Simulation Based Analysis of Stochastic Online Machine Scheduling Problems
نویسندگان
چکیده
In the stochastic online scheduling environment, jobs with unknown release times and weights arrive over time. Upon arrival, the information on the weight of the job is revealed but the processing requirement remains unknown until the job is finished. With the objective of minimizing the total weighted completion time, various asymptotically optimal algorithms have been proposed for the online single machine problem, uniform parallel machine problem and flow shop problem. We perform extensive and sophisticated simulation studies on these stochastic online scheduling problems and show that two generic nondelay algorithms converge very fast. Our simulation results suggest that some known asymptotic optimality results may be extendable to the stochastic online open shop and job shop problems. The simulation results also suggest that, compared with the total weighted completion time metric, the total weighted flow time metric and total weighted stretch metric are more sensitive and may be better performance measures.
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