Using Genetic Algorithms to Improve Crew Allocation Process in Labour-Intensive Industries

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Abstract

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.
Original languageEnglish
Title of host publicationComputing in Civil Engineering
EditorsCarlos H. Caldas, William J. O'Brien
PublisherAmerican Society of Civil Engineers
Pages166-175
ISBN (Print)978-0-7844-1052-3
DOIs
Publication statusPublished - 2009

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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