## Abstract

Given a plan (or schedule) with uncertain task times, we propose a deterministic polynomial (time and memory) algorithm for estimating the probability that it meets a deadline, or, equivalently, that its makespan is less than a given duration. Approximation is needed as it is known that this problem is NP-hard even for sequential plans (sum of random variables). In addition, we show two new complexity results: (1) Counting the number of events that do not cross the deadline is #P-hard; (2) Computing the expected makespan of a hierarchical plan is NP-hard. For the proposed approximation algorithm, we establish formal approximation bounds and show that the time and memory complexities grow polynomially with the required accuracy, the number of nodes in the plan, and with the size of the support of the random variables that represent the durations of the primitive tasks. We examine these approximation bounds empirically and demonstrate, using task networks taken from the literature, how our scheme outperforms sampling techniques and exact computation in terms of accuracy and run-time. As the empirical data shows much better error bounds than guaranteed, we also suggest a method for tightening the bounds in some cases.

Original language | English |
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Pages (from-to) | 329-355 |

Number of pages | 27 |

Journal | Artificial Intelligence |

Volume | 275 |

DOIs | |

State | Published - 1 Oct 2019 |

## Keywords

- Approximation
- Deadline
- Makespan
- Plan
- Random variables
- Schedule

## ASJC Scopus subject areas

- Language and Linguistics
- Linguistics and Language
- Artificial Intelligence