An alternative artificial bee colony algorithm with destructive-constructive neighbourhood operator for the problem of composing medical crews

We address the problem of composing medical crews according to the principles of equity and efficiency.An artificial bee colony algorithm and a local search procedure are proposed for this problem.Bees partially destroy and heuristically construct previous solutions to produce new candidate configurations.The results on three context with increasing difficult levels show the proposal as a tool of choice for the problem. We propose an artificial bee colony algorithm for the problem of composing medical crews with equity and efficiency principles. The objective is to constitute crews of practitioners with different skills and as efficient as possible. The artificial bee colony algorithm is a swarm intelligence model inspired in the foraging behaviour of honeybees. In this framework, bees produce candidate solutions for the problem by exploring the vicinity of food sources. The proposed approach exploits useful knowledge of the problem at this neighbourhood exploration, considering the partial destruction and heuristical reconstruction of selected solutions. We show the effectiveness of our model through an empirical analysis where three different contexts are considered: easy, challenging, and difficult problem cases. The results are compared with those of a genetic algorithm and the current state-of-the-art method for this problem.