A Multi-Layer Genetic Algorithm for Solving Crew Allocation Problem in the Precast Industry

The increasing complexity of solving crew allocation problem in a number of manufacturing systems that involve specialised labour-driven processes is resulted by the explosion of a very large number of crew combinations. The aim of this study is to develop an innovative crew allocation system that can efficiently allocate possible crews of workers to precast concrete labour-intensive processes in order reduce the allocation cost and achieve a better flow of work. This has been achieved by combining simulation with a Genetic Algorithm (GA). The integrated system dubbed ‘SIM_Crew’ determines the least costly and most productive crews to be allocated on any production process. Discrete Event Simulation methodology is used to simulate a manufacturing system. A multi-layered chromosome is proposed to store different sets of inputs such as crews working on different shifts, process priorities. GA operators are developed to suit such a chromosome structure. As a case study, sleeper precast manufacturing system is chosen to prove the concept of the developed system. The results showed that proper allocation of combination of crews of workers to production processes should lead to lower resource allocation cost.