Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization

Cave, Alexander Paul, Nahavandi, Saeid and Creighton, Douglas 2006, Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization, International journal of production research, vol. 44, no. 6, pp. 1193-1216, doi: 10.1080/00207540500362286.

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Title Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization
Author(s) Cave, Alexander Paul
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Creighton, DouglasORCID iD for Creighton, Douglas
Journal name International journal of production research
Volume number 44
Issue number 6
Start page 1193
End page 1216
Publisher Taylor & Francis
Place of publication London, England
Publication date 2006-03
ISSN 0020-7543
Keyword(s) benchmark
stochastic systems
assembly systems
Summary The 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.
Language eng
DOI 10.1080/00207540500362286
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Taylor & Francis
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