Constructive cooperative coevolutionary optimisation for interacting production stations

  • Emile GlorieuxEmail author
  • Fredrik Danielsson
  • Bo Svensson
  • Bengt Lennartson


Optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation techniques, it often becomes necessary to use simulation models to represent the problem because of the high complexity (i.e. simulation-based optimisation). Standard optimisation techniques are likely to either exceed the practical time frame or under-perform compared to the manual tuning by the operators or process planners. This paper presents the Constructive cooperative coevolutionary (C3) algorithm, which objective is to enable effective simulation-based optimisation for the control of automated interacting production stations within a practical time frame. C3 is inspired by an existing cooperative coevolutionary algorithm. Thereby, it embeds an algorithm that optimises subproblems separately. C3 also incorporates a novel constructive heuristic to find good initial solutions and thereby expedite the optimisation. In this work, two industrial optimisation problems, involving interaction production stations, with different sizes are used to evaluate C3. The results illustrate that with C3, it is possible to optimise these problems within a practical time frame and obtain a better solution compared to manual tuning.


Manufacturing automation Metaheuristic optimisation algorithm Optimised production technology Interacting production stations Sheet metal press line 


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  1. 1.
    Aickelin U, Dowsland KA (2000) Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem. J Sched 3(3):139–153MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Alvarez-Valdes R, Crespo E, Tamarit JM, Villa F (2008) GRASP and path relinking for project scheduling under partially renewable resources. Eur J Oper Res 189:1153–1170. 3CrossRefzbMATHGoogle Scholar
  3. 3.
    Andradóttir S (1998) Simulation optimization. In: Banks J (ed). Wiley, New York, pp 307–333Google Scholar
  4. 4.
    Antonio L, Coello Coello C (2013) Use of cooperative coevolution for solving large scale multiobjective optimization problems. In: Proc. congr. evolutionary computation (CEC 13), pp 2758–2765Google Scholar
  5. 5.
    Arroyo JEC, Vieira PS, Vianna DS (2008) A GRASP algorithm for the multi-criteria minimum spanning tree problem. Ann Oper Res 159(1):125–133MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Van den Bergh F, Engelbrecht A (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239CrossRefGoogle Scholar
  7. 7.
    Van den Bergh F, Engelbrecht AP (2000) Cooperative learning in neural networks using particle swarm optimizers. S Afr Comput J 26:84–90Google Scholar
  8. 8.
    Boudia M, Louly M, Prins C (2007) A reactive GRASP and path relinking for a combined production–distribution problem. Comput Oper Res 34(11):3402–3419CrossRefzbMATHGoogle Scholar
  9. 9.
    Cai Z, Peng Z (2002) Cooperative coevolutionary adaptive genetic algorithm in path planning of cooperative multi-mobile robot systems. J Intell Robot Syst 33(1):61–71CrossRefzbMATHGoogle Scholar
  10. 10.
    Coello Coello CA, Sierra M (2003) A coevolutionary multi-objective evolutionary algorithm. In: Proc. congr. evolutionary computation (CEC 03), vol 1, pp 482–489Google Scholar
  11. 11.
    Corberán A, Marti R, Sanchis J (2002) A GRASP heuristic for the mixed Chinese postman problem. Eur J Oper Res 142(1):70–80MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Cung VD, Martins SL, Ribeiro CC, Roucairol C (2002) Strategies for the parallel implementation of metaheuristics. In: Essays and surveys in metaheuristics, no. 15 in operations research/computer science interfaces series. Springer US, pp 263–308Google Scholar
  13. 13.
    Das S, Suganthan P (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31CrossRefGoogle Scholar
  14. 14.
    Dorronsoro B, Danoy G, Nebro AJ, Bouvry P (2013) Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution. Comput Oper Res 40(6):1552–1563MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Dzitac P, Mazid A (2008) An efficient control configuration development for a high-speed robotic palletizing system. In: 2008 IEEE conference on robotics, automation and mechatronics, pp 140–145Google Scholar
  16. 16.
    Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proc. congr. evolutionary computation (CEC 00), vol 1, pp 84–88Google Scholar
  17. 17.
    Feo TA, Resende MGC (1989) A probabilistic heuristic for a computationally difficult set covering problem. Oper Res Lett 8:67–71MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6(2):109–133MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Glorieux E, Svensson B, Danielsson F, Lennartson B (2014) A constructive cooperative coevolutionary algorithm applied to press line optimisation. In: Proceedings of the 24th international conference on flexible automation and intelligent manufacturing, Texas, pp 909–917Google Scholar
  20. 20.
    Glorieux E, Svensson B, Danielsson F, Lennartson B (2014) Optimisation of interacting production stations using a constructive cooperative coevolutionary approach. In: Proc. 2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014), pp 322–327Google Scholar
  21. 21.
    Goh C, Tan K, Liu D, Chiam S (2010) A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. Eur J Oper Res 202(1): 42–54CrossRefzbMATHGoogle Scholar
  22. 22.
    Goodman MD, Dowsland KA, Thompson JM (2009) A grasp-knapsack hybrid for a nurse-scheduling problem. J Heuristics 15(4):351–379CrossRefzbMATHGoogle Scholar
  23. 23.
    Grendreau M, Potvin JY (eds) (2010) Handbook of metaheuristics, international series in operations research & management science, vol 146, 2nd edn. Springer, USA.
  24. 24.
    Jamil M, Yang X (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194zbMATHGoogle Scholar
  25. 25.
    Jimenez MA, Gutierrez SV, Lizarraga G, Garza MA, Gonzalez DS, Acevedo JL, Osorio MC, Rodríguez RA (2013) Automation and parameters optimization in production line: a case of study. Int J Adv Manuf Technol 66(9–12):1315–1318CrossRefGoogle Scholar
  26. 26.
    Jones DR, Perttunen CD, Stuckman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl 79(1):157–181MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proc. IEEE Int. conf. neural networks, vol 4, pp 1942–1948Google Scholar
  28. 28.
    Kim YK, Kim JY, Kim Y (2000) A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines. Appl Intell 13(3):247–258CrossRefGoogle Scholar
  29. 29.
    Li H, Ceglarek D (2002) Optimal trajectory planning for material handling of compliant sheet metal parts. J Mech Des 124(2):213–222CrossRefGoogle Scholar
  30. 30.
    Li J, Masood SH (2008) Modelling robotic palletising process with two robots using queuing theory. J Achiev Mater Manuf Eng 31(2):526–530Google Scholar
  31. 31.
    Nascimento MC, Resende MG, Toledo FM (2010) GRASP heuristic with path-relinking for the multi-plant capacitated lot sizing problem. Eur J Oper Res 200(3):747–754CrossRefzbMATHGoogle Scholar
  32. 32.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Nia NK, Danielsson F, Lennartson B (2011) A faster collision detection method applied on a sheet metal press line. In: Proc. of 21st int. conf. on flexible automation and intelligent manufacturing (FAIM), Taichung, pp 833–840Google Scholar
  34. 34.
    Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670CrossRefGoogle Scholar
  35. 35.
    Osman IH, Al-Ayoubi B, Barake M (2003) A greedy random adaptive search procedure for the weighted maximal planar graph problem. Comput Ind Eng 45(4):635–651CrossRefGoogle Scholar
  36. 36.
    Pettersson M, Olvander J, Andersson H (2007) Application adapted performance optimization for industrial robots. In: IEEE international symposium on industrial electronics, 2007. ISIE 2007, pp 2047–2052Google Scholar
  37. 37.
    Potter MA, Jong KAD (1994) A cooperative coevolutionary approach to function optimization. In: Davidor Y, Schwefel HP, Männer R (eds) Parallel problem solving from nature—PPSN III, no. 866 in Lecture Notes in Computer Science. Springer, Berlin, pp 249–257Google Scholar
  38. 38.
    Qin A, Li X (2013) Differential evolution on the CEC-2013 single-objective continuous optimization testbed. In: Proc. Congr. evolutionary computation (CEC 13), pp 1099–1106Google Scholar
  39. 39.
    Qingyu S, Baofeng G, Jian L (2013) Drawing motion profile planning and optimizing for heavy servo press. Int J Adv Manuf Technol 69(9–12):2819–2831CrossRefGoogle Scholar
  40. 40.
    Qu H, Xing K, Alexander T (2013) An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots. Neurocomputing 120:509– 517CrossRefGoogle Scholar
  41. 41.
    Resende MG (2008) Metaheuristic hybridization with GRASP. TutORials in Operations Research. Institute of Management Science and Operational ResearchGoogle Scholar
  42. 42.
    Seok Shin K, Park JO, Keun Kim Y (2011) Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Comput Oper Res 38(3):702– 712MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Shan S, Wang G (2010) Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Struct Multidiscip Optim 41:219–241. 2MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proc. IEEE world congr. computational intelligence evolutionary computation, pp 69–73Google Scholar
  45. 45.
    Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefzbMATHGoogle Scholar
  46. 46.
    Sun W, Huo J, Chen J, Li Z, Zhang X, Guo L, Zhao H, Zhao Y (2011) Disc cutters’ layout design of the full-face rock tunnel boring machine (TBM) using a cooperative coevolutionary algorithm. J Mech Sci Technol 25(2):415–427CrossRefGoogle Scholar
  47. 47.
    Svensson B, Danielsson F, Lennartson B (2013) An efficient algorithm for press line optimisation. Int J Adv Manuf Technol 68(5–8):1627–1638CrossRefGoogle Scholar
  48. 48.
    Takayama Y (2008) Tandem press line, operation control method for tandem press line, and work transportation device for tandem press lineGoogle Scholar
  49. 49.
    Wiegand RP, Liles WC, Jong KAD (2001) An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Proceedings from the genetic and evolutionary computation conference. Morgan Kaufmann, pp 1235–1242Google Scholar
  50. 50.
    Yang Z, Tang K, Yao X (2007) Differential evolution for high-dimensional function optimization. In: Proc. congr. evolutionary computation (CEC 07), pp 3523–3530. doi: 10.1109/CEC.2007.4424929
  51. 51.
    Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inf Sci 178(15):2985–2999MathSciNetCrossRefzbMATHGoogle Scholar
  52. 52.
    Yu Y, Xinjie Y (2007) Cooperative coevolutionary genetic algorithm for digital IIR filter design. IEEE Trans Ind Electron 54(3):1311–1318CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Department of Engineering ScienceUniversity WestTrollhättanSweden
  2. 2.Department of Signals and SystemsChalmers University of TechnologyGothenburgSweden

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