Using genetic algorithms to solve construction time-cost trade-off problems

Time-cost trade-off analysis is one of the most important aspects of construction project planning and control. There are trade-offs between time and cost to complete the activities of a project; in general, the less expensive the resources used, the longer it takes to complete an activity. Using critical path method (CPM), the overall project cost can be reduced by using less expensive resources for noncritical activities without impacting the project duration. Existing methods for time-cost trade-off analysis focus on using heuristics or mathematical programming. These methods, however, are not efficient enough to solve large-scale CPM networks (hundreds of activities or more). Analogous to natural selection and genetics in reproduction, genetic algorithms (GAs) have been successfully adopted to solve many science and engineering problems and have proven to be an efficient means for searching optimal solutions in a large problem domain. This paper presents: (1) an algorithm based on the principles of GAs for construction time-cost trade-off optimization; and (2) a computer program that can execute the algorithm efficiently.