An intelligent crew allocation system for the precast manufacturing systems: Railway sleepers precast concrete as a case study

This paper presents an innovative approach for best allocation of crews of workers on production processes by combining flow-chart based simulation tool with a powerful genetic optimization procedure. The proposed approach determines the least costly and most productive crew allocation on a set of production processes. IDEFO diagrams were developed to provide generic and functional diagrams for each process. Discrete Event Simulation Methodology is used to develop the precast-component manufacturing simulation model. A proposed GA model is developed to be integrated with the developed simulation model. This type of integration will add more intelligence to the simulation model for a better search. GA operators were developed to suit this type of problems. A precast concrete manufacturing system producing sleepers was chosen as a test bed for the proposed SIM-Crew allocation system. SIM-Crew system is easy to use and can be utilised for large scale manufacturing systems. Based on this research, computer simulation and genetic algorithms can be an effective combination and offer a great potential for improving productivity and saving production time and cost. The results showed that adopting different combinations of possible crews of workers had a substantial impact on reducing the total labour cost, process idle time and in maximising the utilisation of skilled workers.