A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Intelligent Systems Research in the Construction Industry
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently Huang and Sun [Huang RY, Sun KS. Non-unit based planning and scheduling of repetitive construction project. J Constr Eng Manage ASCE 2006;132(6):585-97] developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. A sample case study is used for model validation and demonstration. Results and findings are reported.