An optimization model for workgroup-based repetitive scheduling

• Published in: Canadian journal of civil engineering Vol. 33; no. 9; pp. 1172 - 1194
• Main Authors: Huang, Rong-yau, Sun, Kuo-Shun
• Format: Journal Article
• Language: English
• Published: Ottawa, Canada NRC Research Press 01.09.2006; National Research Council of Canada; Canadian Science Publishing NRC Research Press
• Subjects: Analysis; Applied sciences; Buildings. Public works; Civil engineering; Computation methods. Tables. Charts; Construction; Construction industry; Exact sciences and technology; Genetic algorithms; Industrial project management; Management; Methods; Optimization; Project engineering; Project management; Project management. Process of design; Scheduling; Sewer systems; Structural analysis. Stresses; Canada; Script; Costs; Project management; Construction scheduling; Scheduling; Modeling; Optimization; Construction industry; Case study; Sewer; Genetic algorithm; Efficiency; Repetition; Method study
• ISSN: 0315-1468; 1208-6029
• DOI: 10.1139/l06-057

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 (2005) 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 or near-optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. Two sample case studies, one simple and one sewer system project, are used for model validation and demonstration. Results and findings are reported.Key words: construction scheduling, repetitive project, workgroup, optimization, genetic algorithm.