An effective modified Particle Swarm Optimization algorithm for process planning

In the modern manufacturing system, most jobs have a large number of flexible process plans. However, there is only one process plan can be selected for a job in the manufacturing process. Therefore, flexible process plans selection has become a crucial problem in a manufacturing environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from the practical workshop environment as well as the jobs to be processed. In this paper, a new method using a modified particle swarm optimization (PSO) algorithm is presented to optimize the process planning problem. To improve the optimization performance of the approach, efficient encoding and updating strategies have been developed. To verify the feasibility and performance of the proposed approach, a case study has been conducted. The results show that the proposed modified PSO algorithm can generate satisfactory solutions.

[1]  Mojtaba Salehi,et al.  Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning , 2009, Eng. Appl. Artif. Intell..

[2]  W. D. Li,et al.  A simulated annealing-based optimization approach for integrated process planning and scheduling , 2007, Int. J. Comput. Integr. Manuf..

[3]  Liang Gao,et al.  Optimization of flexible process planning by genetic programming , 2008 .

[4]  Andrew Y. C. Nee,et al.  Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments , 2005 .

[5]  Liang Gao,et al.  Cellular particle swarm optimization , 2011, Inf. Sci..

[6]  M. M. Ali,et al.  Improved particle swarm algorithms for global optimization , 2008, Appl. Math. Comput..

[7]  Xun Xu,et al.  Computer-aided process planning – A critical review of recent developments and future trends , 2011, Int. J. Comput. Integr. Manuf..

[8]  Dongbo Li,et al.  A clustering-based modeling scheme of the manufacturing resources for process planning , 2008 .

[9]  M. Tiwari,et al.  A fuzzy goal-programming model of machine-tool selection and operation allocation problem in FMS: a quick converging simulated annealing-based approach , 2006 .

[10]  A Web-based process planning optimization system for distributed design , 2005, Comput. Aided Des..

[11]  Xinyu Shao,et al.  A multi-dimensional tabu search algorithm for the optimization of process planning , 2011 .

[12]  Shyi-Ming Chen,et al.  Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques , 2011, Expert Syst. Appl..

[13]  Y. Wang,et al.  Chaotic particle swarm optimization for assembly sequence planning , 2010 .

[14]  Andrew Y. C. Nee,et al.  Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts , 2002 .

[15]  F. Musharavati,et al.  Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines , 2011, Expert Syst. Appl..

[16]  Zita Vale,et al.  Process planning using a genetic algorithm approach , 1999, Proceedings of the 1999 IEEE International Symposium on Assembly and Task Planning (ISATP'99) (Cat. No.99TH8470).

[17]  Juneho Yi,et al.  Manufacturing feature recognition toward integration with process planning , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Kay Chen Tan,et al.  A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design , 2010, Eur. J. Oper. Res..

[19]  Farayi Musharavati,et al.  Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing , 2012 .

[20]  Xionghui Zhou,et al.  GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts , 2007 .

[21]  Dongbo Li,et al.  A novel process planning schema based on process knowledge customization , 2009 .

[22]  Yunfeng Zhang,et al.  Using Hybrid Particle Swarm Optimization for Process Planning Problem , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[23]  Alluru Gopala Krishna,et al.  Optimisation of operations sequence in CAPP using an ant colony algorithm , 2006 .

[24]  Andrew Kusiak,et al.  Integer programming approach to process planning , 1985 .

[25]  Andrew Y. C. Nee,et al.  Using genetic algorithms in process planning for job shop machining , 1997, IEEE Trans. Evol. Comput..

[26]  K. L. Mak,et al.  A hybrid Hopfield network-genetic algorithm approach to optimal process plan selection , 2000 .

[27]  Y. F. Zhang,et al.  A Branch and Bound Algorithm Based Process-Planning System for Plastic Injection Mould Bases , 2001 .

[28]  Mojtaba Salehi,et al.  Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining , 2011, J. Intell. Manuf..

[29]  G. Moslehi,et al.  A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search , 2011 .

[30]  Liang Gao,et al.  An Effective Hybrid Genetic Simulated Annealing Algorithm for Process Planning Problem , 2009, 2009 Fifth International Conference on Natural Computation.

[31]  Guang-Yu Zhu,et al.  Comparison and application of four versions of particle swarm optimization algorithms in the sequence optimization , 2011, Expert Syst. Appl..

[32]  Felix T.S. Chan,et al.  A holonic architecture of the concurrent integrated process planning system , 2003 .

[33]  Liang Gao,et al.  Optimization of process planning with various flexibilities using an imperialist competitive algorithm , 2012 .

[34]  Nobuhiro Sugimura,et al.  Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system , 2010, J. Intell. Manuf..

[35]  Manoj Kumar Tiwari,et al.  Ant colony optimization to select the best process plan in an automated manufacturing environment , 2006 .

[36]  Jesuk Ko,et al.  A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling , 2003, Comput. Oper. Res..

[37]  W. D. Li.  A Web-based service for distributed process planning optimization , 2005, Comput. Ind..

[38]  Russell C. Eberhart,et al.  Guest Editorial Special Issue on Particle Swarm Optimization , 2004, IEEE Trans. Evol. Comput..

[39]  Manoj Kumar Tiwari,et al.  A psycho-clonal-algorithm-based approach to the solve operation sequencing problem in a CAPP environment , 2008, Int. J. Comput. Integr. Manuf..

[40]  F. T. S. Chan *,et al.  Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system , 2005 .

[41]  Kun Hua Tsai,et al.  Interactive and dynamic review course composition system utilizing contextual semantic expansion and discrete particle swarm optimization , 2009, Expert Syst. Appl..

[42]  Andrew Y. C. Nee,et al.  Optimization of process plans using a constraint-based tabu search approach , 2004 .

[43]  Jong-Oh Park,et al.  Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm , 2011, Comput. Oper. Res..

[44]  Ligang Cai,et al.  An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts , 2012 .

[45]  Y W Guo,et al.  Operation sequencing optimization using a particle swarm optimization approach , 2006 .

[46]  Andrew Y. C. Nee,et al.  A simulated annealing-based optimization algorithm for process planning , 2000 .

[47]  Seyed Taghi Akhavan Niaki,et al.  Using flower pollinating with artificial bees (FPAB) technique to determine machinable volumes in process planning for prismatic parts , 2009 .