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We also obtain the distributions of blocking times and first failure times, respectively.
In this paper, we consider a three-stage AFSP with blocking times and sequence dependent setup times.
To make this type of assembly flowshop more realistic our research added the blocking times limitation (buffer = 0) to the model presented in [5].
In research conducted through combined analytical simulation approach, Medeossi et al. [59] applied stochastic approach on blocking times of trains to improve the timetable planning using OpenTrack simulation software.
For this purpose, several factors have to be taken into account, such as train running times, blocking times and minimum headway between two successive convoys, dwell times at stations and possible buffer times.
From [3], we know (AF (m, 1, 1)//) is an NP-hard problem, so by adding sequence-dependent setup time and blocking times to this model it is strongly NP-hard too.
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Blocking Time with Varying k To the aspect of blocking time in Figs. 8 and 9, the blocking time reduces with k in three approaches because the number of resulting blocks (n/k) becomes less as k gets bigger.
When a candidate task starts to run, lines 1 and 2 recycle the free time between the maximal blocking time with the practical blocking time.
Blocking Time with Varying Database Sizes In Fig. 10, we compare the blocking time for three approaches with different dataset sizes.
However, this model is not correct and the blocking time is confusing.
Two methods are proposed for PICARD to reduce the maximum blocking time.
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