An Iterated Local Search Framework with Adaptive Operator Selection for Nurse Rostering

Abstract

Considerable attention has been paid to selective hyper-heuristic frameworks for addressing computationally hard scheduling problems. By using selective hyper-heuristics, we can derive benefits from the strength of low level heuristics and their components at different stages of the heuristic search. In this paper, a simple, general and effective selective hyper heuristic is presented. We introduce an iterated local search based hyper-heuristic framework that incorporates the adaptive operator selection scheme to learn through the search process. The considered iterative approach employs an action selection model to decide the perturbation strategy to apply in each step and a credit assignment module to score its performance. The designed framework allows us to employ any action selection model and credit assignment mechanism used in the literature. Empirical results and an analysis of six different action selection models against state-of-the-art approaches, across 39 problem instances, highlight the significant potential of the proposed selection hyper-heuristics. Further analysis on the adaptive behavior of the model suggests that two of the six models are able to learn the best performing perturbation strategy, resulting in significant performance gains.