Simulation‐based genetic algorithms for construction supply chain management: Off‐site precast concrete production as a case study

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 precast production operations using multi‐layered genetic algorithms (GAs). The multi‐layered concept emerged in response to the requirement of modelling different sets of labour inputs. As part of the techniques used in developing a crew allocation ‘SIM–Crew’ system, a process mapping methodology is used to model 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 GAs 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. 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 reduced throughput and process‐waiting times and improved resource utilisation profiles can be achieved when compared with a real‐life case study.