results have been obtained. In this study, the simulation method will be used to solve the problems arising from the facility layout in a manufacturing enterprise.Lack of a systematic layout study in a manufacturing enterprise; it makes it inevitable to encounter situations such as machine and job waiting, overloading of machinery and workers in the workflow processes of the enterprise. Insufficient layout causes unnecessary transportation costs between departments/machines. If business facility layout is systematically arranged; with the reduction of transportation costs, production costs can be reduced, and the competitiveness of the company can be increased.In this study, it is aimed to eliminate the cost increases caused by settlement in a manufacturing enterprise. The production system is first examined by examining the facility layout; systematic suggestions for improvement will be made. Due to the difficulty of implementing it on the real system; the comparison of the current situation and the proposed situation can be made by using simulation method. Also, through the simulation of the existing system, resources that create a bottleneck in the system are determined. Suggestions will be made for resource planning with different scenarios.

Allocating Small Transporters to Large Jobs

We optimize the assignment of transporters to several jobs. Each job consists of processing a large, decomposable volume. A fleet of transporters is given, each of which can only process a limited volume at a time. After processing its share, a transporter must rest for a short time before being able to process another part. This time is only dependent on the assigned job, not on the transporter. Other transporters can take over the processing while a transporter rests. Transporters assigned to the same job wait for their turn in a queue. A transporter can only be assigned to one job. Our goal is to simultaneously minimize the maximum job completion time and the number of assigned transporters by computing the frontier of Pareto optimal solutions. In general, we show that it is NP-hard in the strong sense to compute even a single point on the Pareto frontier. We provide exact methods and heuristics to compute the Pareto frontier for the general problem and compare them computationally.

Horus: Interference-Aware and Prediction-Based Scheduling in Deep Learning Systems

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this article we propose Horus: an interference-aware and prediction-based resource manager for DL systems. Horus proactively predicts GPU utilization of heterogeneous DL jobs extrapolated from the DL model's computation graph features, removing the need for online profiling and isolated reserved GPUs. Through micro-benchmarks and job co-location combinations across heterogeneous GPU hardware, we identify GPU utilization as a general proxy metric to determine good placement decisions, in contrast to current approaches which reserve isolated GPUs to perform online profiling and directly measure GPU utilization for each unique submitted job. Our approach promotes high resource utilization and makespan reduction; via real-world experimentation and large-scale trace driven simulation, we demonstrate that Horus outperforms other DL resource managers by up to 61.5 percent for GPU resource utilization, 23.7-30.7 percent for makespan reduction and 68.3 percent in job wait time reduction.

ROA-CONS: Raccoon Optimization for Job Scheduling

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

Appointment scheduling with a quantile objective

Appointment scheduling has many applications (e.g., surgery scheduling, airport gate scheduling, container vessel dockings and radiation therapy bookings) and it has a direct and significant operational and economic impact. For example, in healthcare, surgical departments are one of the main drivers of hospital costs and revenue, and appointment scheduling is used to book surgeries. Effective scheduling not only enables patients’ timely access to care but also enables more efficient operations. This becomes especially important as healthcare costs and demand are on the rise in many countries. We study appointment scheduling where there are jobs (e.g., patients, container vessels, airplanes) with random processing durations, an expensive processor (e.g., a doctor, dock crane, airport gate) and significant costs for processor idle time, processor overtime, and job waiting. The goal is to determine an appointment schedule that minimizes a measure of total costs as the objective. The appointment scheduling problem has been well studied in the literature with the expected cost objective. Almost all papers in the literature on appointment scheduling use the expected cost criterion, which may not be suitable when risk measures and/or service levels are considered. In this paper, we study this problem with a new objective: minimization of any quantile of the cost distribution, e.g., median, 90th percentile. We obtain theoretical results for some special cases and develop an algorithm for the general case. Our algorithm does not require a specific distribution assumption and can work directly with data samples. We present numerical examples with real data on surgeries. Our results show that allocated schedules based on the quantile objective with identical jobs are different than the ones generated by the expected cost objective and they do not show the well-known dome-shaped pattern but a semi-dome-shaped pattern which first increases (like the dome-shaped pattern) but then its decrease is not monotone (unlike the dome-shaped pattern). To the best of our knowledge, this is the first paper on appointment scheduling problem with the objective of the quantile function minimization.

Minimizing Makespan in A Two-Machine Flowshop Problem with Processing Time Linearly Dependent on Job Waiting Time

This paper is aimed at studying a two-machine flowshop scheduling where the processing times are linearly dependent on the waiting times of the jobs prior to processing on the second machine. That is, when a job is processed completely on the first machine, a certain delay time is required before its processing on the second machine. If we would like to reduce the actual waiting time, the processing time of the job on the second machine increases. The objective is to minimize the makespan. When the processing time is reduced, it implies that the consumption of energy is reduced. It is beneficial to environmental sustainability. We show that the proposed problem is NP-hard in the strong sense. A 0-1 mixed integer programming and a heuristic algorithm with computational experiment are proposed. Some cases solved in polynomial time are also provided.

Redundancy-d: The Power of d Choices for Redundancy

Redundancy is an important strategy for reducing response time in multi-server distributed queueing systems. This strategy has been used in a variety of settings, but only recently have researchers begun analytical studies. The idea behind redundancy is that customers can greatly reduce response time by waiting in multiple queues at the same time, thereby experiencing the minimum time across queues. Redundancy has been shown to produce significant response time improvements in applications ranging from organ transplant waitlists to Google’s BigTable service. However, despite the growing body of theoretical and empirical work on the benefits of redundancy, there is little work addressing the questions of how many copies one needs to make to achieve a response time benefit, and the magnitude of the potential gains. In this paper we propose a theoretical model and dispatching policy to evaluate these questions. Our system consists of k servers, each with its own queue. We introduce the Redundancy-d policy, under which each incoming job makes copies at a constant number of servers, d, chosen at random. Under the assumption that a job’s service times are exponential and independent across servers, we derive the first exact expressions for mean response time in Redundancy-d systems with any finite number of servers, as well as expressions for the distribution of response time which are exact as the number of servers approaches infinity. Using our analysis, we show that mean response time decreases as d increases, and that the biggest marginal response time improvement comes from having each job wait in only d = 2 queues. The e-companion is available at https://doi.org/10.1287/opre.2016.1582 .

Building Semi-Elastic Virtual Clusters for Cost-Effective HPC Cloud Resource Provisioning

Recent studies have found cloud environments increasingly appealing for executing HPC applications, including tightly coupled parallel simulations. At the same time, while public clouds offer elastic, on-demand resource provisioning and pay-as-you-go pricing, individual users setting up their on-demand virtual clusters may not be able to take full advantage of common cost-saving opportunities, such as reserved instances. In this paper, we propose a Semi-Elastic Cluster (SEC) computing model for organizations to reserve and dynamically resize a virtual cloud-based cluster. We present a set of integrated batch scheduling plus resource scaling strategies uniquely enabled by SEC, as well as an online reserved instance provisioning algorithm based on job history. Our trace-driven simulation results show that such a model has a 61.0 percent cost saving than individual users acquiring and managing cloud resources without causing longer average job wait time. Moreover, to exploit the advantages of different public clouds, we also extend SEC to a multi-cloud environment, where SEC can get a lower cost than on any single cloud. We design and implement a prototype system of the SEC model and evaluate it in terms of management overhead and average job wait time. Experimental results show that the management overhead is negligible with respect to the job wait time.

Integration of PanDA workload management system with Titan supercomputer at OLCF

The PanDA (Production and Distributed Analysis) workload management system (WMS) was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. While PanDA currently distributes jobs to more than 100,000 cores at well over 100 Grid sites, the future LHC data taking runs will require more resources than Grid computing can possibly provide. To alleviate these challenges, ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers.We will describe a project aimed at integration of PanDA WMS with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). The current approach utilizes a modified PanDA pilot framework for job submission to Titan's batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on Titan's multicore worker nodes. It also gives PanDA new capability to collect, in real time, information about unused worker nodes on Titan, which allows precise definition of the size and duration of jobs submitted to Titan according to available free resources. This capability significantly reduces PanDA job wait time while improving Titan's utilization efficiency. This implementation was tested with a variety of Monte-Carlo workloads on Titan and is being tested on several other supercomputing platforms.Notice: This manuscript has been authored, by employees of Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

CONWIP: closed or semi-open queuing network?

Closed queuing networks (CQNs) are generally used to model CONWIP systems. An implicit assumption in the CQN model is that whenever the work-in-process (WIP) level goes below the set value, there is always a job waiting for the released kanban. In other words, there are an infinite number of jobs in the external queue and as a result the kanban never waits in the resource pool. Modelling the CONWIP under this unrealistic assumption tends to underestimate the true cycle time of the kanban (and the job). In this technical note, we represent a CONWIP as a semi-open queuing network (SOQN) in which the job waits in the external queue until a kanban is available or vice-versa. Using published data and numerical experiments, we point out a modelling flaw of CONWIP systems in the literature and also show that the SOQN approach is a more appropriate model for representing CONWIP systems.

A Note on Appointment Scheduling with Piecewise Linear Cost Functions

We consider the problem of determining the optimal schedules for a given sequence of jobs on a single processor. The objective is to minimize the expected total cost incurred by job waiting and processor idling, where the job processing times are random variables. It is known in the prior literature that if the processing times are integers and the costs are linear functions satisfying a mild condition, then the problem can be solved in a polynomial number of expected cost evaluations. In this work, we extend the result to piecewise linear cost functions, which include many useful objective functions in practice. Our analysis explores the (hidden) dual network flow structure of the appointment scheduling problem and thus greatly simplifies that of prior work. We also find the number of samples needed to compute a near optimal solution when only some independent samples of processing times are known.

Matrix-geometric solution for semi-open queuing network model of autonomous vehicle storage and retrieval system

In this paper, we model the autonomous vehicle storage and retrieval system (AVS/RS) as a semi-open queuing network (SOQN) and apply a matrix-geometric method (MGM) for analyzing it. An AVS/RS is an automated material handling system for the high-rise pallet storage area of a warehouse and allows pallets to be stored and retrieved quickly and efficiently from their storage locations. It is an alternative to the traditional crane-based AS/RS (automated storage and retrieval system). A combination of lifts and autonomous vehicles store pallets into and retrieve them out of their respective rack storage locations. The crane based AS/RS typically utilizes aisle-captive, mast-mounted cranes that can access any storage location in an aisle via horizontal movement of the mast and vertical movement of the crane on the mast.In an SOQN, it is assumed that an arriving job or customer is paired with another device and the two visit all the stations that must process the job in the appropriate sequence. After all operations are completed on the job, it exits the system, but the device returns back to a device pool and awaits the next customer. Sometimes a job may have to wait for a device to arrive at the pool or a device may have to wait for a job to arrive. Although closed queuing networks (CQNs) and open queuing networks (OQNs) model systems that require pairing of an incoming job with a device, unlike the SOQN, they ignore the time that a device waits for a job or the time that a job waits for a device.In the context of an AVS/RS, the jobs correspond to storage/retrieval (S/R) transaction requests and the autonomous vehicles (AVs) correspond to the devices. Because an AV may sometimes have to wait for an S/R transaction or vice versa, we model the AVS/RS as an SOQN. We build the queuing network by deriving general travel times of pre-defined servers. We model the AVS/RS system as a single-class, multiple-server, SOQN. Then, we solve the network using the MGM and obtain its key performance measures. We apply the MGM technique for solving the SOQN model to a warehouse in France that uses AVS/RS.

A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process

One of the largest bottlenecks in iron and steel production is the steelmaking-continuous casting (SCC) process, which consists of steel-making, refining and continuous casting. The SCC scheduling is a complex hybrid flowshop (HFS) scheduling problem with the following features: job grouping and precedence constraints, no idle time within the same group of jobs and setup time constraints on the casters. This paper first models the scheduling problem as a mixed-integer programming (MIP) problem with the objective of minimizing the total weighted earliness/tardiness penalties and job waiting. Next, a Lagrangian relaxation (LR) approach relaxing the machine capacity constraints is presented to solve the MIP problem, which decomposes the relaxed problem into two tractable subproblems by separating the continuous variables from the integer ones. Additionally, two methods, i.e., the boundedness detection method and time horizon method, are explored to handle the unboundedness of the decomposed subproblems in iterations. Furthermore, an improved subgradient level algorithm with global convergence is developed to solve the Lagrangian dual (LD) problem. The computational results and comparisons demonstrate that the proposed LR approach outperforms the conventional LR approaches in terms of solution quality, with a significantly shorter running time being observed.

Heavy-Traffic Limits for a Many-Server Queueing Network with Switchover

We study a multiclass Markovian queueing network with switchover across a set of many-server stations. New arrivals to each station follow a nonstationary Poisson process. Each job waiting in queue may, after some exponentially distributed patience time, switch over to another station or leave the network following a probabilistic and state-dependent mechanism. We analyze the performance of such networks under the many-server heavy-traffic limiting regimes, including the critically loaded quality-and-efficiency-driven (QED) regime, and the overloaded efficiency-driven (ED) regime. We also study the limits corresponding to mixing the underloaded quality-driven (QD) regime with the QED and ED regimes. We establish fluid and diffusion limits of the queue-length processes in all regimes. The fluid limits are characterized by ordinary differential equations. The diffusion limits are characterized by stochastic differential equations, with a piecewise-linear drift term and a constant (QED) or time-varying (ED) covariance matrix. We investigate the load balancing effect of switchover in the mixed regimes, demonstrating the migration of workload from overloaded stations to underloaded stations and quantifying the load balancing impact of switchover probabilities.

Heavy-Traffic Limits for a Many-Server Queueing Network with Switchover

We study a multiclass Markovian queueing network with switchover across a set of many-server stations. New arrivals to each station follow a nonstationary Poisson process. Each job waiting in queue may, after some exponentially distributed patience time, switch over to another station or leave the network following a probabilistic and state-dependent mechanism. We analyze the performance of such networks under the many-server heavy-traffic limiting regimes, including the critically loaded quality-and-efficiency-driven (QED) regime, and the overloaded efficiency-driven (ED) regime. We also study the limits corresponding to mixing the underloaded quality-driven (QD) regime with the QED and ED regimes. We establish fluid and diffusion limits of the queue-length processes in all regimes. The fluid limits are characterized by ordinary differential equations. The diffusion limits are characterized by stochastic differential equations, with a piecewise-linear drift term and a constant (QED) or time-varying (ED) covariance matrix. We investigate the load balancing effect of switchover in the mixed regimes, demonstrating the migration of workload from overloaded stations to underloaded stations and quantifying the load balancing impact of switchover probabilities.

Improvement of WMS Functionality, Aiming to Minimize Processing Time of Jobs in Grid Computing

WMS (Workload Management System) algorithm of functionality improvement, based on quality of a cluster and used inperforming large scale computing in GRID network, is presented. Limitations for a minimal number of cluster nodes, a minimal numberof central processors in each node, the largest number of jobs queued and the longest acceptable average time for job execution aredefined in distributed jobs. The total time of time, when a job waits in queue, and time, when it is executed, is evaluated. Validity of thejob certificate and a list of clusters are evaluated additionally. A simulator that evaluates the mentioned factors was created forresearching WMS characteristics. Ill. 5, bibl. 9, tabl. 3 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.113.7.624

Rolling Horizon Approach for Dynamic Parallel Machine Scheduling Problem with Release Times

In this paper, we study a dynamic parallel machine scheduling problem with release times, where the release times and processing times of jobs may change during the production process due to uncertainties. The problem is different from classical scheduling problems in the deterministic environment where all information of jobs is known at the beginning of the scheduling horizon and will not change during the operations throughout the whole horizon. In practice, there are often unpredictable events causing dynamic changes in job release times and/or processing times. Traditional optimization methods cannot solve the dynamic scheduling problem directly even though they have been successful in solving the static version of the problem. A model predictive control (MPC) strategy based rolling horizon approach is applied to tackle the dynamic parallel machine scheduling problem with the objective of minimizing the total weighted completion times of jobs, the energy consumption due to job waiting, and the total ...

Minimization of Job Waiting Time Variance on Identical Parallel Machines

We consider the problem of scheduling independent jobs on identical parallel machines so as to minimize the waiting time variance of the jobs ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ). We show that the optimal value of( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ) is identical to the optimal value of the problem for minimizing the completion time variance of jobs on identical parallel machines( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ). We prove that, given the same job set, any feasible schedule of ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ) can be transformed into the feasible solution for ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ) with the same job set by applying the polynomial algorithms proposed in this paper. Several other important properties of ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ) are also proved. Heuristic algorithms are proposed to solve ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pm</i> // <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WTV</i> ) problems. We present the testing results of these heuristic algorithms, which are applied to problems with both small and large job sets.