Hybrid Genetic Algorithm Parameter Effects for Optimization of Construction Resource Allocation Problem

Abstract

The optimal solutions for the resource allocation problem are of great significant to project planners for distributing their available resources into the activities most effectively. Many studies have been undertaken to solve the resource-constrained project scheduling problems using genetic algorithms, which have been proven as an effective and efficient optimization tool to solve difficult and complex problems. One of the trends in the genetic algorithm research study is to develop a hybrid meta-heuristic method using artificial intelligence and biologically-inspired techniques. In an effort to address this issue, the author developed a new hybrid genetic algorithm to solve the construction resource-constrained project scheduling problems. This paper evaluates the parameter effects of the hybrid genetic algorithm for optimization because optimal settings of the genetic algorithm parameters such as population size, crossover probability, and mutation probability, are critical conditions in producing the best value for the outcomes.