A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Using Genetic Algorithms to Improve Crew Allocation Process in Labour-Intensive Industries
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.
Using Genetic Algorithms to Improve Crew Allocation Process in Labour-Intensive Industries
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.
Using Genetic Algorithms to Improve Crew Allocation Process in Labour-Intensive Industries
Dawood, Nashwan (author) / Al-Bazi, Ammar (author)
International Workshop on Computing in Civil Engineering 2009 ; 2009 ; Austin, Texas, United States
Computing in Civil Engineering (2009) ; 166-175
2009-06-19
Conference paper
Electronic Resource
English
Using Genetic Algorithms to Improve Crew Allocation Process in Labour-Intensive Industries
| British Library Conference Proceedings | 2009
Fuzzy optimisation of labour allocation by genetic algorithms
| Online Contents | 2003
Fuzzy optimisation of labour allocation by genetic algorithms
| Emerald Group Publishing | 2003