The present invention provides a project scheduling
rule mining method and
system based on
gene expression
programming (GEP), which is used to solve the multi-objective
optimization problem of multi-skill, resource-constrained project scheduling. During the solution process, previous project information is used as
training set data. Project information and
resource information are integrated to extract various feature attributes with decision-making value. These attributes are combined with several basic
mathematical operators to form the genetic source of soft chromosomes. Each soft
chromosome represents a
hybrid scheduling rule, which is used as a decision-making method for the order of task execution to obtain a specific scheduling solution. The solution process uses an improved GEP
algorithm, which designs a backward traversal decoding method, incorporates four neighborhood structure operators and a
rule mining perturbation mechanism, and improves the ENS
ranking method used in the solution evaluation process. This greatly improves the
algorithm performance and the efficiency of exploring the frontier solution set. The resulting metaheuristic rule set can be easily applied to real projects and production environments.