Constructing ensembles of dispatching rules for multi-objective tasks in the unrelated machines environment

Scheduling is a frequently studied combinatorial optimisation problem that often needs to be solved under dynamic conditions and to optimise multiple criteria. The most commonly used method for solving dynamic problems are dispatching rules (DRs), simple constructive heuristics that build the schedule incrementally. Since it is difficult to design DRs manually, they are often created automatically using genetic programming. Although such rules work well, their performance is still limited and various methods, especially ensemble learning, are used to improve them. So far, ensembles have only been used in the context of single-objective scheduling problems. This study aims to investigate the possibility of constructing ensembles of DRs for solving multi-objective (MO) scheduling problems. To this end, an existing ensemble construction method called SEC is adapted by extending it with non-dominated sorting to construct Pareto fronts of ensembles for a given MO problem. In addition, the algorithms NSGA-II and NSGA-III were adapted to construct ensembles and compared with the SEC method to demonstrate their effectiveness. All methods were evaluated on four MO problems with different number of criteria to be optimised. The results show that ensembles of DRs achieve better Pareto fronts compared to individual DRs. Moreover, the results show that SEC achieves equally good or even slightly better results than NSGA-II and NSGA-III when constructing ensembles, while it is simpler and slightly less computationally expensive. This shows the potential of using ensembles to increase the performance of individual DRs for MO problems.

Automated summary

This study investigates the possibility of constructing ensembles of dispatching rules (DRs) to solve multi-objective (MO) scheduling problems. An existing ensemble construction method called SEC is adapted and compared with the NSGA-II and NSGA-III algorithms. The results show that ensembles of DRs achieve better Pareto fronts compared to individual DRs, and SEC achieves equally good or slightly better results than NSGA-II and NSGA-III when constructing ensembles, while being simpler and slightly less computationally expensive.