Optimisation of Maintenance Scheduling Strategies on the Grid

The emerging paradigm of grid computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introduces a grid-based optimisation framework that provides a powerful tool for the optimisation of such computationally intensive objective functions. This framework is then used in the optimisation of maintenance scheduling strategies for fleets of aero-engines, a computationally intensive problem with a high-degree of stochastic noise

[1]  Marian Bubak,et al.  Advances in Grid Computing - EGC 2005, European Grid Conference, Amsterdam, The Netherlands, February 14-16, 2005, Revised Selected Papers , 2005, EGC.

[2]  Leonard Kleinrock,et al.  Queueing Systems: Volume I-Theory , 1975 .

[3]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[4]  Alex Shenfield.  Grid enabled optimisation using evolutionary algorithms , 2008 .

[5]  Karl Sims,et al.  Artificial evolution for computer graphics , 1991, SIGGRAPH.

[6]  Paul Bryant Grosso,et al.  Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model , 1985 .

[7]  Runhe Huang,et al.  Implementing the Genetic Algorithm on Transputer Based Parallel Processing Systems , 1990, PPSN.

[8]  Pierre Borne,et al.  EVOLUTIONARY ALGORITHMS FOR JOB-SHOP SCHEDULING , 2004 .

[9]  David A. Chappell,et al.  Java Web Services , 2002 .

[10]  Kit Po Wong,et al.  A Java-based parallel platform for the implementation of evolutionary computation for engineering applications , 2004, Int. J. Syst. Sci..

[11]  William B. Langdon,et al.  Scheduling Planned Maintenance of the National Grid , 1995, Evolutionary Computing, AISB Workshop.

[12]  Eduardo Huedo,et al.  A Grid-Oriented Genetic Algorithm , 2005, EGC.

[13]  John Crocker,et al.  Age-related maintenance versus reliability centred maintenance: a case study on aero-engines , 2000, Reliab. Eng. Syst. Saf..

[14]  Reiko Tanese,et al.  Parallel Genetic Algorithms for a Hypercube , 1987, ICGA.

[15]  Mikkel T. Jensen,et al.  Generating robust and flexible job shop schedules using genetic algorithms , 2003, IEEE Trans. Evol. Comput..

[16]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..

[17]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[18]  D. Hamby.  A review of techniques for parameter sensitivity analysis of environmental models , 1994, Environmental monitoring and assessment.

[19]  Rajkumar Buyya,et al.  Grids and Grid technologies for wide‐area distributed computing , 2002, Softw. Pract. Exp..

[20]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[21]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[22]  L. Darrell Whitley,et al.  Optimization Using Distributed Genetic Algorithms , 1990, PPSN.

[23]  Tomoyuki Hiroyasu,et al.  The System for Evolutionary Computing on the Computational Grid , 2002, IASTED PDCS.

[24]  Tomàs Margalef,et al.  Evolutionary Optimization Techniques on Computational Grids , 2002, International Conference on Computational Science.

[25]  Peter J. B. Hancock,et al.  An Empirical Comparison of Selection Methods in Evolutionary Algorithms , 1994, Evolutionary Computing, AISB Workshop.

[26]  Simon J. Cox,et al.  Numerical Optimisation as Grid Services for Engineering Design , 2004, Journal of Grid Computing.

[27]  Ian Foster,et al.  The Globus toolkit , 1998 .

[28]  Kalyanmoy Deb,et al.  A genetic-fuzzy approach for mobile robot navigation among moving obstacles , 1999, Int. J. Approx. Reason..

[29]  David B. Fogel,et al.  A note on representations and variation operators , 1997, IEEE Trans. Evol. Comput..

[30]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[31]  Ian T. Foster,et al.  Grid Services for Distributed System Integration , 2002, Computer.

[32]  Francine Berman,et al.  Application-Level Scheduling on Distributed Heterogeneous Networks , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[33]  Bu-Sung Lee,et al.  A service-oriented approach for aerodynamic shape optimisation across institutional boundaries , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[34]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[35]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[36]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[37]  David M. Booth,et al.  Web Services Architecture , 2004 .

[38]  Ben Paechter,et al.  Application of the Grouping Genetic Algorithm to University Course Timetabling , 2005, EvoCOP.

[39]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[40]  Leonardo Vanneschi,et al.  An Empirical Study of Multipopulation Genetic Programming , 2003, Genetic Programming and Evolvable Machines.

[41]  Wilson Rivera,et al.  Scalable Parallel Genetic Algorithms , 2001, Artificial Intelligence Review.

[42]  David E. Goldberg,et al.  On the Scalability of Parallel Genetic Algorithms , 1999, Evolutionary Computation.

[43]  Arthur Tay,et al.  Design and implementation of a distributed evolutionary computing software , 2003, IEEE Trans. Syst. Man Cybern. Part C.

[44]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..