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A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy

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Abstract

Operation sequencing in CAPP aims at determining the optimal order of machining operations with minimal machining cost and satisfying all the precedence constraints. The genetic algorithm (GA) is widely used to solve precedence constrained operation sequencing problem (PCOSP) due to its efficiency and parallel processing capability. How to guarantee the precedence constraints is always a hot research topic and there are mainly two classes of methods. The first ones use additional adjustment approaches to repair the infeasible solutions that break precedence constraints. It is unreliable and low efficient. The second ones avoid infeasible solutions in initialization through some encoding approaches such as topological storing based encoding approach, but the premature convergence problem may occur facing some complicated PCOSPs. To solve these problems, an edge selection strategy based GA is proposed. The edge selection based strategy could produce feasible solutions in initialization, and assures that every feasible solution will be generated with acceptable probability so as to improve GA’s converging efficiency. Then the precedence constraints are kept by order crossover. Modified mutation operator is designed to optimize the selection of machine tool, tool access direction and cutting tool for each operation. The experiments illustrate that the proposed algorithm is effective and efficient.

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References

  • Amaitik, S., & Kiliç, S. E. (2007). An intelligent process planning system for prismatic parts using STEP features. The International Journal of Advanced Manufacturing Technology, 31(9–10), 978–993. doi:10.1007/s00170-005-0269-5.

    Article  Google Scholar 

  • Chen, C.-F., Wu, M.-C., Li, Y.-H., Tai, P.-H., & Chiou, C.-W. (2013). A comparison of two chromosome representation schemes used in solving a family-based scheduling problem. Robotics and Computer-Integrated Manufacturing, 29(3), 21–30.

    Article  Google Scholar 

  • Cho, D., Lee, Y., Lee, T., & Gen, M. (2014). An adaptive genetic algorithm for the time dependent inventory routing problem. Journal of Intelligent Manufacturing, 25(5), 1025–1042. doi:10.1007/s10845-012-0727-5.

    Article  Google Scholar 

  • Costa, A., Cappadonna, F., & Fichera, S. (2015). A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1049-1.

  • Deja, M., & Siemiatkowski, M. (2013). Feature-based generation of machining process plans for optimised parts manufacture. Journal of Intelligent Manufacturing, 24(4), 831–846. doi:10.1007/s10845-012-0633-x.

    Article  Google Scholar 

  • Ding, L., Yue, Y., Ahmet, K., Jackson, M., & Parkin, R. (2005). Global optimization of a feature-based process sequence using GA and ANN techniques. International Journal of Production Research, 43(15), 3247–3272. doi:10.1080/00207540500137282.

    Article  Google Scholar 

  • Eremeev, A. V. (2012). A genetic algorithm with tournament selection as a local search method. Journal of Applied and Industrial Mathematics, 6(3), 286–294. doi:10.1134/S1990478912030039.

    Article  Google Scholar 

  • Goldberg, D. E., & Bridges, C. L. (1990). An analysis of a reordering operator on a GA-hard problem. Biological Cybernetics, 62(5), 397–405. doi:10.1007/BF00197646.

    Article  Google Scholar 

  • Hua, G.-R., Zhou, X.-H., & Ruan, X.-Y. (2007). GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts. The International Journal of Advanced Manufacturing Technology, 33(5), 594–603.

    Article  Google Scholar 

  • Huang, W., Hu, Y., & Cai, L. (2012). An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts. The International Journal of Advanced Manufacturing Technology, 62(9–12), 1219–1232. doi:10.1007/s00170-011-3870-9.

    Article  Google Scholar 

  • Jiménez, P. (2013). Survey on assembly sequencing: A combinatorial and geometrical perspective. Journal of Intelligent Manufacturing, 24(2), 235–250. doi:10.1007/s10845-011-0578-5.

    Article  Google Scholar 

  • Kafashi, S. (2011). Integrated setup planning and operation sequencing (ISOS) using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 56(5–8), 589–600. doi:10.1007/s00170-011-3202-0.

    Article  Google Scholar 

  • Koulamas, C. (1993). Operation sequencing and machining economics. International Journal of Production Research, 31(4), 957–975. doi:10.1080/00207549308956769.

    Article  Google Scholar 

  • Laporte, G., Riera-Ledesma, J., & Salazar-González, J. (2003). A branch-and-cut algorithm for the undirected traveling purchaser problem. Operations Research, 51(6), 940–951.

    Article  Google Scholar 

  • Lee, D. H., Kiritsis, D., & Xirouchakis, P. (2001). Search heuristics for operation sequencing in process planning. International Journal of Production Research, 39(16), 3771–3788. doi:10.1080/00207540110061922.

    Article  Google Scholar 

  • Lee, S., Soak, S., Kim, K., Park, H., & Jeon, M. (2008). Statistical properties analysis of real world tournament selection in genetic algorithms. Applied Intelligence, 28(2), 195–205. doi:10.1007/s10489-007-0062-2.

    Article  Google Scholar 

  • Li, F., Yang, J., & Jin, C. (2012). A Strategy of genetic operations based on schema. In D. Jin & S. Lin (Eds.), Advances in computer science and information engineering (Vol. 168, pp. 489–494, Advances in intelligent and soft computing). Berlin: Springer.

  • Li, S., Liu, Y., Li, Y., Landers, R., & Tang, L. (2013). Process planning optimization for parallel drilling of blind holes using a two phase genetic algorithm. Journal of Intelligent Manufacturing, 24(4), 791–804. doi:10.1007/s10845-012-0628-7.

    Article  Google Scholar 

  • Li, W. D., Ong, S. K., & Nee, A. Y. C. (2002a). Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. International Journal of Production Research, 40(8), 1899–1922. doi:10.1080/00207540110119991.

    Article  Google Scholar 

  • Li, W. D., Ong, S. K., & Nee, A. Y. C. (2002b). Recognizing manufacturing features from a design-by-feature model. Computer-Aided Design, 34(11), 849–868.

    Article  Google Scholar 

  • Li, W. D., Ong, S. K., & Nee, A. Y. C. (2004). Optimization of process plans using a constraint-based tabu search approach. International Journal of Production Research, 42(10), 1955–1985.

    Article  Google Scholar 

  • Li, Y., & Gong, S. H. (2003). Dynamic ant colony optimisation for TSP. International Journal of Advanced Manufacturing Technology, 22(7–8), 528–533. doi:10.1007/s00170-002-1478-9.

    Article  Google Scholar 

  • Liang, Z., & Xiaohang, Y. (2011). Operations sequencing in flexible production lines with Bernoulli machines. IEEE Transactions on Automation Science and Engineering, 8(3), 645–653. doi:10.1109/TASE.2011.2109061.

    Article  Google Scholar 

  • Lin, C.-J., & Wang, H.-P. (1993). Optimal operation planning and sequencing: Minimization of tool changeovers. International Journal of Production Research, 31(2), 311–324.

    Article  Google Scholar 

  • Liu, Q., Ullah, S., & Zhang, C. (2011). An improved genetic algorithm for robust permutation flowshop scheduling. The International Journal of Advanced Manufacturing Technology, 56(1–4), 345–354. doi:10.1007/s00170-010-3149-6.

    Article  Google Scholar 

  • Liu, X.-J., Yi, H., & Ni, Z.-H. (2013). Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing, 24(1), 1–13.

    Article  Google Scholar 

  • Liu, Z., & Wang, L. (2007). Sequencing of interacting prismatic machining features for process planning. Computers in Industry, 58(4), 295–303.

    Article  Google Scholar 

  • Moghaddam, M. J., Soleymani, M. R., & Farsi, M. A. (2015). Sequence planning for stamping operations in progressive dies. Journal of Intelligent Manufacturing, 26(2), 347–357. doi:10.1007/s10845-013-0788-0.

    Article  Google Scholar 

  • Moon, C., Kim, J., Choi, G., & Seo, Y. (2002). An efficient genetic algorithm for the traveling salesman problem with precedence constraints. European Journal of Operational Research, 140(3), 606–617.

    Article  Google Scholar 

  • Nallakumarasamy, G., Srinivasan, P. S. S., Venkatesh Raja, K., & Malayalamurthi, R. (2011). Optimization of operation sequencing in CAPP using simulated annealing technique (SAT). The International Journal of Advanced Manufacturing Technology, 54(5–8), 721–728. doi:10.1007/s00170-010-2977-8.

    Article  Google Scholar 

  • Novkovic, S., & Šverko, D. (1998). The minimal deceptive problem revisited: The role of “genetic waste”. Computers and Operations Research, 25(11), 895–911.

    Article  Google Scholar 

  • Potvin, J.-Y. (1996). Genetic algorithms for the traveling salesman problem. Annals of Operations Research, 63(3), 337–370. doi:10.1007/BF02125403.

    Article  Google Scholar 

  • Qiao, L., Wang, X. Y., & Wang, S. C. (2000). A GA-based approach to machining operation sequencing for prismatic parts. International Journal of Production Research, 38(14), 3283–3303. doi:10.1080/002075400418261.

    Article  Google Scholar 

  • Reddy, S., Shunmugam, M., & Narendran, T. (1999). Operation sequencing in CAPP using genetic algorithms. International Journal of Production Research, 37(5), 1063–1074.

    Article  Google Scholar 

  • Salehi, M., & Bahreininejad, A. (2011). Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. Journal of Intelligent Manufacturing, 22(4), 643–652. doi:10.1007/s10845-010-0382-7.

  • Sun, X., Chu, X., Su, Y., & Tang, C. (2010). A new directed graph approach for automated setup planning in CAPP. International Journal of Production Research, 48(22), 6583–6612. doi:10.1080/00207540903307615.

  • Tseng, Y.-J., Kao, H.-T., & Huang, F.-Y. (2009). Integrated assembly and disassembly sequence planning using a GA approach. International Journal of Production Research, 48(20), 5991–6013. doi:10.1080/00207540903229173.

    Article  Google Scholar 

  • Wang, B., Guan, Z., Ullah, S., Xu, X., & He, Z. (2014). Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: A multi-objective hybrid artificial bee colony algorithm. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0988-2.

  • Whitley, D., Starkweather, T., & Shaner, D. (1991). The traveling salesman and sequence scheduling: Quality solutions using genetic edge recombination. In L. Davis (Ed.), Handbook of genetic algorithms (pp. 350–372). New York: Department of Computer Science, Colorado State University.

    Google Scholar 

  • Xu, P., & Wang, L. (2014). An exact algorithm for the bilevel mixed integer linear programming problem under three simplifying assumptions. Computers and Operations Research, 41(1), 309–318. doi:10.1016/j.cor.2013.07.016.

  • Yun, Y., Chung, H., & Moon, C. (2013). Hybrid genetic algorithm approach for precedence-constrained sequencing problem. Computers and Industrial Engineering, 65(1), 137–147. doi:10.1016/j.cie.2011.11.019.

    Article  Google Scholar 

  • Yun, Y., & Moon, C. (2011). Genetic algorithm approach for precedence-constrained sequencing problems. Journal of Intelligent Manufacturing, 22(3), 379–388. doi:10.1007/s10845-009-0296-4.

    Article  Google Scholar 

  • Yusof, Y., & Latif, K. (2014). Survey on computer-aided process planning. The International Journal of Advanced Manufacturing Technology, 75(1–4), 77–89. doi:10.1007/s00170-014-6073-3.

    Article  Google Scholar 

  • Zhang, W., Gen, M., & Jo, J. (2014). Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem. Journal of Intelligent Manufacturing, 25(5), 881–897. doi:10.1007/s10845-013-0814-2.

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by National Natural Science Foundation of China (Grant No. 51475290, Grant No. 51075261), Research Fund for the Doctoral Program of Higher Education of China (No. 20120073110096) Shanghai Science and Technology Innovation Action Plan (No. 11DZ1120800).

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Correspondence to Yuliang Su.

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Su, Y., Chu, X., Chen, D. et al. A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy. J Intell Manuf 29, 313–332 (2018). https://doi.org/10.1007/s10845-015-1109-6

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