Development of simulation-based genetic algorithms model for crew allocation in the precast industry

  • Ammar. F. J. Al-Bazi

Student thesis: Doctoral Thesis


The focus of this thesis is on the precast concrete products manufacturing industry, which as one of the labour-intensive industries requires a substantial number of highly skilled operators in terms of crews to produce the final product. A crew is a group of multi-skilled chargehands and operators that have various skills and experience necessary to conduct an activity in a professional way. The high cost of skilled operators and the apparent inefficiencies of utilising such skilled operators in the industry are the major driving force. To achieve this, optimal crew allocation is required. Crew allocation is complex because of the multi-criteria nature of the problem and availability of thousands of possibilities and allocation alternatives. There is a gap in previous research efforts associated with crew allocation planning in the precast industry. Current practices suggest that the crew allocation process is carried out intuitively and the allocation of crews to production processes is subjective. This has led to high process-waiting times, improper allocation of skilled operators and ultimately higher production costs. In this context, the aim of this research is to propose an effective crew allocation methodology and a computer-based intelligent simulation model for its implementation. The objective of the approach is to guarantee a better workflow through minimising process-waiting time, optimising operator utilisation, and subsequently reducing the allocation cost. This research develops a holistic and integrated methodology for modelling crew allocation problems by reviewing state-of-art resource allocation techniques, structured interviews with production managers, site visits and a detailed case study. The methodology is developed using an IDEF0 process model and a generic process map for both the business and the production processes of the precast manufacturing system. A multi-layered genetic algorithm model is developed in conjunction with a process-simulation model to form a hybrid allocation system dubbed ‘SIM_Crew’. The model incorporates databases (Excel and MS Access), a simulation model (developed using Arena 12.0) and genetic algorithms (developed using Visual Basic for Applications) to facilitate the generation and evaluation of various “what-if” crew allocation scenarios. A number of performance criteria have been developed to evaluate the allocation plans. ‘SIM_Crew’ enables the investigation and analysis of allocating possible schedules and provides a facility to visualise the production processes. ‘SIM_Crew’ was validated using real life case study data and it was concluded that the allocation of crews to precast processes using genetic algorithm improves the throughput time and reduces the allocation cost as compared with real life production data. It is anticipated that future use of this research will solve the crew allocation problem in the precast industry.
Date of Award11 Jun 2010
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorNashwan Dawood (Supervisor) & John. T. Dean (Supervisor)

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