Effective IG heuristics for a single-machine scheduling problem with family setups and resource constraints

In this paper we investigate the problem of scheduling a set of jobs on a single-machine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstream processes. The total resource consumed must not exceed the resource supply up. Therefore, jobs may have to wait and the machine has to be idle due to an insufficient availability of the resource. To minimize the total tardiness, simple and effective iterated greedy (IG) heuristics are proposed. Different neighborhood operators are used in the local search phase. To choose the right neighborhood operators, we propose an adaptive selecting strategy. The heuristics are tested over an extensive computational experience on benchmark of instances from the literature and instances randomly generated in this work. Experimental results and statistical tests show that the proposed heuristics are able to obtain high-quality solutions within reasonable computational effort, and they outperform the state-of-the-art heuristic.