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Research Papers

A Cooperative Co-Evolutionary Algorithm for Large-Scale Process Planning With Energy Consideration

[+] Author and Article Information
Fei Tao

School of Automation Science and
Electrical Engineering,
Beihang University,
Haidian District,
Beijing 100191, China
e-mail: ftao@buaa.edu.cn

Luning Bi, Ying Zuo

School of Automation Science and
Electrical Engineering,
Beihang University,
Beijing 100191, China

A. Y. C. Nee

Department of Mechanical Engineering,
National University of Singapore,
Singapore 117576, Singapore

1Corresponding author.

Manuscript received March 3, 2016; final manuscript received December 23, 2016; published online March 3, 2017. Assoc. Editor: Jianjun Shi.

J. Manuf. Sci. Eng 139(6), 061016 (Mar 03, 2017) (11 pages) Paper No: MANU-16-1135; doi: 10.1115/1.4035960 History: Received March 03, 2016; Revised December 23, 2016

Process planning can be an effective way to improve the energy efficiency of production processes. Aimed at reducing both energy consumption and processing time (PT), a comprehensive approach that considers feature sequencing, process selection, and physical resources allocation simultaneously is established in this paper. As the number of decision variables increase, process planning becomes a large-scale problem, and it is difficult to be addressed by simply employing a regular meta-heuristic algorithm. A cooperative co-evolutionary algorithm, which hybridizes the artificial bee colony algorithm (ABCA) and Tabu search (TS), is therefore proposed. In addition, in the proposed algorithm, a novel representation method is designed to generate feasible process plans under complex precedence. Compared with some widely used algorithms, the proposed algorithm is proven to have a good performance for handling large-scale process planning in terms of maximizing energy efficiency and production times.

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References

Dai, M. , Tang, D. , Xu, Y. , and Li, W. , 2015, “ Energy-Aware Integrated Process Planning and Scheduling for Job Shops,” Proc. Inst. Mech. Eng., Part B, 229(1-Suppl), pp. 13–26. [CrossRef]
Doh, H.-H. , Yu, J.-M. , Kim, J.-S. , Lee, D.-H. , and Nam, S.-H. , 2013, “ A Priority Scheduling Approach for Flexible Job Shops With Multiple Process Plans,” Int. J. Prod. Res., 51(12), pp. 3748–3764. [CrossRef]
Alting, L. , and Zhang, H. , 1989, “ Computer Aided Process Planning: The State-of-the-Art Survey,” Int. J. Prod. Res., 27(4), pp. 553–585. [CrossRef]
Yusof, Y. , and Latif, K. , 2014, “ Survey on Computer-Aided Process Planning,” Int. J. Adv. Manuf. Technol., 73(1), pp. 1–13.
Sadaiah, M. , Yadav, D. R. , Mohanram, P. V. , and Radhakrishnan, P. , 2002, “ A Generative Computer-Aided Process Planning System for Prismatic Components,” Int. J. Adv. Manuf. Technol., 20(10), pp. 709–719. [CrossRef]
Ren, L. , Sparks, T. , Ruan, J. , and Liou, F. , 2010, “ Integrated Process Planning for a Multiaxis Hybrid Manufacturing System,” ASME J. Manuf. Sci. Eng., 132(2), p. 021006. [CrossRef]
Chen, H. , Xi, N. , Sheng, W. , and Chen, Y. , 2005, “ General Framework of Optimal Tool Trajectory Planning for Free-Form Surfaces in Surface Manufacturing,” ASME J. Manuf. Sci. Eng., 127(1), pp. 49–59. [CrossRef]
Zhou, C. , 2014, “ A Direct Tool Path Planning Algorithm for Line Scanning Based Stereolithography,” ASME J. Manuf. Sci. Eng., 136(6), p. 061023. [CrossRef]
Wang, Z. , Tan, C. , and Dong, X. , 2009, “ Conflict Resolution Based on CBR in Intelligent CAPP System,” International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, Zhejiang, China, Aug. 26–27, pp. 133–136.
Yang, Z. , Wysk, R. A. , and Joshi, S. , 2012, “ Setup Planning Automation for Six-Axis Wire Electrical Discharge Machining,” ASME J. Manuf. Sci. Eng., 134(2), p. 021009. [CrossRef]
Li, Y. , and Frank, M. C. , 2012, “ Computing Axes of Rotation for Setup Planning Using Visibility of Polyhedral Computer-Aided Design Models,” ASME J. Manuf. Sci. Eng., 134(4), p. 041005. [CrossRef]
Wang, J. , and Meng, Q. , 2008, “ Knowledge Representation for Knowledge-Based Generative CAPP,” IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM), Wuhan, China, Dec. 21–22, pp. 1010–1013.
Kim, Y. K. , Park, K. , and Ko, J. , 2003, “ A Symbiotic Evolutionary Algorithm for the Integration of Process Planning and Job Shop Scheduling,” Comput. Oper. Res., 30(02), pp. 1151–1171. [CrossRef]
Musharavati, F. , and Hamouda, A. S. M. , 2012, “ Enhanced Simulated-Annealing-Based Algorithms and Their Applications to Process Planning in Reconfigurable Manufacturing Systems,” Adv. Eng. Software, 45(1), pp. 80–90. [CrossRef]
Shabaka, A. I. , and ElMaraghy, H. A. , 2008, “ A Model for Generating Optimal Process Plans in RMS,” Int. J. Comput. Integr. Manuf., 21(2), pp. 180–194. [CrossRef]
Bensmaine, A. , Dahane, M. , and Benyoucef, L. , 2013, “ A Non-Dominated Sorting Genetic Algorithm Based Approach for Optimal Machines Selection in Reconfigurable Manufacturing Environment,” Comput. Ind. Eng., 66(3), pp. 519–524. [CrossRef]
Chaube, A. , Benyoucef, L. , and Tiwari, M. K. , 2012, “ An Adapted NSGA-2 Algorithm Based Dynamic Process Plan Generation for a Reconfigurable Manufacturing System,” J. Intell. Manuf., 23(4), pp. 1141–1155. [CrossRef]
Manupati, V. K. , Thakkar, J. J. , Wong, K. Y. , and Tiwari, M. K. , 2013, “ Near Optimal Process Plan Selection for Multiple Jobs in Networked Based Manufacturing Using Multi-Objective Evolutionary Algorithms,” Comput. Ind. Eng., 66(1), pp. 63–76. [CrossRef]
Roohnavazfar, M. , Houshmand, M. , Nasiri-Zarandi, R. , and Mirsalim, M. , 2014, “ Using Axiomatic Design Theory for Selection of the Optimum Design Solution and Manufacturing Process Plans of a Limited Angle Torque Motor,” ASME J. Manuf. Sci. Eng., 136(5), p. 051009. [CrossRef]
Haapala, K. R. , Catalina, A. V. , Johnson, M. L. , and Sutherland, J. W. , 2012, “ Development and Application of Models for Steelmaking and Casting Environmental Performance,” ASME J. Manuf. Sci. Eng., 134(5), p. 051013. [CrossRef]
Haapala, K. R. , Zhao, F. , Camelio, J. , Sutherland, J. W. , Skerlos, S. J. , Dornfeld, D. A. , Jawahir, I. S. , Zhang, H. C. , and Clarens, A. F. , 2011, “ A Review of Engineering Research in Sustainable Manufacturing,” ASME Paper No. MSEC2011-50300.
Karlsdottir, M. R. , Palsson, O. P. , and Palsson, H. , 2008, “ Energy Efficiency Consideration of Geothermal Based Power Production,” International Symposium on District Heating and Cooling, Reykjavik, Iceland, Aug. 31–Sept. 2.
Weinert, N. , Chiotellis, S. , and Seliger, G. , 2011, “ Methodology for Planning and Operating Energy-Efficient Production Systems,” CIRP Ann.-Manuf. Technol., 60(1), pp. 41–44. [CrossRef]
Suh, S. H. , 2015, “ A Green Productivity Based Process Planning System for a Machining Process,” Int. J. Prod. Res., 53(17), pp. 5085–5105. [CrossRef]
Mei, Y. , Li, X. , and Yao, X. , 2014, “ Cooperative Coevolution With Route Distance Grouping for Large-Scale Capacitated Arc Routing Problems,” IEEE Trans. Evol. Comput., 18(3), pp. 435–449. [CrossRef]
Frans, V. D. B. , and Engelbrecht, A. P. , 2004, “ A Cooperative Approach to Particle Swarm Optimization,” IEEE Trans. Evol. Comput., 8(3), pp. 225–239. [CrossRef]
Mcgibney, A. , Klepal, M. , and Pesch, D. , 2011, “ Agent-Based Optimization for Large Scale WLAN Design,” IEEE Trans. Evol. Comput., 15(4), pp. 470–486. [CrossRef]
Omidvar, M. N. , Li, X. , Mei, Y. , and Yao, X. , 2014, “ Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization,” IEEE Trans. Evol. Comput., 18(3), pp. 378–393. [CrossRef]
Li, X. , and Yao, X. , 2012, “ Cooperatively Coevolving Particle Swarms for Large Scale Optimization,” IEEE Trans. Evol. Comput., 16(2), pp. 210–224. [CrossRef]
Wang, L. , Cai, N. , Feng, H.-Y. , and Liu, Z. , 2006, “ Enriched Machining Feature-Based Reasoning for Generic Machining Process Sequencing,” Int. J. Prod. Res., 44(8), pp. 1479–1501. [CrossRef]
Nonaka, Y. , Erdős, G. , Kis, T. , Nakano, T. , and Váncza, J. , 2012, “ Scheduling With Alternative Routings in CNC Workshops,” CIRP Ann.-Manuf. Technol., 61(1), pp. 449–454. [CrossRef]
Fazli, A. , Arezoo, B. , and Hasanniya, M. H. , 2014, “ An Automated Process Sequence Design and Finite Element Simulation of Axisymmetric Deep Drawn Components,” ASME J. Manuf. Sci. Eng., 136(3), p. 031005. [CrossRef]
Azab, A. , and Elmaraghy, H. A. , 2007, “ Mathematical Modeling for Reconfigurable Process Planning,” CIRP Ann.-Manuf. Technol., 56(1), pp. 467–472. [CrossRef]
Wang, L. , Holm, M. , and Adamson, G. , 2010, “ Embedding a Process Plan in Function Blocks for Adaptive Machining,” CIRP Ann.-Manuf. Technol., 59(1), pp. 433–436. [CrossRef]
Nonaka, Y. , Erdős, G. , Kis, T. , Kovács, A. , Monostori, L. , Nakano, T. , and Váncza, J. , 2013, “ Generating Alternative Process Plans for Complex Parts,” CIRP Ann.-Manuf. Technol., 62(1), pp. 453–458. [CrossRef]
Tapoglou, N. , Mehnen, J. , Vlachou, A. , Doukas, M. , Milas, N. , and Mourtzis, D. , 2015, “ Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring,” ASME J. Manuf. Sci. Eng., 137(4), p. 040909. [CrossRef]
Cai, X. , Li, W. , He, F. , and Li, X. , 2015, “ Customized Encryption of Computer Aided Design Models for Collaboration in Cloud Manufacturing Environment,” ASME J. Manuf. Sci. Eng., 137(4), p. 040905. [CrossRef]
Tao, F. , Laili, Y. , Xu, L. , and Zhang, L. , 2013, “ FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System,” IEEE Trans. Ind. Inf., 9(4), pp. 2023–2033. [CrossRef]
Karaboga, D. , 2005, “ An Idea Based on Honey Bee Swarm for Numerical Optimization,” Technical Report No. TR06.
Liu, J. , Zhu, H. , Ma, Q. , Zhang, L. , and Xu, H. , 2015, “ An Artificial Bee Colony Algorithm With Guide of Global & Local Optima and Asynchronous Scaling Factors for Numerical Optimization,” Appl. Soft Comput., 37(C), pp. 608–618. [CrossRef]

Figures

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Fig. 1

Top-down structure of process planning

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Fig. 2

Selection of process

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Fig. 3

Selection of physical resources

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Fig. 4

Structure of traditional ABCA

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Fig. 5

Encoding and decoding of process plan

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Fig. 6

Sequence graph of a product with ten features

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Fig. 7

The encapsulation of the process plan of clusters

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Fig. 8

The calculation of overall fitness

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Fig. 9

Structure of the proposed ABCA

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Fig. 10

The structure of ANC 101 [15]

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Fig. 11

Feature precedence graph of a product [15]

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Fig. 12

Convergence curve of the proposed algorithm

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Fig. 13

Running results of five algorithms (NF = 20)

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Fig. 14

Running results of five algorithms (NF = 30)

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Fig. 15

Running results of five algorithms (NF = 40)

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Fig. 16

Values of the objectives (NF = 20)

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Fig. 17

Values of the objectives (NF = 30)

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Fig. 18

Values of the objectives (NF = 40)

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