An enhanced ant colony optimization approach for integrated process planning and scheduling

Abstract

An enhanced ant colony optimization (eACO) meta-heuristics is proposed in this paper to accomplish the integrated process planning and scheduling (IPPS) in the jobshop environments. The IPPS problem is graphically formulated to implement the ACO algorithm. In accordance with the characteristics of the IPPS problem, the mechanism of eACO has been enhanced with several modifications, including quantification of convergence level, introduction of pheromone on nodes, new strategy of determining heuristic desirability and directive pheromone deposit strategy. Experiments are conducted to evaluate the approach, while makespan and CPU time are used as measurements. Encouraging results can be seen when comparing to other IPPS approaches based on evolutionary algorithms. © 2013 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.