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...

In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on a precedence feasible sequence...

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,...

Based upon an empirical survey among the 200 biggest CPA firms in Germany an hierarchical modeling framework for audit-staff scheduling with three levels has been developed. For the second level, the so-called medium-to-short-term planning, a binary optimization model is introduced which is...

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,...

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...