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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Published in:2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE) pp. 964 - 971
Main Authors: Chen, Make, Geng, Haitian, Li, Jingran
Format: Conference Proceeding
Language:English
Published: IEEE 26.03.2025
Subjects:
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
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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.
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
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  givenname: Haitian
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  organization: Dongbei University of Finance and Economics,School of Accounting,Dalian,China
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  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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