scholarly journals Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gilseung Ahn ◽  
Sun Hur
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Real Time ◽  
Fitness Function ◽  
Cloud Manufacturing ◽  
Genetic Operators ◽  
Real Time Scheduling ◽  
Chromosome Representation ◽  
Time Scheduling ◽  
The Mathematical Model

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

scholarly journals An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing

2020 ◽  
Vol 10 (7) ◽  
pp. 2491
Author(s):  
Shengkai Chen ◽  
Shuliang Fang ◽  
Renzhong Tang
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Large Scale ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Matching Task ◽  
Task Completion ◽  
Ann Model ◽  
Real Time Scheduling ◽  
Time Scheduling

The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type matching, task precedence, resource occupation, and logistics duration, need to be met, and the internal manufacturing plan of providers must also be considered. Since the platform aggregates massive manufacturing resources to serve large-scale manufacturing tasks, the space of feasible solutions is huge, resulting in many conventional search algorithms no longer being applicable. In this paper, we considered resource allocation as the key procedure for real-time scheduling, and an ANN (Artificial Neural Network) based model is established to predict the task completion status for resource allocation among candidates. The trained ANN model has high prediction accuracy, and the ANN-based scheduling approach performs better than the preferred method in terms of the optimization objectives, including total cost, service satisfaction, and make-span. In addition, the proposed approach has potential in the application for smart manufacturing or Industry 4.0 because of its high response performance and good scalability.

Dynamic hard-real-time scheduling using genetic algorithm for multiprocessor task with resource and timing constraints

2009 ◽  
Vol 36 (1) ◽  
pp. 852-860 ◽  
Author(s):  
Shu-Chen Cheng ◽  
Der-Fang Shiau ◽  
Yueh-Min Huang ◽  
Yen-Ting Lin
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Timing Constraints ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
Hard Real Time

Real-Time Scheduling of Cloud Manufacturing Services Based on Dynamic Data-Driven Simulation

2019 ◽  
Vol 15 (9) ◽  
pp. 5042-5051 ◽  
Author(s):  
Longfei Zhou ◽  
Lin Zhang ◽  
Lei Ren ◽  
Jian Wang
Keyword(s):  
Real Time ◽  
Cloud Manufacturing ◽  
Data Driven ◽  
Dynamic Data ◽  
Real Time Scheduling ◽  
Time Scheduling

scholarly journals A New Hybrid Genetic Algorithm-based Approach for Critical Multiprocessor Real-Time Scheduling with Low Power Optimization

2019 ◽  
Vol 159 ◽  
pp. 1547-1557
Author(s):  
Ibrahim Gharbi ◽  
Hamza Gharsellaoui ◽  
Sadok Bouamama
Keyword(s):  
Genetic Algorithm ◽  
Low Power ◽  
Real Time ◽  
Power Optimization ◽  
Hybrid Genetic Algorithm ◽  
Real Time Scheduling ◽  
Time Scheduling

scholarly journals A Deep-Reinforcement-Learning-Based Optimization Approach for Real-Time Scheduling in Cloud Manufacturing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 9987-9997
Author(s):  
Huayu Zhu ◽  
Mengrong Li ◽  
Yong Tang ◽  
Yanfei Sun
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Cloud Manufacturing ◽  
Optimization Approach ◽  
Real Time Scheduling ◽  
Time Scheduling

scholarly journals Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kai Nie ◽  
Qinglei Zhou ◽  
Hong Qian ◽  
Jianmin Pang ◽  
Jinlong Xu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Multicore Processors ◽  
Fitness Function ◽  
Selection Problem ◽  
Initial Population ◽  
Chromosome Representation ◽  
Nested Loops ◽  
Selection For ◽  
Specific Implementation

Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

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