Solving the Response Time Variability Problem by means of Multi-start and GRASP metaheuristics

The Response Time Variability Problem (RTVP) is an NP-hard scheduling optimization problem that has recently appeared in the literature. This problem has a wide range of real-life applications in, for example, manufacturing, hard real-time systems, operating systems and network environments. The RTVP occurs whenever models, clients or jobs need to be sequenced to minimize variability in the time between the instants at which they receive the necessary resources. The RTVP has been already solved in the literature with a multi-start and a GRASP algorithm. We propose an improved multi-start and an improved GRASP algorithm to solve the RTVP. The computational experiment shows that, on average, the results obtained with our proposed algorithms improve on the best obtained results to date.

[1]  M. Resende,et al.  A probabilistic heuristic for a computationally difficult set covering problem , 1989 .

[2]  Carl A. Waldspurger,et al.  Lottery and stride scheduling: flexible proportional-share resource management , 1995 .

[3]  Alberto García-Villoria,et al.  Introducing dynamic diversity into a discrete particle swarm optimization , 2009, Comput. Oper. Res..

[4]  J. Miltenberg,et al.  Level schedules for mixed-model assembly lines in just-in-time production systems , 1989 .

[5]  Albert Corominas,et al.  Solving the Response Time Variability Problem by means of metaheuristics , 2006, CCIA.

[6]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[7]  Celia A. Glass,et al.  The Scheduling of Maintenance Service , 1998, Discret. Appl. Math..

[8]  Jeffrey W. Herrmann,et al.  Generating Cyclic Fair Sequences using Aggregation and Stride Scheduling , 2007 .

[9]  Wieslaw Kubiak,et al.  Response time variability , 2007, J. Sched..

[10]  Rami G. Melhem,et al.  Time slot allocation for real-time messages with negotiable distance constraints , 1998, Proceedings. Fourth IEEE Real-Time Technology and Applications Symposium (Cat. No.98TB100245).

[11]  Rafael Martí.  Multi-Start Methods , 2003, Handbook of Metaheuristics.

[12]  Manuel Laguna,et al.  Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search , 2006, Oper. Res..

[13]  Alexander H. G. Rinnooy Kan,et al.  A stochastic method for global optimization , 1982, Math. Program..