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Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms
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DOI：10.1111/j.1467-8667.2010.00666.x.png)
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摘要
En 中文
The Precast Concrete Industry (PCI) is one of the major contributors to the national economy and can be categorized as labor-intensive industry. It is currently experiencing shortcomings in terms of delivery products at a competitive cost and time. This is mainly due to the inefficiencies associate with planning and scheduling of skilled operators within crew configurations. This article presents a new strategy for efficient allocation of crews of workers in the precast concrete industry using Genetic Algorithms-based simulation modeling. The aim of this study is to develop a crew allocation system that can efficiently allocate possible crews of workers to precast concrete labor-intensive repetitive processes. Genetic algorithms (GAs) have been developed to solve this type of problem. Process mapping methodologies were used to identify and document the processes involved in producing precast components. Then process simulation was used to model and simulate all these processes and GAs were tailored to be embedded with the simulation model for a better search of promising solutions. GA operators were designed to suit this type of allocation problem. Class Interval selection strategy was developed to give a greater opportunity for the promising chromosomes to be chosen for further investigation. Dynamic crossover and mutation operators were developed to add more randomness to the search mechanism. The results showed that adopting different combinations of crews of workers had a substantial impact on reducing the process throughput time, minimizing resources cost, and achieving the required operatives utilization.
Keyword:
NEURAL DYNAMICS MODEL
COST OPTIMIZATION
RESOURCE-ALLOCATION
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