This paper introduces a new general framework for genetic algorithms to solve a broad range of optimization problems. When designing a genetic algorithm, there may be several alternatives for a component such as crossover, mutation or decoding procedure, and it may be difficult to determine the...

Resource-constrained project scheduling under a net present value objective attracts growing interest. Because this is an NP-hard problem, it is unlikely that optimum solutions can be computed for large instances. Thus, heuristics have become a popular research field. Up to now, however, tight...

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,...

We consider the multi-mode resource-constrained project scheduling problem. The focus is on an algorithm which is supposed to find a makespan optimal solution. This algorithm has been presented in a recent paper by Speranza and Vercellis. The correctness of the algorithm is examined. Moreover,...