Scheduling of Multiple Projects with Resource Constraints Using Genetic Algorithms

Abstract

A scheduling approach using genetic algorithms (GA) was presented to optimize multiple projects for quality project period performance with resource constraints. The model of the approach and key parameters of the algorithm including chromosome encoding and decoding, fitness computation, initial population, selection and crossover were conducted. A precedence feasible list was used in the chromosome encoding and decoding operation to reduce search space. An efficient crossover method was developed to avoid the procedure of chromosome recovery. A comparison was made between the algorithm and a heuristic scheduling method with an example. The result validates the superiority of the approach.