Job Processing Research Articles

An Algebraic Approach to the Solutions of the Open Shop Scheduling Problem

The open shop scheduling problem (OSSP) is one of the standard scheduling problems. It consists of scheduling jobs associated with a finite set of tasks developed by different machines. In this case, each machine processes at most one operation at a time, and the job processing order on the machines does not matter. The goal is to determine the completion times of the operations processed on the machines to minimize the largest job completion time, called Cmax. This paper proves that each OSSP has associated a path algebra called Brauer configuration algebra whose representation theory (particularly its dimension and the dimension of its center) can be given using the corresponding Cmax value. It has also been proved that the dimension of the centers of Brauer configuration algebras associated with OSSPs with minimal Cmax are congruent modulo the number of machines.

Effective league championship algorithm and lower bound procedure for scheduling a single batch-processing machine with non-identical job sizes and job rejection

We address the scheduling problem of a set of non-identical size jobs on a single batch-processing machine (SBPM) wherein the scheduler can make decision whether to schedule a job in batches or not to schedule it with a job-dependent penalty. The processing time of a batch is the greatest job processing time in that batch (parallel batching or p-batching). The scheduler wants to minimize a given objective function f, where f is the total rejection penalties of the rejected jobs (rejection cost) plus the makespan of the scheduled ones. We formulate the aforementioned problem as a 0–1 mixed integer programming model. We also apply an effective dynamic programming algorithm (DPA) to calculate a lower bound (LB) on the optimal cost of the problem. To tackle the problem, we propose a grouping algorithm, based on league championship algorithm (LCA), with new updating equations maintaining the major characteristics of the original updating equations of the LCA and well-suited to the structure of the problem. For small problems, performance of the proposed LCA is compared with GAMS/CPLEX solver. For large-scale instances, a genetic algorithm is adopted as a basis for comparison. Simulated experiments confirm the performance of the proposed methods.

Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Permutation flow-shop scheduling is the strategy that ensures the processing of jobs on each subsequent machine in the exact same order while optimizing an objective, which generally is the minimization of makespan. Because of its NP-Complete nature, a substantial portion of the literature has mainly focused on computational efficiency and the development of different AI-based hybrid techniques. Particle Swarm Optimization (PSO) has also been frequently used for this purpose in the recent past. Following the trend and to further explore the optimizing capabilities of PSO, first, a standard PSO was developed during this research, then the same PSO was hybridized with Variable Neighborhood Search (PSO-VNS) and later on with Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The effect of hybridization was validated through an internal comparison based on the results of 120 different instances devised by Taillard with variable problem sizes. Moreover, further comparison with other reported hybrid metaheuristics has proved that the hybrid PSO (HPSO) developed during this research performed exceedingly well. A smaller value of 0.48 of ARPD (Average Relative Performance Difference) for the algorithm is evidence of its robust nature and significantly improved performance in optimizing the makespan as compared to other algorithms.

A Two-Phase Iterative Mathematical Programming-Based Heuristic for a Flexible Job Shop Scheduling Problem with Transportation

In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry 4.0. In this article, a two-phase iterative mathematical programming-based heuristic is proposed to minimize makespan using a machine-operation assignment centric decomposition scheme. The first phase approximates the FJSPT through an augmented flexible job shop scheduling problem (FJSP + T) that reduces the solution space while serving as a heuristic in locating good machine-operation assignments. In the second phase, a job shop scheduling problem with transportation (JSPT) network is constructed from these assignments and solved for the makespan. Compared to prior JSPT implementations, the proposed JSPT model considers job pre-emption, which is instrumental in enabling this FJSPT implementation to outperform certain established benchmarks, confirming the importance of considering job pre-emption. Results indicate that the proposed approach is effective, robust, and competitive.

TEBAS: A Time-Efficient Balance-Aware Scheduling Strategy for Batch Processing Jobs

Common schedulers for long-term running services that perform task-level optimization fail to accommodate short-living batch processing (BP) jobs. Thus, many efficient job-level scheduling strategies are proposed for BP jobs. However, the existing scheduling strategies perform time-consuming objective optimization which yields non-negligible scheduling delay. Moreover, they tend to assign BP jobs in a centralized manner to reduce monetary cost and synchronization overhead, which can easily cause resource contention due to the task co-location. To address these problems, this paper proposes TEBAS, a time-efficient balance-aware scheduling strategy, which spreads all tasks of a BP job into the cluster according to the resource specifications of a single task based on the observation that computing tasks of a BP job commonly possess similar features. The experimental results show the effectiveness of TEBAS in terms of scheduling efficiency and load balancing performance.

Explore the Leadership of Principals and its Influence on Job Satisfaction of Employees in Private Colleges of Lahore Punjab

Leadership motivates subordinates. Leadership styles determine organizational success. Leadership influences job satisfaction, commitment, and productivity. This study examined private college principals' leadership and success strategies. Parents, teachers, and students expect brand-commitment. Instructional leadership by a strong and experienced private college leader or supervisor ensures quality. We asked faculty and subordinates about Supervisor leadership styles. This exploratory descriptive research study interviews and samples faculty to assess Principal leadership style and job satisfaction. Five private college principals and 10 departmental faculty are chosen. A Principal, Instructional Management Rating Scale (PIMRS) and Self-Assessment Instrument for Teacher's Evaluation (SATE) interview protocol (SITE). Final theme analysis used 15 valid cases. Teachers had natural semi-structured interviews. Input, process, outcome, and teacher job satisfaction emerged. Principals' instruction made teachers happier. Principals should improve teacher job satisfaction through instructional leadership. Private colleges should improve teacher satisfaction with instructional leadership.

Study on Due-Date Assignment Scheduling with Setup Times and General Truncated Learning Effects

This paper concentrates on the single-machine scheduling problem with past-sequence-dependent setup times and general truncated learning effects, where the job processing times are non-increasing function of their positions in a sequence. Under common, slack and different (unrestricted) due-date assignments, our goal is to minimize the weighted sum of number of early/tardy jobs and due-date assignment cost, where the weight is not related to the job but to a position, i.e., the position-dependent weight. Under the three due-date assignments, some optimal properties and three optimal solution algorithms are proposed to solve these problems, respectively.

Hybridizing genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources

<span lang="EN-US">This paper focuses on solving unrelated parallel machine scheduling with resource constraints (UPMR). There are </span><em><span lang="EN-US">j</span></em><span lang="EN-US"> jobs, and each job needs to be processed on one of the machines aim at minimizing the </span><em><span lang="EN-US">makespan</span></em><span lang="EN-US">. Besides the dependence of the machine, the processing time of any job depends on the usage of a rare renewable resource. A certain number of those resources R</span><em><span lang="EN-US">max</span></em><span lang="EN-US"> can be disseminated to jobs for the purpose of processing them at any time, and each job j needs units of resources (</span><em><span lang="EN-US">rjm</span></em><span lang="EN-US">) when processing in machine m. When more resources are assigned to a job, the job processing time minimizes. However, the number of resources available is limited, and this makes the problem difficult to solve for a good quality solution. Genetic algorithm shows promising results in solving UPMR. However, genetic algorithm suffers from premature convergence, which could hinder the resulting quality. Therefore, the work hybridizes genetic algorithm with a single-based metaheuristics (SBH) to handle the premature convergence in the genetic algorithm with the aim to escape from the local optima and improve the solution quality further. The single-based metaheuristics replaces the mutation in the genetic algorithm. The evaluation of the algorithm performance was conducted through extensive experiments. </span>

Accelerating Industrial IoT Acoustic Data Separation With In-Network Computing

Acoustic data from the Industrial Internet of Things (IIoT) are widely used in anomaly detection because audio information reflects richer internal statuses of monitored working machines than the video does. Since multiple acoustic data sources interfere with each other by nature, source data estimation is a prerequisite of subsequent anomaly detection. Existing schemes often use a centralized manner to separate full data on a remote node in clouds. However, such a centralized manner may delay reactions to anomalies due to data transmission delay and the complexity of solving data separation problems. This article shows that the data separation phase can be substantially accelerated with an in-network computing approach. The key idea is to offload data processing jobs to intermediate network nodes along the forwarding path. We first propose a distributed algorithm so that the data separation jobs can be done in a progressive manner; likewise, we modify the forwarding layer in order to eliminate hop-by-hop data transmission delay that hurts the performance of using in-network computing. We further derive theoretical upper and lower bounds of the required number of intermediate nodes that achieve the maximum acceleration. We also implement our proposed solution in a full-stack network emulator. Based on an open and professional data set, evaluation results justify the feasibility and advantages of our idea with nearly 32.18% acceleration on total processing time. This work exemplifies the convergence of IIoT, edge, and clouds.

Optimizing Decision Making on Business Processes Using a Combination of Process Mining, Job Shop, and Multivariate Resource Clustering

The current business environment has no room for inefficiency as it can cause companies to lose out to their competitors, to lose customer trust, and to experience cost overruns. Business processes within the company continue to grow and cause them to run more complex. The large scale and complexity of business processes pose a challenge in improving the quality of process model because the effectiveness of time and the efficiency of existing resources are the biggest challenges. In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. Meanwhile, in the event log data, not only the activities but also the other resources, such as records of employee or staff working time, process service time, and processing costs, are recorded. This article proposes a mechanism alternative to optimize business processes by exploring the resources that occur in the process. The mechanism is carried out in three stages. The first stage is optimizing the job shop scheduling method from the generated event log. Scheduling the time becomes a problem in the job shop. Utilizing the right time can increase the effectiveness of performance in order to reduce costs. Scheduling can be defined as the allocation of multiple jobs in a series of machines, in which each machine only does one job at a time. In general, scheduling becomes a problem when sequencing the operations and allocating them into specific time slots without prolonging the technical and capacity constraints. The second stage is generating the resource value that is recorded in the event log from the results of analysis of the previous stage, namely, job shop scheduling. The resource values are multivariate and then clustered to determine homogeneous clusters. The last stage is optimizing the nonlinear multipolynomials in the homogeneous cluster formed by using the Hessian solution. The results obtained are analyzed to get recommendations on business processes that are appropriate for the company’s needs. The impact of long waiting times will increase service costs, but by improving workload, costs can be reduced. The process model and the value of service costs resulting from the mechanism in the research can be a reference for process owners in evaluating and improving ongoing processes.

Multi-Job Intelligent Scheduling With Cross-Device Federated Learning

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this article, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).

Online Approximation Scheme for Scheduling Heterogeneous Utility Jobs in Edge Computing

Edge computing systems typically handle a wide variety of applications that exhibit diverse degrees of sensitivity to job latency. Therefore, a multitude of utility functions of the job response time need to be considered by the underlying job dispatching and scheduling mechanism. Nonetheless, previous studies in edge computing mainly focused on optimizing a single utility function across all jobs, e.g., linear, sigmoid, or the hard deadline. In this paper, we design online job dispatching and scheduling strategies in which different jobs can be categorized by different non-increasing utility functions. Our goal is to maximize the total utility of all scheduled jobs. We first prove that no online deterministic algorithm could achieve a competitive ratio better than the lower bound <inline-formula> <tex-math notation="LaTeX">$\Omega(\frac{1}{\sqrt{\epsilon}})$</tex-math> </inline-formula> under the <inline-formula> <tex-math notation="LaTeX">$(1+\epsilon)$</tex-math> </inline-formula>-speed augmentation model. We proceed to propose an online algorithm, named as, for handling jobs with heterogeneous utilities. We prove that is <inline-formula> <tex-math notation="LaTeX">$O(\frac{1}{\epsilon^2})$</tex-math> </inline-formula>-competitive. We also design its distributed version, i.e., . We implement and on an edge computing testbed running deep learning inference jobs. With the production trace from Google Cluster, our experimental and large-scale simulation results indicate that can increase the total utility by up to <inline-formula> <tex-math notation="LaTeX">$50\%$</tex-math> </inline-formula> compared with state-of-the-art methods. Besides, the performance loss of is only <inline-formula> <tex-math notation="LaTeX">$2\%$</tex-math> </inline-formula> compared with with a small communication overhead involved. Moreover, both of our algorithms are robust to estimation errors in job processing time and transmission delay.

A Makespan-Optimal Schedule for Processing Jobs with Possible Operation Preemptions As an Optimal Mixed Graph Coloring

A Makespan-Optimal Schedule for Processing Jobs with Possible Operation Preemptions As an Optimal Mixed Graph Coloring

A novel risk assessment model for work-related musculoskeletal disorders in tea harvesting workers

Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problems in agriculture workers due to repetitive and excessive force movement activities involved in their job processes. The Fine-Kinney method has been commonly used as a quantitative evaluation method in risk assessment studies. Classically, the risk value via Fine–Kinney is calculated by the mathematical multiplication irrespective of the degree of importance of each risk parameter (probability (P), exposure (E), and consequence (C)). Hence, a novel risk management model was proposed based on integrating Fine-Kinney and spherical fuzzy AHP-TOPSIS. First, each risk parameter is weighted using the spherical fuzzy AHP (SF-AHP). Second, the spherical fuzzy TOPSIS (SF-TOPSIS) method is used for hazard ranking. The proposed model is applied to evaluate risks in tea harvesting workers for work-related musculoskeletal disorders. Subsequently, a sensitivity analysis is carried out to test the proposed model. Finally, we compare the proposed model’s applicability and effectiveness with the spherical fuzzy COmbinative Distance-based ASsessment (SF-CODAS) method based on Fine-Kinney. The ranking similarity between the proposed Fine-Kinney-based SF-TOPSIS and SF-CODAS methods is checked by applying Spearman’s rank correlation coefficient, in which 92% of rankings are matched.

A Novel Method of Digital Twin-Based Manufacturing Process State Modeling and Incremental Anomaly Detection

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly.

Single-machine scheduling with autonomous and induced learning to minimize total weighted number of tardy jobs

As commonly observed in practice, firms can accelerate job processing by adding additional resources to the manufacturing system while striving to sustain on-time delivery. In this paper we consider due date assignment and scheduling with a learning effect that can be invoked by proactive investment at the beginning, known as induced learning in the literature. The objective is to find the optimal decisions on due date assignment, job sequencing, and level of induced learning to minimize a cost function consisting of the total weighted number of tardy jobs, due date penalty, and investment cost. We find a novel property of the optimal solution that although the due date penalty may be larger, the number of on-time jobs will not decrease with the increasing induced learning effect, which is a breakthrough to solve the problem. Exploiting the property, we present a polynomial-time algorithm that generates an approximation solution with a gap less than Lϵ compared with the optimal result, where ϵ can tend to be infinitesimal, and L is a constant.

Segmentation and Analysis Emphasizing Neonatal MRI Brain Images Using Machine Learning Techniques

MRI scanning has shown significant growth in the detection of brain tumors in the recent decade among various methods such as MRA, X-ray, CT, PET, SPECT, etc. Brain tumor identification requires high exactness because a minor error can be life-threatening. Brain tumor disclosure remains a challenging job in medical image processing. This paper targets to explicate a method that is more precise and accurate in brain tumor detection and focuses on tumors in neonatal brains. The infant brain varies from the adult brain in some aspects, and proper preprocessing technique proves to be fruitful to avoid miscues in results. This paper is divided into two parts: In the first half, preprocessing was accomplished using HE, CLAHE, and BPDFHE enhancement techniques. An analysis is the sequel to the above methods to check for the best method based on performance metrics, i.e., MSE, PSNR, RMSE, and AMBE. The second half deals with the segmentation process. We propose a novel ARKFCM to use for segmentation. Finally, the trends in the performance metrics (dice similarity and Jaccard similarity) as well as the segmentation results are discussed in comparison with the conventional FCM method.

A Unified Approach to Single-Machine Scheduling with Position-Based Processing Times, Machine Availability, and Job Rejection

We present a unified approach to single-machine scheduling with position-based processing times, an availability constraint, and job rejection. The approach uses two general position-based processing time functions to model both the learning and aging effects in scheduling. In addition, taking machine availability and job rejection into consideration, the models are more realistic, which seek to minimize the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs. We present fully polynomial-time approximation schemes to address the two NP-hard problems.

DataCurator.jl: efficient, portable and reproducible validation, curation and transformation of large heterogeneous datasets using human-readable recipes compiled into machine-verifiable templates.

Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator, a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable, machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine-verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl.

Productivity Improvement in Pathology Laboratories Using Motion and Time Study Techniques.

This study aimed to determine the standard time required for the performed job and to examine the standard job critically for productivity improvement in the pathology laboratory. In this study that was conducted at a tertiary teaching hospital, observation, fishbone diagram, and flow charts were used to collect the information about the job process. All employees were observed from September 2017 to June 2018. The observations were recorded by video camera in order to overcome the Hawthorne effect. Nine basic procedure steps were followed for productivity improvement. Within the scope of the study, the jobs of `trimming tissue blocks` and `sectioning of tissue blocks` was selected. The standard time required was 0.19 minutes for `trimming tissue blocks` and 0.34 minutes for `sectioning of tissue blocks`. The procedure steps, named `Removal of tissue block` and `Fixing the block to the device`, were removed from the flow chart to define the improved method. The implementation of the improved method brought in a gain of 11.28 work days per year. It is obvious that the pathology laboratory needs to take certain measures to improve working conditions and increase efficiency. Our results demonstrate applying the study techniques could reduce the workload and processing time. This study also shows that the study techniques can be applied in the hospital laboratory. Incorporation of all pathology technicians in the change or innovation process will be important in maintaining the achievements.