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oai:www.ideals.illinois.edu:2142/83453 2023-07-11 col_2142_5131 col_2142_14770 com_2142_5130 com_2142_3518 com_2142_234

Scott A. Burns Feng, Chung-Wei 2015-09-25T21:05:00Z 2015-09-25T21:05:00Z 10000-01-01 1998 1998 This research investigates the use of GAs (Genetic Algorithms) as the foundation for multi-objective construction process optimization. Based on GA principles, a set of algorithms is developed specifically for construction process analysis and optimization. This set of algorithms is also integrated with simulation techniques to analyze risks and uncertainties while addressing process optimization. Computerized systems based on these algorithms are able to analyze construction processes efficiently and identify the optimal solutions and the associated probabilities for large-scale construction projects. Made available in DSpace on 2015-09-25T21:05:00Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9904453.pdf: 5289205 bytes, checksum: 6ed111c16fc9a012f616948fffd7c8a6 (MD5) Previous issue date: 1998 Liu, Liang Y. Embargo set by: Seth Robbins for item 84734 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 136 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998. http://hdl.handle.net/2142/83453 (MiAaPQ)AAI9904453 eng Computer Science Applications of Genetic Algorithms for Multi-Objective Resource Allocation of Construction Projects Under Certainty text Civil and Environmental Engineering Civil and Environmental Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.