The high cost of skilled labour in the precast concrete industry and the dynamic nature of the production processes encouraged senior managers in the industry to develop more intelligent and optimal labour allocationstrategies. In this paper, a Genetic Algorithm (GA)-based simulation optimisation approach is used for optimalallocation of different crews of workers based on different precast concrete production processes. A processsimulation model is integrated to a GA-based optimisation model in order to simulate the physical processes that are involved in a labour-driven facility and to optimise the allocation of labour crews in that facility. Theoutcome of the proposed approach determines the optimal or near optimal allocation of crews to labour-intensive processes. This should eventually lead to maximum utilisation of a set of skilled workers involved in the allocated crew and subsequently minimise total labour costs. The paper discusses a simulation system dubbed “SIM_Crew” developed during the study. The simulation model is developed initially as a test bench for the proposed allocation system. A GA is used to guide the simulation towards the best course of action; with the chromosome designed to consider all of the decision variables. A probabilistic selection procedure has been developed in order to guarantee various selections of chromosomes. A sleeper precast concrete is developed as a case study to prove the proposed allocation concept. The results showed that efficient utilisation of skilled labour has a substantial impact on reducing throughput time, minimising labour costs, idle times and maximising the skilled workers utilisation.
|Publication status||Published - 2009|
|Event||Flexible Automation and Intelligent Manufacturing - Teesside University , Middlesbrough , United Kingdom|
Duration: 6 Jul 2009 → 8 Jul 2009
|Conference||Flexible Automation and Intelligent Manufacturing|
|Abbreviated title||FAIM 2009|
|Period||6/07/09 → 8/07/09|
Dawood, N., & Al Bazi, A. (2009). Solving Complex Crew Allocation Problems in Labour-Intensive Industries Using Genetic Algorithms. Paper presented at Flexible Automation and Intelligent Manufacturing, Middlesbrough , United Kingdom.