SUBMITTED: December 2009 
REVISED: May 2010 
PUBLISHED: July 2010 
EDITOR: Ruikar K

Ammar Frederick Al-Bazi, Ph.D. Student 
Centre for Construction Innovation Research (CCIR), School of Science and Technology (SST),
University of Teesside, Middlesbrough, TS1 3BA, UK, 
a.f.albazi@tees.ac.uk and http://tees.academia.edu/AmmarAlBazi

Nashwan Dawood, Professor
Centre for Construction Innovation Research (CCIR), School of Science and Technology (SST),
University of Teesside, Middlesbrough, TS1 3BA, UK, 
n.n.dawood@tees.ac.uk and http://tees.academia.edu/NashwanDawood

Dr John T Dean
Centre for Construction Innovation Research (CCIR), School of Science and Technology (SST),
University of Teesside, Middlesbrough, TS1 3BA, UK, 
j.dean@tees.ac.uk

SUMMARY:The increased use of precast components in building and heavy civil engineering projects has led to the introduction of innovative management and scheduling systems to meet the demand for increased reliability, efficiency and cost reduction. The aim of this study is to develop an innovative crew allocation system that can efficiently allocate crews of workers to labour-intensive repetitive processes. The objective is to improve off-site pre-cast production operations using Multi-Layered Genetic Algorithms. The Multi-Layered concept emerged in response to the modelling requirements of different sets of labour inputs. As part of the techniques used in developing the Crew Allocation “SIM_Crew” System, a process mapping methodology is used to model the processes of precast concrete operations and to provide the framework and input required for simulation. Process simulation is then used to model and imitate all production processes, and Genetic Algorithms are embedded within the simulation model to provide a rapid and intelligent search. A Multi-Layered chromosome is used to store different sets of inputs such as crews working on different shifts and process priorities. A ‘Class Interval’ selection strategy is developed to improve the chance of selecting the most promising chromosomes for further investigation. Multi-Layered Dynamic crossover and mutation operators are developed to increase the randomness of the searching mechanism for solutions in the solution space. The results illustrate that adopting different combinations of crews of workers has a substantial impact on the labour allocation cost and this should lead to increased efficiency and lower production cost. In addition, the results of the simulation show that minimum throughput time, minimum process-waiting time and optimal resource utilisation profiles can be achieved when compared to a real-life case study.

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