Scheduling under uncertainty: bounding the makespan distribution

Authors:
Rolf H. Möhring
Affiliations:
Technische Univ. Berlin, Berlin, Germany
Venue:
Computational Discrete Mathematics
Year:
2001

Citing 3
Cited 5

Optimal reduction of two-terminal directed acyclic graphs

SIAM Journal on Computing
Linear-time computability of combinatorial problems on series-parallel graphs

Journal of the ACM (JACM)
Measuring the Distance to Series-Parallelity by Path Expressions

WG '94 Proceedings of the 20th International Workshop on Graph-Theoretic Concepts in Computer Science

Decision Support and Optimization in Shutdown and Turnaround Scheduling

INFORMS Journal on Computing
On the Complexity of Nonoverlapping Multivariate Marginal Bounds for Probabilistic Combinatorial Optimization Problems

Operations Research
Robust local search for solving RCPSP/max with durational uncertainty

Journal of Artificial Intelligence Research
Decision Support and Optimization in Shutdown and Turnaround Scheduling

INFORMS Journal on Computing
Parallelism and concurrency of stochastic graph transformations

ICGT'12 Proceedings of the 6th international conference on Graph Transformations

Quantified Score

Hi-index	0.00

Visualization

Abstract

Deterministic models for project scheduling and control suffer from the fact that they assume complete information and neglect random influences that occur during project execution. A typical consequence is the underestimation of the expected project duration and cost frequently observed in practice. This phenomenon occurs even in the absence of resource constraints, and has been the subject of extensive research in discrete mathematics and operations research. This article presents a survey on the reasons for this phenomenon, its complexity, and on methods how to obtain more relevant information. To this end, we consider scheduling models with fixed precedence constraints, but (independent) random processing times. The objective then is to obtain information about the distribution of the project makespan. We will demonstrate that this is an #P-complete problem in general, and then consider several combinatorial methods to obtain approximate information about the makespan distribution.