Approximation in stochastic scheduling: the power of LP-based priority policies
Journal of the ACM (JACM)
Approximation Techniques for Average Completion Time Scheduling
SIAM Journal on Computing
Boosted sampling: approximation algorithms for stochastic optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Parallel scheduling of complex dags under uncertainty
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Approximation algorithms for multiprocessor scheduling under uncertainty
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Approximation algorithms for budgeted learning problems
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Optimization of continuous queries with shared expensive filters
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Model-driven optimization using adaptive probes
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Exceeding expectations and clustering uncertain data
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A case for on-machine load balancing
Journal of Parallel and Distributed Computing
Stochastic online scheduling on parallel machines
WAOA'04 Proceedings of the Second international conference on Approximation and Online Algorithms
List scheduling in order of α-points on a single machine
Efficient Approximation and Online Algorithms
Hi-index | 0.00 |
We consider parallel machine scheduling problems where the jobs are subject to precedence constraints, and the processing times of jobs are governed by independent probability distributions. The objective is to minimize the weighted sum of job completion times ∑, w, C, in expectation, where w, ⪈ 0. Building upon an LP-relaxation from [3] and an idle time charging scheme from [1], we derive the first approximation algorithms for this model.