scholarly journals Optimization Method of Equipment Maintenance Resource Scheduling Based on Hidden Semi-Markov Model

2021 ◽  
Vol 6 (2) ◽  
pp. 23-31
Author(s):  
Pengrui Wang ◽  
Bailin Liu ◽  
Tao Zhao ◽  
Pengxiang Cao
Keyword(s):  
Markov Model ◽  
Resource Scheduling ◽  
Optimization Method ◽  
Equipment Maintenance

scholarly journals Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanhua Yang ◽  
Ligang Yao
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimization Model ◽  
Power Grid ◽  
Optimization Method ◽  
Optimal Decision ◽  
Maintenance Cost ◽  
Equipment Maintenance ◽  
Safety Constraints ◽  
Maintenance Plan

The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.

A generalized interval probability-based optimization method for training generalized hidden Markov model

2014 ◽  
Vol 94 ◽  
pp. 319-329 ◽  
Author(s):  
Fengyun Xie ◽  
Bo Wu ◽  
Youmin Hu ◽  
Yan Wang ◽  
Guangfei Jia ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Optimization Method ◽  
Interval Probability

An Optimization Method for P2P Resource Scheduling

2012 ◽  
Vol 6 (4) ◽  
pp. 159-165 ◽  
Author(s):  
Shidong Zhang ◽  
Gengyu Wei ◽  
Bai Wang ◽  
Deyu Yuan
Keyword(s):  
Resource Scheduling ◽  
Optimization Method

scholarly journals Optimization Method of Art Design Resource Scheduling for 6G Network Environment in Colleges and Universities

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Meng Zhou ◽  
Dong Yang
Keyword(s):  
Resource Scheduling ◽  
Optimization Method ◽  
Colleges And Universities ◽  
Control Group ◽  
Network Environment ◽  
Scheduling Optimization ◽  
Experimental Control ◽  
Art And Design ◽  
Traditional Art ◽  
Scheduling Method

In order to solve the problem of low flexibility margin of traditional art and design resource scheduling in colleges and universities, an optimization method for art and design resource scheduling in the 6G network environment has been designed. By determining the flexibility margin index of university art and design resource scheduling, the scheduling optimization model is established, the scheduling communication parameters are set for the 6G network environment, the delay of university art and design resource scheduling is perceived, the period of insufficient flexibility is searched, and elimination measures are taken to realize the optimization of university art and design resource scheduling. The experimental results show that the margin of the designed scheduling method is always higher than that of the experimental control group in the same scheduling period, which can solve the problem of low scheduling flexibility margin of traditional methods.

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