Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17044
Title: Concrete plant operations optimization using combined simulation and genetic algorithms
Authors: Cao, M
Lu, M
Zhang, J
Keywords: Civil engineering computing
Concrete
Construction industry
Discrete event simulation
Genetic algorithms
Production control
Ready-mixed materials
Stochastic processes
Issue Date: 2004
Publisher: IEEE
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 7, p. 4204-4209 How to cite?
Abstract: This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation model construction, conduct simulation experiments on certain scenarios of site orders and resource provisions, and optimize the system performance measures under a stochastic simulation environment. HKCONSIM is suitable for assisting a RMC plant in its resource provision planning and concrete production scheduling in order to meet given demands at a number of sites for concrete over a working day, determine the least costly, most productive amount of truckmixer resources to improve the supply service level and the utilization level of the truckmixer resources available. To simulate and optimize the RMC production operations with HKCONSIM does not require familiarity by the user with any software-specific terminology and modeling schematics; simulation model construction can be easily achieved by specifying the attributes for each pour and site and providing the plant and truck-mixer resources available on self-explanatory on-screen forms. This work also presents two case studies for optimizing concrete plant daily operations based on Hong Kong's real operations data. Conclusions are given on the research and recommendations for future work made.
URI: http://hdl.handle.net/10397/17044
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1384577
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