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Updated: Jun 12, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A review of simulation optimization with connection to artificial intelligence.

Yijie Peng^1,2, Chun-Hung Chen³, Michael C Fu⁴

¹PKU-WUHAN Institute for Artificial Intelligence, Guanghua School of Management, Peking University, Beijing 100871, China.

Fundamental Research

|June 11, 2026

Summary

This review explores simulation optimization and artificial intelligence (AI). It highlights AI's role in stochastic gradient estimation for deep learning and reinforcement learning, and ranking/selection for Monte Carlo tree search.

Keywords:

Artificial intelligence Deep Learning Ranking and selection Reinforcement Learning Simulation optimization Stochastic gradient estimation

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Last Updated: Jun 12, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

Simulation optimization is crucial for complex systems.
Artificial intelligence (AI) offers advanced computational techniques.
Bridging simulation optimization and AI can enhance problem-solving capabilities.

Purpose of the Study:

To review simulation optimization methods and their integration with AI.
To focus on stochastic gradient estimation and ranking/selection techniques.
To examine the intersection of simulation optimization and AI in inventory management.

Main Methods:

Literature review of simulation optimization.
Analysis of AI techniques, including deep learning and reinforcement learning.
Exploration of Monte Carlo tree search algorithms.

Main Results:

Stochastic gradient estimation is key for training neural networks in deep learning and reinforcement learning.
Ranking and selection methods are applicable to node selection in Monte Carlo tree search.
Inventory management is a shared research area for both simulation optimization and AI.

Conclusions:

AI techniques offer powerful tools for simulation optimization.
Further research can leverage AI for advanced simulation optimization applications.
The integration of AI and simulation optimization has broad implications for various fields, including inventory management.