Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-10T21:07:46.464Z Has data issue: false hasContentIssue false
  • English
  • Français

Robustness of cyclic schedules for the charging of batteries

Published online by Cambridge University Press:  17 February 2009

Simon Dunstall  and
Graham Mills
Simon Dunstall
Affiliation:
CSIRO Mathematical and Information Sciences, Private Bag 33, Clayton South 3169, Australia; e-mail: Simon.Dunstall@csiro.au.
Graham Mills
Affiliation:
CSIRO Mathematical and Information Sciences, Private Bag 2, Glen Osmond 5064, Australia; e-mail: Graham.Mills@bigpond.com.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In 2002 the Mathematics in Industry Study Group (MISG) investigated the question of optimally scheduling cyclic production in a battery charging and finishing facility. The facility produces various types of battery and the scheduling objective is to maximize battery throughout subject to achieving a pre-specified product-mix. In this paper we investigate the robustness of such schedules using simulation experiments that span multiple production cycles. We simulate random variations (delays) in battery charging time and find that an optimal off-line schedule yields higher throughput in comparison to a common on-line dispatching rule. This result has been found to hold for a range of expected charging-time delays and has significant practical implications for scheduling battery charging and finishing facilities.

Keywords

schedulingsimulationrobustness
Type
Research Article
Information
The ANZIAM Journal , Volume 48 , Issue 4 , April 2007 , pp. 475 - 492
Copyright
Copyright © Australian Mathematical Society 2007

References

[1] Arena Software, http://www.arenasimulation.com, 2004.Google Scholar
[2]Dunstall, S. and Mills, R. G. J., “Scheduling the charging of batteries”, in Proc. Mathematics-in-Industry Study Group, (2002), 87–105.Google Scholar
[3]Gan, H. S., “Robust scheduling”, Ph. D. Thesis, University of Melbourne, Australia, 2004.Google Scholar
[4]Honkomp, S. J., Mockus, L. and Reklaitis, G. V., “A framework for schedule evaluation with processing uncertainty”, Computers Chem. Eng. 23 (1999) 595–609.CrossRefGoogle Scholar
[5]Lawler, E. L., Lenstra, J. K., Rinnooy Kan, A. H. G. and Shmoys, D. B., “Sequencing and scheduling: algorithms and complexity”, in Handbooks in Operations Research and Management Science (eds. Graves, S. C., Rinnooy Kan, A. H. G. and Zipkin, P. H.), Volume 4, (Elsevier, 1993).Google Scholar
[6]Liu, K. C., “Dispatching rules for stochastic finite capacity scheduling”, Computers Ind. Engng 35 (1995) 113–116.CrossRefGoogle Scholar
[7]Mignon, D. J., Honkomp, S. J. and Reklaitis, G. V., “A framework for investigating schedule robustness under uncertainty”, Computers Chem. Eng. 19 Suppl. (1995) S615–S620.CrossRefGoogle Scholar
[8]Panton, D. M. and Beaumont, N., “Optimisation of work flow”, in Proc. Mathematics-in-Industry Study Group, (1997), 31–51.Google Scholar
[9]Turnquist, A. M., University/industry cooperation on an integrated production planning software system, (Institute of Electrical and Electronic Engineers, 1991) 456–459.Google Scholar
[10]Vieira, G. E., Herrmann, J. W. and Lin, E., “Rescheduling manufacturing systems: a framework of strategies, policies and methods”, J. Scheduling 6 (2003) 39–62.Google Scholar