Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

Yuan, Minghai; Li, Yadong; Zhang, Lizhi; Pei, Fengque

doi:10.1016/j.rcim.2021.102141

Cited by 82 publications

(29 citation statements)

References 21 publications

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“…Department network intercommunication and information sharing, real-time broadcast of traffic information, traffic failure in a timely manner [18]; the use of bus priority control system to ensure the efficiency and safety of bus operation; strengthen the R&D and application of smart city transportation, encourage the cooperation between enterprises and research institutions, etc. [19,20]. Through the road control station, FIRD technology, advanced monitoring system, and natural traffic flow roadside system, the entering vehicles can be automatically identified, and "road congestion tax" is levied at a specific time, which effectively reduces traffic flow, reduces traffic congestion by 25%, increases road traffic capacity by 80%, and 2…”

Section: Related Workmentioning

confidence: 99%

Intelligent City Traffic Scheduling Optimization Based on Internet of Things Communication

Jiang

2021

Wireless Communications and Mobile Computing

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The application of Internet of Things technology in the construction of smart city traffic system is the inevitable choice of smart city traffic system to promote the development of road traffic in the process of urban development. First of all, the traffic in the city, on the basis of the research background and significance, research on the Internet of Things, and wisdom of the construction of the urban traffic are summarized and analyzed; secondly, on the basis of requirement analysis and feasibility analysis, combined with the principles and goals of the construction of the system, the establishment of the model of urban traffic based on Internet of Things and the city’s architecture based on Internet of Things intelligent urban traffic system and design are put forward. Finally, an improved ant colony algorithm is proposed in the aspect of system optimal path algorithm, and its superiority is verified. The results show that complementary networks can significantly improve the robustness of networks. These conclusions provide a theoretical basis for the construction of smart city public transport network traffic scheduling and provide model support for the operation and management of smart city public transport.

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Section: Related Workmentioning

confidence: 99%

Intelligent City Traffic Scheduling Optimization Based on Internet of Things Communication

Jiang

2021

Wireless Communications and Mobile Computing

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“…For a VM or physical node, CPU and memory are two important resources that are often used to measure its load or utilization in the construction of scheduling models, such as the literatures [14,22,[25][26][27][28][29][30][31][32][33][34][35][36][37]. Because…”

Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

“…In addition, our model dynamically adjusts the resource balance according to the running state of VMs on physical nodes, whereas the previous dynamic scheduling models are based on data traffic mainly. -A system is built to simulate a big data center in cloud, and our proposed model is evaluated via comparing with Greedy [23], Round Robin [24] and the Non-dominated Sorting Genetic Algorithm (NSGA II) [25] in Eucalyptus. The experimental results demonstrate that our model significantly outperforms the representative Round Robin, Greedy and NSGA II, especially in dynamic and unbalanced data flow distribution.…”

Section: Introductionmentioning

confidence: 99%

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Zhang

Yan

2021

Preprint

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Efficient resource scheduling is one of the most critical issues for big datacenters in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

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“…Early studies focus on rescheduling resources offline to achieve new schedules, which may suspend the manufacturing processes and increase the lead time. 5 Simulation technologies (e.g. multi-agent system) build scheduling algorithms with empirical rules and historical data, but they are limited in achieving optimal schedules in a changeable manufacturing environment.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

Zhou

Zhu

Tang

et al. 2022

Advances in Mechanical Engineering

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The job-shop scheduling problem (JSSP) is a complex combinatorial problem, especially in dynamic environments. Low-volume-high-mix orders contain various design specifications that bring a large number of uncertainties to manufacturing systems. Traditional scheduling methods are limited in handling diverse manufacturing resources in a dynamic environment. In recent years, artificial intelligence (AI) arouses the interests of researchers in solving dynamic scheduling problems. However, it is difficult to optimize the scheduling policies for online decision making while considering multiple objectives. Therefore, this paper proposes a smart scheduler to handle real-time jobs and unexpected events in smart manufacturing factories. New composite reward functions are formulated to improve the decision-making abilities and learning efficiency of the smart scheduler. Based on deep reinforcement learning (RL), the smart scheduler autonomously learns to schedule manufacturing resources in real time and improve its decision-making abilities dynamically. We evaluate and validate the proposed scheduling model with a series of experiments on a smart factory testbed. Experimental results show that the smart scheduler not only achieves good learning and scheduling performances by optimizing the composite reward functions, but also copes with unexpected events (e.g. urgent or simultaneous orders, machine failures) and balances between efficiency and profits.

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Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

Cited by 82 publications

References 21 publications

Intelligent City Traffic Scheduling Optimization Based on Internet of Things Communication

Intelligent City Traffic Scheduling Optimization Based on Internet of Things Communication

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Reinforcement learning for online optimization of job-shop scheduling in a smart manufacturing factory

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