A novel simulated metamorphosis algorithm for homecare nurse scheduling

Inspired by the biological concepts of metamorphosis evolution, this paper presents a novel simulated metamorphosis (SM) algorithm for solving the homecare nurse scheduling problem in a fuzzy environment. The algorithm is motivated by the need for interactive, multi-objective, and efficient optimization approaches to solving problems with fuzzy conflicting goals and constraints. The SM goes through initialization, growth, and maturation phases, mimicking the metamorphosis process. Initialization generates a candidate solution which successively goes through growth and maturation loops. Comparative computational tests on benchmark problems show that, when compared to other algorithms, SM is more efficient and effective, producing near-optimal solutions within reasonable computation times.