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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 Creighton
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.

History

Journal

International journal of production research

Volume

44

Issue

6

Pagination

1193 - 1216

Publisher

Taylor & Francis

Location

London, England

ISSN

0020-7543

eISSN

1366-588X

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2006, Taylor & Francis

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