The complexity of mean payoff games on graphs
Theoretical Computer Science
Scheduling to minimize average completion time: off-line and on-line approximation algorithms
Mathematics of Operations Research
Approximation in stochastic scheduling: the power of LP-based priority policies
Journal of the ACM (JACM)
Forcing relations for AND/OR precedence constraints
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Developments from a June 1996 seminar on Online algorithms: the state of the art
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
Solving project scheduling problems by maximum cut computation
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Scheduling modular projects on a bottleneck resource
Journal of Scheduling
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Deterministic models for project schedulingan d control suffer from the fact that they assume complete information and neglect random influences that occur duringpr oject execution. A typical consequence is the underestimation of the expected project duration and cost frequently observed in practice. To cope with these phenomena, we consider scheduling models in which processing times are random but precedence and resource constraints are fixed. Schedulingis done by policies which consist of an an online process of decisions that are based on the observed past and the a priori knowledge of the distribution of processing times. We give an informal survey on different classes of policies and show that suitable combinatorial properties of such policies give insights into optimality, computational methods, and their approximation behavior. In particular, we present recent constant-factor approximation algorithms for simple policies in machine scheduling that are based on a suitable polyhedral relaxation of the performance space of policies.