Generating Cyclic Fair Sequences for Multiple Servers. Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10-12 Aug 2009, Dublin, Ireland, pages 628-644, 2009.
Paper
Fair sequences allocate capacity to competing demands in a variety of manufacturing and computer systems. This paper considers the generation of cyclic fair sequences for a set of servers that must process a given set of products, each of which must be produced multiple times in each cycle. The objective is to create a sequence that minimizes, for each product, the variability of the time between consecutive completions (the response time). Because minimizing response time variability is known to be NP-hard and the performance of existing heuristics is poor for certain classes of problems, we present an aggregation approach that combines products with the same demand into groups, creates a sequence for those groups, and then disaggregates the sequence into a sequence for each product. Computational experiments show that using aggregation can reduce computational effort and response time variability dramatically.
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@INPROCEEDINGS{2009-628-644-P, author = {J. W. Herrmann},
title = {Generating Cyclic Fair Sequences for Multiple Servers},
booktitle = {Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10-12 Aug 2009, Dublin, Ireland},
year = {2009},
editor = {J. Blazewicz and M. Drozdowski and G. Kendall and B. McCollum},
pages = {628--644},
note = {Paper},
abstract = {Fair sequences allocate capacity to competing demands in a variety of manufacturing and computer systems. This paper considers the generation of cyclic fair sequences for a set of servers that must process a given set of products, each of which must be produced multiple times in each cycle. The objective is to create a sequence that minimizes, for each product, the variability of the time between consecutive completions (the response time). Because minimizing response time variability is known to be NP-hard and the performance of existing heuristics is poor for certain classes of problems, we present an aggregation approach that combines products with the same demand into groups, creates a sequence for those groups, and then disaggregates the sequence into a sequence for each product. Computational experiments show that using aggregation can reduce computational effort and response time variability dramatically.},
owner = {gxk},
timestamp = {2010.10.11},
webpdf = {2009-628-644-P.pdf} }