Job Completion Research Articles

A Deep Reinforcement Learning Method Based on a Transformer Model for the Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization problem widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal solution for all scenarios within a reasonable time frame faces serious challenges. This paper proposes a solution that transforms the FJSSP into a Markov Decision Process (MDP) and employs deep reinforcement learning (DRL) techniques for resolution. First, we represent the state features of the scheduling environment using seven feature vectors and utilize a transformer encoder as a feature extraction module to effectively capture the relationships between state features and enhance representation capability. Second, based on the features of the jobs and machines, we design 16 composite dispatching rules from multiple dimensions, including the job completion rate, processing time, waiting time, and manufacturing resource utilization, to achieve flexible and efficient scheduling decisions. Furthermore, we project an intuitive and dense reward function with the objective of minimizing the total idle time of machines. Finally, to verify the performance and feasibility of the algorithm, we evaluate the proposed policy model on the Brandimarte, Hurink, and Dauzere datasets. Our experimental results demonstrate that the proposed framework consistently outperforms traditional dispatching rules, surpasses metaheuristic methods on larger-scale instances, and exceeds the performance of existing DRL-based scheduling methods across most datasets.

RISK AND OPPORTUNITY MANAGEMENT IN MEP PROJECT-BASED COMPANIES AND ITS IMPACT ON BUSINESS PERFORMANCE

The board is crucial for MEP project-based organizations to progress. Gamble the board prevents cost overruns and project delays, while opportunity the executives capitalize on excellent opportunities. Chance administration in Pakistan's growth is the study's main focus, and Chance mechanical, electrical, and plumbing (MEP) management in project board in Pakistani development is reviewed. The evaluation included several hundred development (MEP) projects from Karachi, Lahore, Faisalabad, Rawalpindi, Islamabad Pakistan's five largest cities with active development and large investments. Surveys are sent to company managers via group inspection, and 250 responses are received mechanical, electrical, and plumbing (MEP) projects management. Data is analyzed using Savvy PLS's structured condition presentation. Business performance is connected to the board systems that are based on proven evidence, evaluation, checking and helped the construction project succeed. Risk executives' methods fundamentally and well affected job completion, according to the assessment. Understanding executive risk valuation is much better. The results reveal that that in terms of genuine development projects, an efficiency study may present a fuller picture of risk the board in various enterprises. The practice of conducting systematic audits to assess the philosophy of writing is rarely employed in the execution of executive projects and risk analysis. The results revealed that the effective mechanical, electrical, and plumbing (MEP) performance of the board tactics is in improving project execution in Pakistan. The exploratory approach is employed to integrate previous studies on risk management executives and the implementation of business ventures in the field of development.

A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems

Conventional Digital Twins (DTs) in smart manufacturing rely on complex and time-intensive simulation models, hindering real-time DT-based decision-making. However, the availability of big data in Manufacturing Execution Systems (MES) enables training different Machine Learning (ML) models for fast and accurate predictions and decision assessments. Accordingly, this paper proposes an ML-Based Simulation Metamodeling Method (MLBSM) to facilitate DT-based decision-making for dynamic production scheduling in complex Stochastic Flexible Job Shop (SFJS) environments. The proposed MLBSM integrates three modules: a novel data vectorizing method (SPBM), multi-output Adaptive Boosting Regressor (ABR) models, and a new statistical risk evaluation method. SPBM converts unstructured production log data into numerical vectors for ABR training by calculating numeric penalty scores for each job based on the position of operations in the schedule queue. Each trained ABR predicts mean job completion times for various dynamic scenarios based on shift schedules. The risk evaluation method estimates the standard deviation of job completion times and calculates the delay probability scores for each job, aiding DT in promptly evaluating production schedules. Working seamlessly together, MLBSM modules present a novel way of using production log data for ML training and ultimately bypassing several computationally intensive simulation replications. In this research, a simulation model generates the synthetic MES data, focusing on the machining process at a photolithography workstation in the semiconductor manufacturing. Experiments demonstrate the MLBSM’s accuracy and efficiency, predicting high-risk jobs with over 80% recall and being at least 70 times faster than conventional simulation runs. Sensitivity analyses also confirm the MLBSM’s consistency under different workstation conditions.

MODEL PENJADWALAN BACKWARD HYBRID FLOW SHOP 2 STAGE UNTUK MEMINIMASI MEAN FLOWTIME

The scheduling problem is a common challenge when it comes to executing tasks, making the resolution of this issue crucial for decision-making in production activities. The problem addressed in this research is the company's desire to conduct production without waiting times between stages to maintain the quality of pharmaceutical raw materials. Additionally, the production results must meet predefined due dates. The production system formed is a Hybrid Flow Shop system, which is an extension of the Flow Shop concept where one or all stages have identical parallel machines. The objective of this study is to minimize the time between job start and job completion, defined in scheduling problems as the minimization of mean Flow time. The research collected data from 5 products in the company to model according to the research objectives. Process times for each stage and due dates for each product have been predetermined. After modeling and processing using the LINGO application, the optimal result obtained is backward scheduling with an average flow time of 12.6. This demonstrates that the model in this research effectively addresses the Hybrid Flow Shop problem using backward scheduling to minimize mean flow time

Design of an Iterative Method for MapReduce Scheduling Using Deep Reinforcement Learning and Anomaly Detection

Due to high complexities within distributed computing environments, there's a critical need for advanced scheduling frameworks that are capable of optimizing MapReduce systems. Current approaches have static policies that limit their capability to adapt to changing system dynamics and workload variations for different cloud scenarios. To overcome these issues, this study introduces a robust MapReduce framework empowered by intelligent scheduling algorithms, tailored to enhance system efficiency and resilience levels. The framework introduces three novel scheduling models: Deep Reinforcement Learning for Dynamic Job Scheduling (DRDJS), Anomaly Detection-driven Adaptive Scheduling (ADAS), and Cluster-based Job Categorization and Scheduling (CJCS). DRDJS utilizes deep reinforcement learning to dynamically generate optimal scheduling policies based on multiple metrics that include historical job data, system metrics, and workload characteristics. This adaptive approach leads to significant reductions in job completion times, outperforming static scheduling methods by up to 30%. Next, ADAS leverages anomaly detection to prioritize critical tasks and efficiently allocate resources in response to anomalies, resulting in a notable improve scheduling speed by up to 40%. Finally, CJCS employs clustering algorithms to categorize jobs based on their resource requirements and execution profiles, enabling more accurate resource allocation and reducing average job completion times by up to 25%. By integrating these methods into a unified scheduling framework, the proposed solution addresses the variability in job characteristics and system performance, thereby enhancing the overall throughput and stability of MapReduce operations.

ESEN: Efficient GPU sharing of Ensemble Neural Networks

Ensemble neural networks are widely applied in cloud-based inference services due to their remarkable performance, while the growing demand for low-latency services leads researchers to pay more attention to the execution efficiency of these models, especially the device utilization. It is highly desirable to fully utilize GPUs by multiplexing different inference tasks on the same GPU with advanced sharing technique, such as Multi-Process-Service (MPS). However, we find it struggling when applying MPS to Ensemble Neural Networks, which consist of multiple related sub-models. The critical challenge in this predicament revolves around the efficient allocation of resources within an ensemble, aiming to minimize job completion time.To tackle this challenge, we initially examine the interplay among individual neural networks within an ensemble, outlining a guideline for achieving the shortest job completion time. Subsequently, we establish a mathematical model to formalize the resource requirements of each neural network. We introduce a search-based allocation algorithm designed to swiftly identify optimal solutions. Finally, we introduce ESEN, comprising the search-based resource allocation algorithm and efficient model execution mechanisms within PyTorch. ESEN is augmented with customized execution mechanisms for user-friendly implementation. Experimental results demonstrate that proposed ESEN can attain an efficiency improvement up to 17.84% and a GPU utilization increase of 28.09% compared to the default strategy. With the optimization of GPU resource allocation, ESEN significantly improves the efficiency of ensemble models. It provides a low-latency and high-accuracy solution for online interactive services.

How has the COVID-19 pandemic shaped behavior in crowdsourcing? The role of online labor market training

AbstractThe outbreak of the COVID-19 pandemic and the associated lockdown measures have been a shock to market systems worldwide, affecting both the supply and demand of labor. Intensified by this pandemic-driven recession, online labor markets are in many ways at the core of the economic and policy debates about their technological innovation, which could be used as a way of economic reform and recovery. In this work, we focus on crowdsourcing, which is a specific type of online labor. We apply a unique dataset of labor data to investigate the effects of online training, a policy that was provided to requesters by the platform during the COVID-19 period. Our findings suggest that workers indirectly finance on-the-job online training by accepting lower wages during the pandemic. By utilizing a difference-in-difference research design, we also provide causal evidence that online training results in lower job completion time and the probability of being discontinued. Our findings show that both employers and employees in our online labor context reacted to the pandemic by participating in online labor procedures with different risk strategies and labor approaches. Our findings provide key insights for several groups of crowdsourcing stakeholders, including policy-makers, platform owners, hiring managers, and workers. Managerial and practical implications in relation to how online labor markets react to external shocks are discussed.

Enhanced Whale Optimization Algorithm for task scheduling in cloud computing environments

The escalation of cloud services, driven by their accessibility, improved performance, and cost-effectiveness, has led cloud service providers to consistently seek methods to expedite job completion, thereby boosting profits and reducing energy consumption expenses. Despite developing numerous scheduling algorithms, many of these techniques address only a specific objective within the scheduling process. To efficiently achieve better optimization results for the cloud task scheduling problem, a novel approach, the Enhanced Whale Optimization Algorithm (EWOA), is proposed. EWOA integrates the WOA with the Lévy flight. The incorporation of Lévy flight is tailored to broaden the search space of WOA, expediting convergence with adaptive crossover. The EWOA model is simulated using the Cloudsim tool and evaluated under diverse test conditions. The effectiveness of EWOA is assessed by employing various parameters and comparing them with existing algorithms. The results demonstrate that EWOA outperforms other algorithms in resource utilization, energy consumption, and execution cost, establishing its superiority in addressing the complexities of multi-objective cloud task scheduling.

659: Daily adaptive workflow for bladder cancer radiotherapy: can AI do the complete job online?

659: Daily adaptive workflow for bladder cancer radiotherapy: can AI do the complete job online?

Connection-Aware Heuristics for Scheduling and Distributing Jobs under Dynamic Dew Computing Environments

Due to the widespread use of mobile and IoT devices, coupled with their continually expanding processing capabilities, dew computing environments have become a significant focus for researchers. These environments enable resource-constrained devices to contribute computing power to a local network. One major challenge within these environments revolves around task scheduling, specifically determining the optimal distribution of jobs across the available devices in the network. This challenge becomes particularly pronounced in dynamic environments where network conditions constantly change. This work proposes integrating the “reliability” concept into cutting-edge human-design job distribution heuristics named ReleSEAS and RelBPA as a means of adapting to dynamic and ever-changing network conditions caused by nodes’ mobility. Additionally, we introduce a reinforcement learning (RL) approach, embedding both the notion of reliability and real-time network status into the RL agent. Our research rigorously contrasts our proposed algorithms’ throughput and job completion rates with their predecessors. Simulated results reveal a marked improvement in overall throughput, with our algorithms potentially boosting the environment’s performance. They also show a significant enhancement in job completion within dynamic environments compared to baseline findings. Moreover, when RL is applied, it surpasses the job completion rate of human-designed heuristics. Our study emphasizes the advantages of embedding inherent network characteristics into job distribution algorithms for dew computing. Such incorporation gives them a profound understanding of the network’s diverse resources. Consequently, this insight enables the algorithms to manage resources more adeptly and effectively.

Building a Fully-Automatized Active Learning Framework for the Semantic Segmentation of Geospatial 3D Point Clouds

In recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically ≪1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ll 1{\\%}$$\\end{document} of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become human processing units in our hybrid intelligence system mirroring the functionality of electronic processing units.

Lung fibre burden and risk of malignant mesothelioma in shipyard workers: a necropsy-based case-control study.

In Italy, the highest pleural cancer mortality and incidence have been observed among Italian regions where the 2 largest Italian shipyards were (and are) located. The objective of this study was to assess the exposure-response relationship for mesothelioma among male workers employed in the Monfalcone, Italy, shipyard. We conducted a necropsy-based case-control study. Cases (N = 102) were mesothelioma decedents and controls were those with lung cancer (N = 84). Complete job histories were available; the lung fibre content was measured using a scanning electron microscope with X-ray fluorescence, after sample preparation according to the European Respiratory Society guidelines. Odds ratios and 95% confidence intervals of mesothelioma by fibre type and lung fibre burden, as a categorical or continuous variable, were assessed by unconditional logistic regression, adjusted for age and time since exposure cessation. Analyses for the amphibole and chrysotile lung fibre burden were mutually adjusted. We calculated a cumulative exposure index by applying a job-exposure matrix to the job histories of study cases and assessed its correlation with the lung fibre burden. We found an odds ratio of 22.0 (confidence intervals 5.66-85.7) for the highest lung fibre burden category (mean 43.8 million total asbestos fibres per gram of dry tissue) compared with the reference (mean 0.48). Using log10-transformed lung fibre burden, we found that the odds ratio was 3.71 (confidence intervals 2.03-6.79) for a 10-fold lung fibre burden increase. Results for the amphibole lung fibre burden were similar. Odds ratios increased over chrysotile lung fibre burden categories (P-trend = 0.025), and the odds ratio for a 10-fold increase was 4.73 (confidence intervals 0.32-70.4). The cumulative exposure index was correlated with total and amphibole lung fibre burden, but not with chrysotile lung fibre burden. Mesothelioma risk was proportional to total, amphibole, and chrysotile lung fibre burden in shipyard workers.

An asymptotically optimal solution for the minimization of the variation of job completion times in two-stage proportionate no-wait flow shops

We propose an asymptotically optimal solution for the two-stage no-wait proportionate flow shop scheduling problem with the objective of minimizing the total absolute deviation of job completion times. The proposed solution is within 1% of the optimum for problems with more than 20 jobs. We also show that the problem is solved optimally in constant time for the special case where the two smallest jobs have equal processing times. Our results provide answers to most of the outstanding research questions for this problem.

A branch-and-price algorithm for unrelated parallel machine scheduling with machine usage costs

This paper considers unrelated parallel machine scheduling involving machine usage costs, in addition to classic job completion time-related costs. The usage cost of each machine is made up of a fixed usage cost and a variable usage cost proportional to the total processing time of the jobs assigned to it. These features model many practical situations where machine usage costs include, for example, rental fees when the machines are not owned but rented. To tackle this problem, four mathematical models based on the Shortest Weighted Processing Time (SWPT) rule are introduced. Additionally, the problem is formulated into a set-partitioning model, for which a branch-and-price algorithm is proposed with an appropriate branching strategy. This facilitates the development of an efficient pseudo-polynomial dynamic programming algorithm and a polynomial-time heuristic to solve the pricing problem. Extensive numerical experiments demonstrate the superior performance of the proposed branch-and-price algorithm over the four SWPT-based mathematical formulations and an existing branch-and-price algorithm designed for a special case. Notably, it can optimally solve instances involving up to 225 jobs and 15 machines within one hour. Moreover, statistical analyses reveal that the proposed polynomial-time heuristic significantly reduces the computation time, and the mathematical model based on the contribution of every job to the total weighted completion time exhibits the best overall performance.

A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain

Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.

Optimal policies for minimizing total job completion times and deviations from common due dates in unrelated parallel machine scheduling

Optimal policies for minimizing total job completion times and deviations from common due dates in unrelated parallel machine scheduling

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

The job shop scheduling problem (JSSP) has drained a lot of consideration since it is one of the most important optimization problems in the manufacturing domain. The scheduling method is crucial for optimizing the objective of minimizing makespan among thousands of jobs, but evaluating machine capacity for achieving this goal remains challenging despite the development of various population-based optimization algorithms for job shop scheduling problems. To improve the efficiency of Job shop scheduling, a novel Multi-objective Elitist Spotted Hyena Monotonic Scheduling (MESHS) technique is introduced. The proposed MESHS technique includes two major processes: machine selection and operation sequences. The number of jobs is considered for solving the scheduling problem. First, the machine selection is performed by applying the Multi-objective Elitist Spotted Hyena optimization technique. The optimization technique selects the optimal machines parallelly based on multiple objective functions such as energy consumption, CPU utilization, and job completion time. The fitness of every machine is calculated based on these multiple objective functions using Levenberg–Marquardt method. Then the Elitist strategy is applied to select the optimal machine based on fitness. After the machine selection, the rate-monotonic preemptive scheduling is modeled to provide a robust operation sequence by assigning high-priority jobs to the optimal machines. As a result, efficient job scheduling is achieved with minimum time. Finally, the experimental valuation is carried out using a benchmark OR-Library dataset with different factors such as job shop scheduling efficiency, job scheduling time, makespan, and memory consumption concerning a number of jobs.

Eventually-consistent federated scheduling for data center workloads

Data center schedulers operate at unprecedented scales today to accommodate the growing demand for computing and storage power. The challenge that schedulers face is meeting the requirements of scheduling speeds despite the scale. To do so, most scheduler architectures use parallelism. However, these architectures consist of multiple parallel scheduling entities that can only utilize partial knowledge of the data center’s state, as maintaining consistent global knowledge or state would involve considerable communication overhead. The disadvantage of scheduling without global knowledge is sub-optimal placements—tasks may be made to wait in queues even though there are resources available in zones outside the scope of the scheduling entity’s state. This leads to unnecessary queuing overheads and lower resource utilization of the data center.In this paper, we extend our previous work on Thiyyakat et al. (2023), a federated decentralized data center scheduling architecture that uses eventual consistency. The architecture utilizes both parallelism and an eventually consistent global state in each of its scheduling entities to make fast decisions in a scalable manner. In our work, we compare Megha with 3 scheduling architectures — Sparrow, Eagle, and Pigeon, using simulation. We also evaluate Megha’s prototype on a 123-node cluster and compare its performance with Pigeon’s prototype using cluster traces. The results of our experiments show that Megha consistently reduces delays in job completion time when compared to other architectures.

Design And Building Of The Utility Performance Monitoring System At PT. XYZ With Brainstorming Method

In general, utilities in engineering are devices or systems designed to provide basic needs or to help complete certain jobs or processes. In engineering, utilities generally refer to systems or devices designed to provide basic needs such as electricity, water, gas, or telecommunications. At PT. The brainstorming method is used to produce creative ideas that can be applied in developing utility monitoring systems. Various ideas are generated through discussion and debate between brainstorming participants. Then, these ideas are analyzed and selected to select those that best suit PT XYZ's needs. Therefore, the author created a utility performance monitoring system to make it easier for the engineering department to carry out work activities. This utility performance monitoring system was created using a JavaScript framework, namely AngularJS for the frontend and ExpressJS for the backend. Apart from that, the author also tested PT XYZ employees so that the system runs according to needs. Of the 10 respondents, more than 50% of the accumulated respondents stated that the utility performance monitoring system application was running satisfactorily and very satisfactorily, there were 10% to 30% of respondents who stated that it was running well and 10% of respondents said it was not working well. Based on the test results above, it was found that the utility monitoring system at PT XYZ could be categorized as a fairly informative information system for the engineering department.

Renewable-Aware Frequency Scaling Approach for Energy-Efficient Deep Learning Clusters

Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors.