Abstract
Current precast production scheduling methodologies have limited applicability in practice due to the neglect of real-world production circumstances. To improve, a two-hierarchy simulation-GA hybrid model for precast production (TSGH_PP) is developed to (1) specialize the operations of precast production according to their characteristics, (2) incorporate the uncertainty in processing time in practice, and (3) model the process-waiting time on the flow of work based on the genetic algorithm and discrete event simulation. In the proposed model, the trade-off can be achieved between the conflicting goals of the on-time delivery of precast components and minimum production cost, and the production resources configuration is optimized to cut down resource waste. Finally, a real case study is conducted to test the validity of TSGH_PP approach. The developed model fills the gap in simulation system design and methodology for precast production, and increases the applicability of precast production scheduling methods in real construction projects.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 69-80 |
| Number of pages | 12 |
| Journal | Automation in Construction |
| Volume | 86 |
| DOIs | |
| State | Published - Feb 1 2018 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction
Keywords
- Genetic algorithm
- On-time delivery
- Optimization
- Precast production
- Production costs
- Simulation
Cite this
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Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components. / Wang, Zhaojing; Hu, Hao; Gong, Jie.
In: Automation in Construction, Vol. 86, 01.02.2018, p. 69-80.Research output: Contribution to journal › Article
TY - JOUR
T1 - Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components
AU - Wang, Zhaojing
AU - Hu, Hao
AU - Gong, Jie
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Current precast production scheduling methodologies have limited applicability in practice due to the neglect of real-world production circumstances. To improve, a two-hierarchy simulation-GA hybrid model for precast production (TSGH_PP) is developed to (1) specialize the operations of precast production according to their characteristics, (2) incorporate the uncertainty in processing time in practice, and (3) model the process-waiting time on the flow of work based on the genetic algorithm and discrete event simulation. In the proposed model, the trade-off can be achieved between the conflicting goals of the on-time delivery of precast components and minimum production cost, and the production resources configuration is optimized to cut down resource waste. Finally, a real case study is conducted to test the validity of TSGH_PP approach. The developed model fills the gap in simulation system design and methodology for precast production, and increases the applicability of precast production scheduling methods in real construction projects.
AB - Current precast production scheduling methodologies have limited applicability in practice due to the neglect of real-world production circumstances. To improve, a two-hierarchy simulation-GA hybrid model for precast production (TSGH_PP) is developed to (1) specialize the operations of precast production according to their characteristics, (2) incorporate the uncertainty in processing time in practice, and (3) model the process-waiting time on the flow of work based on the genetic algorithm and discrete event simulation. In the proposed model, the trade-off can be achieved between the conflicting goals of the on-time delivery of precast components and minimum production cost, and the production resources configuration is optimized to cut down resource waste. Finally, a real case study is conducted to test the validity of TSGH_PP approach. The developed model fills the gap in simulation system design and methodology for precast production, and increases the applicability of precast production scheduling methods in real construction projects.
KW - Genetic algorithm
KW - On-time delivery
KW - Optimization
KW - Precast production
KW - Production costs
KW - Simulation
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UR - http://www.scopus.com/inward/citedby.url?scp=85033361962&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2017.10.026
DO - 10.1016/j.autcon.2017.10.026
M3 - Article
AN - SCOPUS:85033361962
VL - 86
SP - 69
EP - 80
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
ER -