Learning ensembles of priority rules for online scheduling by hybrid evolutionary algorithms

Abstract

This paper studies the computation of ensembles of priority rules for the One Machine Scheduling Problem with variable capacity and total tardiness minimization. Concretely, we address the problem of building optimal ensembles of priority rules, starting from a pool of rules evolved by a Genetic Programming approach. Building on earlier work, we propose a number of new algorithms. These include an iterated greedy search method, a local search algorithm and a memetic algorithm. Experimental results show the potential of the proposed approaches.