Name
Abstract | ||
|---|---|---|
Proactive scheduling can effectively handle activity duration uncertainty in real-world projects, by generating a baseline solution according to a prior stochastic knowledge. However, most of the previous approaches cannot deal with the activity duration uncertainty caused by time-dependent workability uncertainty. In this paper, we aim at finding a partial-order schedule (POS) that produces the minimum expected makespan on a given probability model of workability uncertainty. Since this is a hard discrete stochastic optimization problem, we propose an approximation approach based on Sample Average Approximation (SAA), and develop a branch-and-bound algorithm to optimally solve the SAA problem. Empirical results on benchmark problem instances and real-world distribution data show that our approach outperforms the best general-purpose POS generation approaches that do not exploit the stochastic knowledge. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5555/3091125.3091161 | AAMAS |
Keywords | Field | DocType |
Proactive project scheduling,time-dependent duration uncertainty,sample average approximation,resource allocation | Sample average approximation,Stochastic optimization,Schedule (project management),Mathematical optimization,Probability model,Job shop scheduling,Scheduling (computing),Computer science,Exploit,Resource allocation,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Song | 1 | 4 | 5.13 |
| Donghun Kang | 2 | 6 | 3.21 |
| Jie Zhang | 3 | 1995 | 156.26 |
| Hui Xi | 4 | 12 | 4.85 |