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Priority rule-based methods (PRBMs) rely on problem-specific knowledge to construct good solutions in a very short time. They can be used as stand-alone procedures or can be integrated into (partial) enumeration procedures, like branch & bound or dynamic programming, and heuristic solution methods. PRBMs are especially important for solving NP-hard optimization problems.

In this study, we provide guidance, how to design PRBMs based on a thorough computational investigation. We conduct our analysis on the example of the NP-hard Simple Assembly Line Balancing Problem (SALBP), on which with small modifications most situations in the planning of assembly lines are based. In our study, we persuasively show that for large instances, unless all the task times are low in comparison to the cycle time, PRBMs are competitive even as a stand-alone solution method. Further, we provide evidence that composite priority rules perform better and more robust than elementary ones. We also give advice on how to incorporate the knowledge on the problem instance’s structure to make PRBMs even more effective and how to find several good-quality solutions. Overall, PRBMs developed in this paper significantly outperform those available in the literature.