In this paper we consider a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Jobs arrive in an online manner and as soon as a job becomes known, the scheduler only learns about the probability distribution of the processing time and not the actual processing time. This model is called the stochastic online scheduling (sos) model. Both online scheduling and stochastic scheduling are special cases of this model. In this paper, we survey the results for the sos model
A stochastic, multi-objective job shop production scheduling model is developed in this research. Th...
We present first constant performance guarantees for preemptive stochastic scheduling to minimize th...
A stochastic, multi-objective job shop production scheduling model is developed in this research. Th...
In this paper we consider a model for scheduling under uncertainty. In this model, we combine the ma...
In this paper we consider a model for scheduling under uncertainty. Inthis model, we combine the mai...
We consider a model for scheduling under, uncertainty. In this model, we combine the main characteri...
We introduce a model for non-preemptive scheduling under uncertainty. In this model, we combine the ...
Abstract. We consider a non-preemptive, stochastic parallel machine scheduling model with the goal t...
We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize...
We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize...
This paper considers online stochastic scheduling problems where time constraints severely limit th...
We derive the first performance guarantees for a combinatorial online algorithm that schedules stoch...
We derive the first performance guarantees for a combinatorial online algorithm that schedules stoch...
The characteristic of online algorithms is that the input is not given at once but it is revealed st...
We consider the stochastic identical parallel machine scheduling problem and its online extension, w...
A stochastic, multi-objective job shop production scheduling model is developed in this research. Th...
We present first constant performance guarantees for preemptive stochastic scheduling to minimize th...
A stochastic, multi-objective job shop production scheduling model is developed in this research. Th...
In this paper we consider a model for scheduling under uncertainty. In this model, we combine the ma...
In this paper we consider a model for scheduling under uncertainty. Inthis model, we combine the mai...
We consider a model for scheduling under, uncertainty. In this model, we combine the main characteri...
We introduce a model for non-preemptive scheduling under uncertainty. In this model, we combine the ...
Abstract. We consider a non-preemptive, stochastic parallel machine scheduling model with the goal t...
We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize...
We consider a non-preemptive, stochastic parallel machine scheduling model with the goal to minimize...
This paper considers online stochastic scheduling problems where time constraints severely limit th...
We derive the first performance guarantees for a combinatorial online algorithm that schedules stoch...
We derive the first performance guarantees for a combinatorial online algorithm that schedules stoch...
The characteristic of online algorithms is that the input is not given at once but it is revealed st...
We consider the stochastic identical parallel machine scheduling problem and its online extension, w...
A stochastic, multi-objective job shop production scheduling model is developed in this research. Th...
We present first constant performance guarantees for preemptive stochastic scheduling to minimize th...
A stochastic, multi-objective job shop production scheduling model is developed in this research. Th...