For most computationally intractable problems there exists no heuristic that is equally effective on all instances. Rather, any given heuristic may do well on some instances but will do worse on others. Indeed, even the 'best' heuristics will be dominated by others on at least some subclasses of...

It is well-known that for many project scheduling problems the Space AS of active schedules contains at least one optimal solution for each feasible instance, so restricting heuristic construction methods to AS will improve algorithmic efficiency without foresaking the chance to eventually find...

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule-based methods still constitute the most important class of these heuristics. Of these, in turn, parameterized biased random sampling methods have attracted...

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule-based methods still constitute the most important class of these heuristics. Of these, in turn, parameterized biased random sampling methods have attracted...

For most computationally intractable problems there exists no heuristic which performs best on all instances. Usually, a heuristic characterized as best will perform good on the majority of instances but leave a minority on which other heuristics do better. In priority rule-based scheduling,...

NP-completeness and other complexity proofs often merely State that the problem at hand is a generalization of some other intractable problem. This proof technique relies on the widely accepted assumption that complexity results hold regardless of the model formulation used to represent the...