Research on Multi-Stage Decision Optimization Based on Monte Carlo Simulation and Deep Q-Network Algorithm
This paper presents a comprehensive study on multi-stage decision optimization leveraging Monte Carlo simulation and Deep Q-Network (DQN) algorithms. We integrate Monte Carlo simulation with sequential sampling, progressively drawing samples and dynamically calculating the log-likelihood ratio to de...
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| Veröffentlicht in: | 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE) S. 964 - 971 |
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| Sprache: | Englisch |
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26.03.2025
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| Abstract | This paper presents a comprehensive study on multi-stage decision optimization leveraging Monte Carlo simulation and Deep Q-Network (DQN) algorithms. We integrate Monte Carlo simulation with sequential sampling, progressively drawing samples and dynamically calculating the log-likelihood ratio to determine the inspection plan. Furthermore, the paper delves into the quality control decision-making problem in multi-stage production processes. Utilizing a DQN model coupled with Bayesian optimization for hyperparameter tuning, our approach employs reinforcement learning where an agent interacts with the environment to progressively learn the optimal decision-making strategy. Experimental results demonstrate that the DQN model effectively reduces inspection and disassembly costs. Notably, when the defective rate is low or inspection costs are high, the agent tends to minimize inspection frequencies. |
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| AbstractList | This paper presents a comprehensive study on multi-stage decision optimization leveraging Monte Carlo simulation and Deep Q-Network (DQN) algorithms. We integrate Monte Carlo simulation with sequential sampling, progressively drawing samples and dynamically calculating the log-likelihood ratio to determine the inspection plan. Furthermore, the paper delves into the quality control decision-making problem in multi-stage production processes. Utilizing a DQN model coupled with Bayesian optimization for hyperparameter tuning, our approach employs reinforcement learning where an agent interacts with the environment to progressively learn the optimal decision-making strategy. Experimental results demonstrate that the DQN model effectively reduces inspection and disassembly costs. Notably, when the defective rate is low or inspection costs are high, the agent tends to minimize inspection frequencies. |
| Author | Geng, Haitian Chen, Make Li, Jingran |
| Author_xml | – sequence: 1 givenname: Make surname: Chen fullname: Chen, Make email: 95560477@qq.com organization: Dongbei University of Finance and Economics,School of Statistics,Dalian,China – sequence: 2 givenname: Haitian surname: Geng fullname: Geng, Haitian email: GHT20040622@163.com organization: Dongbei University of Finance and Economics,School of Accounting,Dalian,China – sequence: 3 givenname: Jingran surname: Li fullname: Li, Jingran email: li-j07@outlook.com organization: Dongbei University of Finance and Economics,International Business College,Dalian,China |
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| Snippet | This paper presents a comprehensive study on multi-stage decision optimization leveraging Monte Carlo simulation and Deep Q-Network (DQN) algorithms. We... |
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| SubjectTerms | Bayes methods Bayesian optimization Costs Decision making Deep Q-Network algorithm Heuristic algorithms Inspection Monte Carlo methods Monte Carlo simulation Optimization Process control Production Quality control |
| Title | Research on Multi-Stage Decision Optimization Based on Monte Carlo Simulation and Deep Q-Network Algorithm |
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