Hybrid Mean Particle Swarm Optimization Algorithm for Permutation Flow Shop Scheduling Problem

Yong Quan Zhou; Zheng Xin Huang; Yan Lian Du; Qiao Qiao Gong

doi:10.4028/www.scientific.net/AMM.44-47.270

Paper Titles

The Development of Nozzle Micro-Hole Abrasive Flow Machining Equipment
p.251

Mechanism of Parameters Variation about Electronic Components Subjected to High Impact Loads
p.256

Research on the Influencing Factors of Dynamic Characteristics of Electronic Injector
p.260

Permutation Algorithm with Simulated Annealing for Laser Antimissile Problem
p.265

Hybrid Mean Particle Swarm Optimization Algorithm for Permutation Flow Shop Scheduling Problem
p.270

Research on Dynamic Characteristic Testing Method for Large-Scale Rolling Mill Servo Cylinder
p.275

Research on NC Technology for High Speed Milling
p.280

The Shell Molding Process Study for Mass Production of Single-Cylinder Diesel Engine Crankshaft
p.284

The Kinetics Model Study of Al-Fe In Situ Composites and Applications
p.289

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 44-47Hybrid Mean Particle Swarm Optimization Algorithm...

Hybrid Mean Particle Swarm Optimization Algorithm for Permutation Flow Shop Scheduling Problem

Abstract:

This paper presents a new hybrid mean particle swarm optimization algorithm with improved NEH heuristic approach and local search strategies by using an immune mechanism. This hybrid mean particle swarm optimization algorithm is used for permutation flow shop scheduling problems. Finally, twenty-five problems are used to test the performance of the algorithm, the experimental results show that the proposed approach is an effective and practical.

You might also be interested in these eBooks

Frontiers of Manufacturing and Design Science

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 44-47)

Pages:

270-274

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.44-47.270

Citation:

Cite this paper

Online since:

December 2010

Authors:

Yong Quan Zhou, Zheng Xin Huang, Yan Lian Du, Qiao Qiao Gong

Keywords:

Immune Algorithm, Mean Particle Swarm Optimization, NEH Heuristic, Pairwise, Permutation Flow Shop Scheduling

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Garey M R, Johnson D S. Computers and Intractability: A Guide to the Theory of NP-completeness. San Francisco: Freeman, (1979).

Google Scholar

[2] Johnson R A, Panwalkar S S, Smith M L. The lessons of flowshop scheduling research. Oper. Res. 40: 7-13(1992).

Google Scholar

[3] Eberhart R, Kennedy J. A New Optimizer Using Particle Swarm Theory. Proc. of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan: IEEE Service Center Piscataway NJ, 39-43(1995).

DOI: 10.1109/mhs.1995.494215

Google Scholar

[4] Kusum Deep, Jagdish Chand Bansal. Mean particle swarm optimization for function optimization. Int. J. Computational Intelligence Studies. 1(1): 72-92(2009).

Google Scholar

[5] Nawaz M, Enscore E, Ham I. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11: 91-95(1983).

DOI: 10.1016/0305-0483(83)90088-9

Google Scholar

[6] Bean J C. Genetic algorithm and random keys for sequencing and optimization. ORSA Journal of Computing, 6(2): 154-160 (1994).

DOI: 10.1287/ijoc.6.2.154

Google Scholar

[7] Aldowaisan T, Allahverdi A. New heuristics for no-wait flowshops to minimize makespan. Comput. Oper. Res. 30: 1219-1231 (2003).

DOI: 10.1016/s0305-0548(02)00068-0

Google Scholar

[8] Carlier J. Ordonnancements a constraintes disjonctives. Oper. Res. 12: 333-351(1978).

DOI: 10.1051/ro/1978120403331

Google Scholar

[9] Reeves C R. A genetic algorithm for flowshop sequencing. Comput. Oper. Res. , 22: 5-13(1995).

Google Scholar