Journal of Artificial Intelligence Research
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In this paper, we propose the Robust Temporal Constraint Network (RTCN) model for simple temporal constraint networks where activity durations are bounded by random variables. The problem is to determine whether such temporal network can be executed with failure probability less than a given 0 ≤ E ≤ 1 for each possible instantiation of the random variables, and if so. how one might find a feasible schedule with each given instantiation. The advantage of our model is that one can vary the value of ∊ to control the level of conservativeness of the solution. We present a computationally tractable and efficient approach to solve these RTCN problems. We study the effects the density of temporal constraint networks have on its makespan under different confidence levels. W e also apply RTCN to solve the stochastic project crashing problem.