Framework for modeling operational uncertainty to optimize offsite production scheduling of precast components

Zhaojing Wang, Hao Hu, Jie Gong

Research output: Contribution to journalArticle

17 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)69-80
Number of pages12
JournalAutomation in Construction
Volume86
DOIs
StatePublished - Feb 1 2018

Fingerprint

Scheduling
Uncertainty
Discrete event simulation
Genetic algorithms
Systems analysis
Processing
Costs

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 journalArticle

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