Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. In this paper, the complicated operation sequencing process has been modelled as a combinatorial optimization problem, and a modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles 'fly' intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles' movements, forming a modified PSO algorithm. A case study involving three prismatic parts has been used to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results using the genetic algorithm (GA) and the simulated annealing (SA) algorithm and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimizing the process planning problem. © IMechE 2006.
|Number of pages||14|
|Journal||Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture|
|Publication status||Published - 1 Dec 2006|
- Combinatorial mathematics
- Process planning
- Genetic algorithms
- Simulated annealing