The high cost of skilled workers in labour-intensive production industries has motivated senior production managers to identify the best allocation strategy of crews of workers to appropriate processes. The aim of this paper is to develop a crew allocation system using Genetic Algorithms-based simulation modeling. The objective is to optimally allocate crews of workers to labour-intensive production industries to minimise labour costs. In this paper, a simulation-based Genetic Algorithm (GA) system dubbed "SIM_Crew" is developed to simulate the physical processes of a labour-driven facility. The GA is tailored to be embedded with the developed simulation model for improved solution searching. A chromosome structure is designed to apply such problems and a probabilistic selection of promising chromosomes is applied as a selection strategy, n-points crossover and mutation strategies are designed to add more randomness to the searching process. A case study in the precast industry is presented to demonstrate and validate the model.
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Dawood, N., & Al-Bazi, A. F. (2009). Using Genetic Algorithms to Improve Crew Allocation Process in Labour-Intensive Industries. In C. H. Caldas, & W. J. O'Brien (Eds.), Computing in Civil Engineering (pp. 166-175). American Society of Civil Engineers. https://doi.org/10.1061/41052(346)17