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Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization
journal contribution
posted on 2006-03-01, 00:00 authored by A Cave, Saeid Nahavandi, Douglas CreightonDouglas CreightonThe Operations Research (OR) community have defined many deterministic manufacturing control problems mainly focused on scheduling. Well-defined benchmark problems provide a mechanism for communication of the effectiveness of different optimization algorithms. Manufacturing problems within industry are stochastic and complex. Common features of these problems include: variable demand, machine part specific breakdown patterns, part machine specific process durations, continuous production, Finished Goods Inventory (FGI) buffers, bottleneck machines and limited production capacity. Discrete Event Simulation (DES) is a commonly used tool for studying manufacturing systems of realistic complexity. There are few reports of detail-rich benchmark problems for use within the simulation optimization community that are as complex as those faced by production managers. This work details an algorithm that can be used to create single and multistage production control problems. The reported software implementation of the algorithm generates text files in eXtensible Markup Language (XML) format that are easily edited and understood as well as being cross-platform compatible. The distribution and acceptance of benchmark problems generated with the algorithm would enable researchers working on simulation and optimization of manufacturing problems to effectively communicate results to benefit the field in general.
History
Journal
International journal of production researchVolume
44Issue
6Pagination
1193 - 1216Publisher
Taylor & FrancisLocation
London, EnglandPublisher DOI
ISSN
0020-7543eISSN
1366-588XLanguage
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2006, Taylor & FrancisUsage metrics
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