Comparison of Heuristics for Identical Parallel Machine Scheduling

This paper addresses the problem of scheduling n independent jobs processed nonpreemtively on m identical parallel machines with the objective of minimizing makespan. Since these scheduling problems are well known to be NP-hard, among various solution methodologies, heuristics are preferred most. They guarantee near-optimal solutions and due to their polynomial time algorithms require reasonable computational effort, especially for solving large problem sizes. We consider three popular heuristics, multifit, combine and listfit since the seminal work of McNaughton in 1959. We present different experimental frameworks to investigate these heuristics for a comprehensive comparative performance evaluation. We show, through computational experimentation, that listfit outperforms the multifit and combine heuristics in most of the problem instances, however, at the cost of increased time complexity. The computational results also reveal that the combine heuristic performs better than the multifit heuristic, while requiring almost similar computational effort.