A novel medical information management and decision model for uncertain demand optimization

Area of Science:

• Healthcare Management
• Operations Research
• Applied Mathematics

Background:

• Accurate procurement volume planning is crucial for controlling medicine inventory costs.
• Uncertain demand, especially for time-sensitive and seasonal biomedicines, complicates procurement decisions.
• Fuzzy mathematics offers a superior approach to modeling uncertain demand compared to traditional random distribution functions.

Purpose of the Study:

• To develop an innovative medical information management and decision model for optimizing procurement under uncertain demand.
• To enhance decision-making processes in medicine inventory management.
• To address the challenges posed by fluctuating demand in the pharmaceutical supply chain.

Main Methods:

• A novel optimal management and decision model was developed.
• The model integrates fuzzy mathematics for handling demand uncertainty.
• A comprehensive improved particle swarm algorithm was employed for optimization.

Main Results:

• The developed optimal management and decision model demonstrated effectiveness in reducing medicine inventory costs.
• The model provides a robust framework for managing procurement volumes.
• Significant cost reductions in medicine inventory were observed.

Conclusions:

• The improved particle swarm optimization algorithm simplifies and enhances fuzzy interference, reducing computational complexity.
• The novel model enables accurate procurement volume decisions even with uncertain demand.
• This approach offers a practical solution for optimizing pharmaceutical inventory management.