Discrete flower pollination algorithm for patient admission scheduling problem

Area of Science:

• Health Informatics
• Operations Research
• Computational Intelligence

Background:

• The Patient Admission Scheduling Problem (PASP) is a critical challenge in healthcare management.
• PASP involves complex combinatorial optimization for assigning patients to hospital resources under strict constraints.
• Maximizing patient comfort is a key objective in effective healthcare scheduling.

Purpose of the Study:

• To introduce a novel meta-heuristic optimization method for the PASP.
• To adapt the Flower Pollination Algorithm (FPA) for discrete optimization domains relevant to healthcare scheduling.
• To enhance the algorithm's search capabilities through various neighborhood structures.

Main Methods:

• Application of the Discrete Flower Pollination Algorithm (DFPA) to the PASP.
• Discretization of the continuous FPA for suitability in combinatorial problems.
• Utilizing diverse neighborhood structures to improve solution exploration.

Main Results:

• The DFPA demonstrated high efficiency in solving the PASP.
• Performance was evaluated on benchmark datasets against existing algorithms.
• The method proved effective in optimizing patient scheduling and resource allocation.

Conclusions:

• The Discrete Flower Pollination Algorithm is a highly efficient method for the PASP.
• This approach offers a promising solution for improving healthcare scheduling and patient comfort.
• The DFPA's adaptability suggests potential for broader applications in complex scheduling problems.