Operation sequencing optimization using a particle swarm optimization approach

Abstract

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.

Keywords

process planning particle swarm optimization operation sequencing genetic algorithm simulated annealing

References

Groover

M. P.

Fundamentals of modern manufacturing, 2002, 2nd edition (John Wiley, New York).

Isik

Mileham

A. R.

Automated feature ordering for generative process planning systems. Expert Systems - The Int. J. Knowledge Engng, August 1992, 9(3), 1–5.

Rustom

E. A.

Mileham

A. R.

Automated decision making for process planning. In Flexible automation and information management (Eds Eyada

O. K.

Ahmad

M. M.

), July 1992, pp. 519–530 (CRC Press, Boca Raton, Florida); also Proceedings of the 2nd International Conference on Flexible automation and information management, Virginia.

Chang

T. C.

Expert process planning for manufacturing, 1990 (Addison-Wesley, Reading, Massachusetts).

Reddy

Bhaskara S. V.

Shunmugam

M. S.

Narendran

T. T.

Operation sequencing in CAPP using genetic algorithms. Int. J. Prod. Res., 1999, 37(5), 1063–1074.

Qiao

Wang

X. Y.

Wang

S. C.

A GA-based approach to machining operation sequencing for prismatic parts. Int. J. Prod. Res., 2000, 38(14), 3283–3303.

Yip-Hoi

Dutta

A genetic algorithm application for sequencing operations in process planning for parallel machining. IIE Trans., 1996, 28, 55–68.

Zhang

Y. F.

Nee

A. Y. C.

Using genetic algorithms in process planning for job shop machining. IEEE Trans. Evolutional Computation, 1997, 1, 278–289.

Ding

Yue

Ahmet

Jackson

Parkin

Global optimization of a feature-based process sequence using GA and ANN techniques. Int. J. Prod. Res., 2005, 43(15), 3247–3272.

10.

G. H.

Zhang

Y. F.

Nee

A. Y. C.

A simulated annealing-based optimization for process planning. Int. J. Prod. Res., 2000, 38(12), 2671–2687.

11.

Lee

D. H.

Kiritsis

Xirouchakis

Search heuristics for operation sequencing in process planning. Int. J. Prod. Res., 2001, 39, 3771–3788.

12.

W. D.

Ong

S. K.

Nee

A. Y. C.

Optimization of process plans using a constraint-based Tabu search approach. Int. J. Prod. Res., 2004, 42(10), 1955–1985.

13.

Kennedy

Eberhart

Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural networks, Perth, Australia, 1995, vol. IV, pp. 1942–1948 (IEEE Service Centre, Piscataway, New Jersey).

14.

Wang

K. P.

Huang

Zhou

C. G.

Pang

Particle swarm optimization for travelling salesman problem. In Proceedings of the Second International Conference on Machine learning and cybernetics, Xi'an, China 2003.

15.

Pang

Wang

K. P.

Zhou

C. G.

Dong

L. J.

Fuzzy discrete particle swarm optimization for solving travelling salesman problem. In Proceedings of the Fourth International Conference on Computer and information technology (CIT'04), Wuhan, China 2004.

16.

Onwubolu

G. C.

Clerc

Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. Int. J. Prod. Res., 2004, 42(3), 473–491.

17.

Jerald

Asokan

Prabaharan

Saravanan

Scheduling optimization of flexible manufacturing systems using particle swarm optimization algorithm. Int. J. Advd Mfg Technol., 2005, 25, 964–971.

18.

Case

Wan Harun

W. A.

Feature-based representation for manufacturing planning. Int. J. Prod. Res., 2000, 38(17), 4285–4300.

19.

Maropoulos

P. G.

Baker

R. P.

Integration of tool selection with design (Part 1. Feature creation and selection of operations and tools). J. Mater. Processing Technol., 2000, 107, 127–134.

20.

Carpenter

I. D.

Maropoulos

P. G.

A flexible tool selection decision support system for milling operations. J. Mater. Processing Technol., 2000, 107, 143–152.

21.

Cagnina

Esquivel

Gallard

Particle swarm optimization for sequencing problems: A case study. In 2004 Congress on Evolution computation (CEC2004), Portland, USA, 2004, vol. 1, pp. 536–541.

22.

W. D.

Ong

S. K.

Nee

A. Y. C.

Hybrid genetic algorithm and simulated annealing approach for the optimisation of process plans for prismatic parts. Int. J. Prod. Res., 2002, 40(8), 1899–1922.

23.

Stacey

Jancic

Grundy

Particle swarm optimization with mutation. In 2003 Congress on Evolution computation (CEC2003), 2003, vol. 2, pp. 1425–1430.

24.

Zhang

Y. F.

Nee

A. Y. C.

Using genetic algorithms in process planning for job shop machining. IEEE Trans. Evolutional Computation, 1997, 1, 278–289.

25.

Clerc

The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. In Proceedings of the Congress on Evolutionary computation, Washington, DC, 1999, pp. 1951–1957.