Estimating the probability of meeting a deadline in schedules and plans

Cohen, Liron; Shimony, Solomon Eyal; Weiss, Gera

doi:10.1016/j.artint.2019.06.009

Cited by 8 publications

(29 citation statements)

References 30 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most relevant work related to this paper is the papers on approximations of random variables in the context of estimating deadlines (Cohen, Shimony, and Weiss 2015;Cohen, Grinshpoun, and Weiss 2018). In these papers, X is defined to be a good approximation of X if F X (t) > F X (t) for any t and sup t F X (t) − F X (t) is small.…”

Section: Related Workmentioning

confidence: 99%

“…All algorithms were implemented in Python and the experiments were executed on a hardware comprised of an Intel i5-6500 CPU @ 3.20GHz processor and 8GB memory. The algorithms of Cohen et al were taken "as is" from in the supplementary material to (Cohen, Shimony, and Weiss 2015).…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Repetitive support size minimization. One use of support size minimization is when commutations that involve summations of random variables slow due to an exponential growth in the support of convolutions of random variables (Cohen, Shimony, and Weiss 2015). A key action in coping with this situation is reduction of the support size by replacing the summed random variable by an approximation of it that has a smaller support size.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Various approaches for approximation of probability distributions are studied in the literature (Pettitt and Stephens 1977;Miller and Rice 1983;Vidyasagar 2012;Cohen, Shimony, and Weiss 2015;Pavlikov and Uryasev 2016;Cohen, Grinshpoun, and Weiss 2018). These approaches vary in the types random variables considered, how they are represented, and in the criteria used for evaluation of the quality of the approximations.…”

Section: Introductionmentioning

confidence: 99%

“…Our main motivation for this work is estimation of the probability for missing deadlines, as described, e.g., in Cohen et al (Cohen, Shimony, and Weiss 2015;Cohen, Grinshpoun, and Weiss 2018) and in (Kashef and Moayeri 2018). Specifically, when X represents the probability distribution of the time to complete some complex schedule and we cannot afford to maintain the full table of its probability mass function, we propose an algorithm for producing a smaller table, whose size can be specified, such that probabilities for missing deadlines are preserved as much as possible.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Optimal Approximation of Discrete Random Variables for Estimation of Probabilities of Missing Deadlines

Cohen

Weiss

2019

AAAI

Self Cite

View full text Add to dashboard Cite

We present an efficient algorithm that, given a discrete random variable X and a number m, computes a random variable whose support is of size at most m and whose Kolmogorov distance from X is minimal. We present some variants of the algorithm, analyse their correctness and computational complexity, and present a detailed empirical evaluation that shows how they performs in practice. The main application that we examine, which is our motivation for this work, is estimation of the probability of missing deadlines in series-parallel schedules. Since exact computation of these probabilities is NP-hard, we propose to use the algorithms described in this paper to obtain an approximation.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%