Abstract
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 and bound or dynamic programming, and heuristic solution methods. PRBMs are especially important for solving NP-hard optimization problems.In this paper, we formulate general design principles on how to construct good-performing PRBMs, based on a thorough computational investigation. Our principles allow to construct effective PRBMs already ad hoc, i.e. without time-consuming data mining algorithms. 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. We also provide a cross-validation of our results and illustrate the application of the formulated principles.
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