Job Completion Research Articles

Sustainable Distributed Scheduling Problem

Many strategies have been devised to facilitate scheduling determinations per the dynamic production landscape. Also, the repercussions of environmental degradation intensify, and the focus on sustainable production methodologies has acquired substantial recognition. In addition, production managers are required to carefully determine the suitable schedule for each machine, considering environmental criteria. In such cases, another challenge in the manufacturing system alongside the minimization of job completion time which is important is reducing the environmental parameters impacts. Hence, this study focuses on addressing the challenge of distributed scheduling problems within the context of sustainable production. For energy-efficient factory selection, in this research, support vector machine (SVM) algorithm and improved particle swarm optimization (IPSO) have been used. In this case, first, factories with the least amount of pollution, waste, and energy consumption were selected. Second, the composition of intelligent algorithms such as gravitational search algorithm (GSA) and genetic algorithm (GA) was used to propose an appropriate distributed jobs scheduling in selected distributed factories. The simulation results show that this intelligent scheduling of distributed sustainable factories has significant potential to minimize environmental parameters inside production cycle optimization.

The Deviation Issue and Total Completion Time on a Single Machine Scale Subject to Decreasing and Growing Linear Deterioration are Theoretically Addressed

We addressed in this research the schedule of activities that are subject to the condition of linear degradation in declining and growing rate states, respectively, with the goal of guaranteeing that the primary process's duration would be fixed (ρ) and that its implementation would take place in a single machine. The objective was to find the optimal schedule for calculating the total completion time ∑_(ḭ=1)^ń▒C_ḭ , and the optimal schedule for the deviation problem (sum of squares of the difference between the tasks' total required time and the previously established due date time. where the objective is to minimize the squared deviation of job completion times from a due date), two theories that state as the Λ-shaped scheduling theory with increasing rate ƛ_ḭ of jobs and the v-shaped scheduling theory with decreasing rate đ_ḭ of jobs satisfy the conditions 0<đ_ḭ<1 ańd đ_ḭ<1/(ń-1), have been demonstrated in situations where the base is implemented at a constant basic processing time .

An Optimization Model for Production Scheduling in Parallel Machine Systems

The efficiency and quality of the manufacturing industry are greatly influenced by production scheduling, which makes it a crucial aspect. A well-designed production scheduling scheme can significantly enhance manufacturing efficiency and reduce enterprise costs. This paper presents a tailored optimization model designed to address a more complex production scheduling problem that incorporates parallel machines and preventive maintenance. The proposed solutions aim to achieve a balance between job sequence and machine reliability, considering the minimum maintenance cost rate for determining maintenance cycles of deteriorating machines in real manufacturing scenarios. Furthermore, the objective of minimizing the maximum completion time guides machine assignment and job sequence based on maintenance constraints. The innovation lies in the introduction of a greedy algorithm that utilizes a water injection model to address this NP-hard integrated problem. A pre-distribution model is constructed using the water injection model, and its solution is utilized as input for constructing the production scheduling model, which aids in determining machine assignment and job sequence. This algorithm demonstrates remarkable effectiveness and efficiency, enabling the achievement of an optimal solution. A numerical example is presented to illustrate the computational process, accompanied by an extensive discussion of the results showcasing improved performance. Furthermore, the optimization model developed in this paper can be adapted to tackle the production scheduling problem with modifications tailored for parallel machines.

Pharmacist perceptions of motivation and well-being using Self-Determination Theory: A qualitative study.

Many US hospitals and health systems have implemented well-being programs to address the clinician well-being and burnout crisis. Most community pharmacists experience at least one symptom of burnout, yet they have been overlooked for inclusion in well-being initiatives. To explore community pharmacists' perceptions of how motivation and burnout impact patient care and how fulfillment of basic psychological needs (autonomy, competence, and relatedness) impacts motivation and well-being. Focus groups were conducted with 20 community pharmacists. A semistructured focus group guide was developed using Self-Determination Theory (SDT). Transcriptions from the focus groups were analyzed using deductive qualitative analysis with SDT as a framework and inductive analysis to code subthemes. Our findings revealed that pharmacists who feel burnout experience depersonalization toward patients which lowers the quality of patient-pharmacist interactions. Pharmacists who did not feel burnout expressed a sense of professional fulfillment, which motivated them to provide patient-centered care. Pharmacists indicated that unrealistic expectations from patients and corporate management, such as pressure from patients to fill prescriptions quickly and management expectations to meet prescription fill quotas, negatively impacted autonomy. Conversely, having access to clinical information, workflow optimization, and realistic job expectations supported autonomy. Poor relationships with patients and coworkers negatively impacted relatedness and contributed to communication barriers, workplace negativity, and emotional detachment from work. Relatedness was facilitated by building relationships and mutual respect with patients and coworkers and cultivating a positive work culture. Expectations for perfection and the need for multitasking when understaffed diminished competency. Adequate staffing and allocation of time to complete job duties served to support competency. Community pharmacists are faced with situations that undermine autonomy, relatedness, and competency, which according to SDT need to be fulfilled to facilitate well-being.

Cloud data centers and networks: Applications and optimization techniques

As the volume and complexity of big data continue to escalate, optimizing the performance, scalability, and energy efficiency of big data applications within cloud data centers has become increasingly crucial. This journal presents a comprehensive survey of current optimization techniques, focusing on data placement, job scheduling, and network configurations tailored for cloud environments. We explore the impact of various data center topologies on the performance of big data frameworks like Hadoop, emphasizing the trade-offs between performance and energy efficiency. Advanced methodologies, including dynamic data placement strategies, locality-aware scheduling, and innovative reduce task placement techniques, are reviewed in depth. Additionally, we highlight the importance of network power effectiveness (NPE) and examine the role of optical and electronic switching technologies in enhancing data center efficiency. By synthesizing findings from recent studies, this paper provides valuable insights into the optimization of cloud data centers, offering recommendations for improving resource utilization and reducing job completion times while maintaining energy efficiency. The findings contribute to the ongoing efforts to scale and adapt cloud data infrastructures for the rapidly growing demands of big data applications.

UNISON framework with fuzzy decision tree for water conservation in the dynamic scheduling of the textile dyeing process

PurposeThis study aimed to optimize the dyeing scheduling process with uncertain job completion time to reduce resource consumption and wastewater generation, and while reconciling the conflicting objectives of minimizing the makespan and the need to limit the production on specific machines to minimize rework.Design/methodology/approachWe employed a UNISON framework that integrates fuzzy decision tree (FDT) to optimize dyeing machine scheduling by minimizing the makespan and water consumption, in which the critical attributes such as machine capacity and processing time can be incorporated into the scheduling model for smart production.FindingsAn empirical study of a high-tech textile company has shown the validity and effectiveness of the proposed approach in reducing the makespan and water consumption by over 8% while high product quality and efficiency being maintained.Originality/valueHigh-tech textile industry is facing the challenges in reducing the environmental impact of the dyeing process while maintaining product quality and efficiency for smart production. Conventional scheduling approaches have not addressed the relationship between machine groups and reworking, resulting in difficulty in controlling the makespan and water consumption and increasing costs and environmental issues. The proposed approach has addressed uncertain job completion via integrating FDT into the scheduling process to effectively reduce makespan and wastewater. The results have shown practical viability of the developed solution in real settings.

A Mixed Neuro Graph Approach with Gradient Boosting to Hybrid Job-Shop Scheduling to Minimize a Regular Function of Job Completion Times and Numbers of Used Machines

The paper considers a multi-stage processing system including sets of identical (parallel) machines and a set of dedicated machines processing different operations of the given jobs in any sectors of economy. Based on the weighted Mixed Neuro graph model, the paper proposes adaptive algorithms for solving this problem via appropriate Mixed Neuro graph transformations. The main novelty is (1) low demands on the source data-unlike classical machine learning algorithms, the approach can offer stable interpretable results even with a short dataset size; (2) the number of new matrix multiplication operations that make up the main load when training models increases linearly with the number of new data from 0 to 999 time periods; (3) the results of the model are repeatable due to the stability of the coefficients of the model. These algorithms are able to solve (exactly or heuristically) the tested instances with N jobs and W types of parallel identical machines within on the personal computer. The gradient boosting result is in interval 5.9677410-3.4982093.

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

DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud

Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model performance, but at the cost of increased GPU/CPU/memory usage. Meanwhile, tech companies have built extensive cloud-based services to accelerate training DLRM models at scale. In this paper, we conduct a deep investigation of the DLRM training platforms at AntGroup and reveal two critical challenges: low resource utilization due to suboptimal configurations by users and the tendency to encounter abnormalities due to an unstable cloud environment. To overcome them, we introduce DLRover, an elastic training framework for DLRMs designed to increase resource utilization and handle the instability of a cloud environment. DLRover develops a resource-performance model by considering the unique characteristics of DLRMs and a three-stage heuristic strategy to automatically allocate and dynamically adjust resources for DLRM training jobs for higher resource utilization. Further, DLRover develops multiple mechanisms to ensure efficient and reliable execution of DLRM training jobs. Our extensive evaluation shows that DLRover reduces job completion times by 31%, increases the job completion rate by 6%, enhances CPU usage by 15%, and improves memory utilization by 20%, compared to state-of-the-art resource scheduling frameworks. DLRover has been widely deployed at AntGroup and processes thousands of DLRM training jobs on a daily basis. DLRover is open-sourced and has been adopted by 10+ companies.

ResLake : Towards Minimum Job Latency and Balanced Resource Utilization in Geo-Distributed Job Scheduling

At internet scale companies like ByteDance, data is generated and consumed at enormously high speed by many different applications. Achieving low latency on such big data jobs is an important problem. However, the naive approach of aggregating all the data required by a job to a single location is not always feasible in a geo-distributed environment. Similarly, existing approaches in geo-distributed job scheduling often try to minimize WAN usage, which may come at the cost of latency. Another crucial element to ensure low latency is resource load balancing among DCs, which enables flexibility in job scheduling and avoids resource bottlenecks. Therefore, to minimize latency, optimizing job completion time (JCT) while maintaining resource utilization balance is important. To this end, we propose ResLake , a global scheduling platform for data-intensive workloads. ResLake aims to reduce JCT of geo-distributed applications while balancing the compute (CPU/Memory) and storage (Disk) usages across DCs and efficiently using WAN interconnections. We have deployed ResLake in ByteDance's production for over 1.5 years. ResLake has scheduled billions of jobs since its deployment. We find that ResLake improves JCT of jobs by at least 20%, and can improve resource utilization balance across DCs by up to 53%.

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

The 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.