To address this problem a Bayesian network is proposed to model the schedule of successive operations.
They consider only two constraints in their model: the schedule should provide a specific number of personnel for each scheduling period and a nurse can start only one shift per day.
To provide better service after summarizing and analyzing the operational updating model, the schedule synchronization, cyclic timetable, minimal energy consumption, and timetable recovery from the disruption are considered as new aspects for the tactical and operational planning stage.
This work starts from modeling the scheduling of n jobs on m machines/stages as flowshop with buffers in manufacturing.
So the researchers modeled the scheduling of threads as a process that has at least a little randomness in it: At any time, there's some probability that a new thread will be initiated on any given core.
Firstly, by modelling the scheduling problem as an SMDP, the entire scheduling process is divided into a series of stages.
These cases are modeled using the scheduling model that is outlined in Section 2.1, without any changes to the constraints or fixing of variables.
Radojevic et al. [18] introduced central load balancing decision model for use in cloud environments; this model automates the scheduling process and reduces the role of human administrators.
