Intercell Scheduling Approach Based on Ant Colony Optimization Algorithm and Genetic Programming

To deal with the intercell scheduling problem with limited transportation capabilities, a hyper-heuristic approach was developed based on an ant colony optimization and genetic programming algorithm. The ant colony optimization algorithm was used to search the appropriate heuristic rules for the addressed problem. And the genetic programming was used to generate well-performing heuristic rules as an extension to the predefined candidate heuristic rules. Meanwhile, a time window was introduced into the proposed algorithm to determine the vehicle waiting time for a batch processing. Experimental results show that the ant colony optimization algorithm can search the outperforming combinations of the heuristic rules, the heuristic rules generated via genetic programming can obviously improve the quality of the candidate rule set, and that the time window can obviously improve the vehicle efficiency and minimize the total weighted tardiness, therefore providing better performance.