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

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Optimization on Medical Material Distribution Management System Based on Artificial Intelligence Robot.

Ying Yang¹, Limin Dong², Hua Rong¹

¹Department of Nursing, General Hospital of Taiyuan Iron & Steel Co.,Ltd., Taiyuan 030008, China.

Journal of Healthcare Engineering

|April 8, 2021

Summary

This summary is machine-generated.

This study introduces an artificial intelligence robot logistics system to optimize medical material management, reducing costs and ensuring continuous hospital operations. The AI-driven approach minimizes inventory waste and improves efficiency in ordering and distribution.

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Area of Science:

Operations Research
Artificial Intelligence
Healthcare Management

Background:

Traditional medical material management systems suffer from inefficiencies due to subjective operator judgment, leading to inventory imbalances and increased operational costs.
Lack of systematic analysis in current systems results in either excessive or insufficient stock, impacting hospital efficiency and financial resources.

Purpose of the Study:

To develop an artificial intelligence (AI) robot logistics system to minimize total costs associated with medical material ordering and distribution.
To address real-world hospital operational constraints by constructing robust ordering and distribution scheduling models.
To enhance decision-making processes for optimal hospital operations and ensure continuity.

Main Methods:

Construction of an AI robot logistics system tailored for medical material management.
Development of spatiotemporal network-based ordering and distribution scheduling planning models for single material certainty, single material stochastic, and multiple material stochastic scenarios.
Experimental design to evaluate the performance and effectiveness of the developed AI system.

Main Results:

The developed AI system demonstrated a positive impact on medical material management.
The proposed models effectively addressed complexities in ordering and distribution scheduling under various conditions.
Experimental validation confirmed the system's capability to improve efficiency and reduce costs.

Conclusions:

The AI robot logistics system offers a systematic and data-driven approach to medical material management.
The spatiotemporal network models provide a valuable tool for hospitals to optimize resource allocation and ensure operational stability.
This research offers a practical reference for enhancing future medical material distribution management systems.