SCHEDULING NURSES USING A TABU-SEARCH HYPERHEURISTIC

Abstract

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. A number of hyperheuristics have been developed over the past few years. Here we propose a new hyperheuristic framework within which heuristics compete against one another. The rules for competition are motivated by the principles of reinforcement learning. We analyse the differences between a previously published choice function hyperheuristic and the new hyperheuristic. We demonstrate how the new hyperheuristic can make further improvements when a number of features are incorporated, including a dynamic tabu list which forbids the use of certain heuristics at certain times. The result is an algorithm which is competitive with the choice function hyperheuristic when applied to a comprehensive suite of nurse scheduling problems at a major UK hospital, featuring a wide variety of solution landscapes.