Development of 3D-Simulation Based Genetic Algorithms to Solve Combinatorial Crew Allocation Problems

Ammar Al Bazi, Nashwan Dawood, John Dean

Research output: Contribution to conferencePaperpeer-review


This paper presents an innovative approach to solve combinatorial crew allocation problems in any labour-intensive industry. This possibly can be achieved by combining 3D-simulation technology with Genetic Algorithm (GA). GA is one of the Artificial Intelligent tools that were successfully used in optimising performance of simulation models. The integrated system can determine the least costly and most productive crews to be assigned to
production processes. Discrete Event Simulation (DES) methodology is used to develop a 3D-simulation model that conveys the idea of how a labour-driven manufacturing system works. A proposed GA-based Multi Layers chromosome is developed to be integrated within the developed simulation model. This type of integration can optimise performance of the developed simulation model, through guiding it toward better solutions. The concept of Multi-Layers chromosome is proposed in order to store different sets of labour inputs such as (daytime shift crew, night shift crew, process priority, etc). GA operators are developed to ensure more random search for promising solutions in a large solution space. As a case study, a sleeper precast concrete manufacturing system is chosen to prove the concept of the proposed system. The results showed that adopting different allocation plans had a substantial impact on reducing total allocation cost, process-waiting time, and optimising resource utilisation. In addition, worker utilisation and process-waiting time have a significant effect on the labour allocation cost.
Original languageEnglish
Publication statusPublished - 2009
EventInternational Conference on Construction Applications of Virtual Reality -
Duration: 5 Nov 20096 Nov 2009
Conference number: 9


ConferenceInternational Conference on Construction Applications of Virtual Reality


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