Optimal Deployment in Emergency Medicine with Genetic Algorithm Exemplified by Lifeguard Assignments

Area of Science:

• Operations Research
• Computer Science
• Emergency Medicine

Background:

• Workforce planning in emergency medicine involves complex constraints, including personnel qualifications and individual preferences.
• Volunteer assignments, such as lifeguards, present unique challenges in balancing mandatory requirements with personal wishes.

Purpose of the Study:

• To develop and evaluate a genetic algorithm for optimizing lifeguard assignments along the German North Sea and Baltic Sea coasts.
• To compare the efficiency and effectiveness of the genetic algorithm against a brute force approach for this specific planning problem.

Main Methods:

• Implementation of a genetic algorithm designed to solve the lifeguard assignment problem.
• Comparison of the genetic algorithm's runtime and solution quality against a brute force method.
• Analysis of performance based on the number of deployment locations.

Main Results:

• The genetic algorithm achieved sub-second runtimes for lifeguard assignment.
• The genetic algorithm consistently produced optimal solutions within fewer than 10 generations.
• The genetic approach demonstrated superior runtime performance compared to brute force when more than three deployment locations were involved.

Conclusions:

• Genetic algorithms provide an efficient and effective method for solving complex workforce planning problems in emergency services.
• This approach successfully balances hard constraints (qualifications) with soft constraints (personnel wishes) in volunteer assignments.
• The developed genetic algorithm offers a practical tool for optimizing lifeguard deployment, ensuring both safety and personnel satisfaction.