Computer-aided process planning is an important component for linking design and manufacturing in computer-aided design/computer-aided process planning/computer-aided manufacturing integrated manufacturing systems. Operation sequencing in computer-aided process planning is one of the most essential tasks. To solve the problem and acquire optimal process plans, operation sequencing is modeled as a combinatorial optimization problem with various constraints, and a novel modified ant colony optimization algorithm is developed to solve it. To ensure the feasibility of process plans, constrained relationships considered among operations are classified into two categories called precedence constraint relationships and clustering constraint relationships. Operation precedence graph based on constrained relationships is formed to get visual representation. To ensure good manufacturing economy, in the mathematical model for optimization, total weighted production cost or weighted resource transformation time related to machine changes, setup changes, tool changes, machines and tools is utilized as the evaluation criterion. To avoid local optimum and enhance global search ability, adaptive updating method and local search mechanism are embedded into the optimization algorithm. Case studies of three parts are carried out to demonstrate the feasibility and robustness of the modified ant colony optimization algorithm, and some comparisons between the modified ant colony optimization algorithm and previous genetic algorithm, simulated annealing algorithm, tabu search and particle swarm optimization algorithm are discussed. The results show that the modified ant colony optimization algorithm performs well in the operation sequencing problem.
An adaptive hybrid approach: Combining genetic algorithm and ant colony optimization for integrated process planning and scheduling