An Optimization Model for 3D Pipe Routing with Flexibility Constraints

  • Gleb BelovEmail author
  • Tobias Czauderna
  • Amel Dzaferovic
  • Maria Garcia de la Banda
  • Michael Wybrow
  • Mark Wallace
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10416)


Optimizing the layout of the equipment and connecting pipes that form a chemical plant is an important problem, where the aim is to minimize the total cost of the plant while ensuring its safety and correct operation. The complexity of this problem is such that it is still solved manually, taking multiple engineers several years to complete. Most research in this area focuses on the simpler subproblem of placing the equipment, while the approaches that take pipe routing into account are either based on heuristics or do not consider sufficiently realistic scenarios. Our work presents a new model of the pipe routing subproblem that integrates realistic requirements, such as flexibility constraints, and aims for optimality while solving the largest problem instance considered in the literature. The model is being developed in collaboration with Woodside Energy Ltd. for their Liquefied Natural Gas plants, and is implemented in the high-level modeling language MiniZinc. The use of MiniZinc has both reduced the amount of time required to develop the model, and allowed us to easily experiment with different solvers.



This research was funded by Woodside Energy Ltd. We thank all our Woodside collaborators, particularly Solomon Faka, for the many useful discussions, as well as for the enlightening visit to their LNG plant.


  1. 1.
    Beldiceanu, N., Carlsson, M., Demassey, S., Petit, T.: Global constraint catalogue: past, present and future. Constraints 12(1), 21–62 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Belov, G., Stuckey, P.J., Tack, G., Wallace, M.: Improved linearization of constraint programming models. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 49–65. Springer, Cham (2016). doi: 10.1007/978-3-319-44953-1_4 CrossRefGoogle Scholar
  3. 3.
    Chu, G., Stuckey, P.J., Schutt, A., Ehlers, T., Gange, G., Francis, K.: Chuffed – a lazy clause solver (2017). Accessed 23 Mar 2017
  4. 4.
    Chu, G.G.: Improving combinatorial optimization. Ph.D. thesis (2011)Google Scholar
  5. 5.
    M.W. Kellogg Company: Design of Piping Systems. Wiley series in Chemical Engineering. Wiley, Hoboken (1956)Google Scholar
  6. 6.
    de Berg, M., van Kreveld, M., Nilsson, B.J., Overmars, M.: Shortest path queries in rectilinear worlds. Int. J. Comput. Geom. Appl. 02(03), 287–309 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Furuholmen, M., Glette, K., Hovin, M., Torresen, J.: Evolutionary approaches to the three-dimensional multi-pipe routing problem: a comparative study using direct encodings. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 71–82. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-12139-5_7 CrossRefGoogle Scholar
  8. 8.
    Google: Google optimization tools (2017).
  9. 9.
    Guirardello, R., Swaney, R.E.: Optimization of process plant layout with pipe routing. Comput. Chem. Eng. 30(1), 99–114 (2005)CrossRefGoogle Scholar
  10. 10.
    Gurobi Optimization, Inc.: Gurobi Optimizer Reference Manual Version 7.0. Houston. Gurobi Optimization, Texas (2016)Google Scholar
  11. 11.
    IBM: IBM ILOG CPLEX Optimization Studio. CPLEX User’s Manual (2017)Google Scholar
  12. 12.
    Jiang, W.-Y., Lin, Y., Chen, M., Yu, Y.-Y.: A co-evolutionary improved multi-ant colony optimization for ship multiple and branch pipe route design. Ocean Eng. 102, 63–70 (2015)CrossRefGoogle Scholar
  13. 13.
    Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-74970-7_38 CrossRefGoogle Scholar
  14. 14.
    Padberg, M.: Packing small boxes into a big box. Math. Methods Oper. Res. 52(1), 1–21 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Sakti, A., Zeidner, L., Hadzic, T., Rock, B.S., Quartarone, G.: Constraint programming approach for spatial packaging problem. In: Quimper, C.-G. (ed.) CPAIOR 2016. LNCS, vol. 9676, pp. 319–328. Springer, Cham (2016). doi: 10.1007/978-3-319-33954-2_23 Google Scholar
  16. 16.
    Schulte, C., Tack, G., Lagerkvist, M.Z.: Modeling and programming with Gecode (2017).
  17. 17.
    Simonis, H., O’Sullivan, B.: Search strategies for rectangle packing. In: Stuckey, P.J. (ed.) CP 2008. LNCS, vol. 5202, pp. 52–66. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85958-1_4 CrossRefGoogle Scholar
  18. 18.
    Stuckey, P.J., Becket, R., Fischer, J.: Philosophy of the MiniZinc challenge. Constraints 15(3), 307–316 (2010)CrossRefzbMATHGoogle Scholar
  19. 19.
    Xu, G., Papageorgiou, L.G.: A construction-based approach to process plant layout using mixed-integer optimization. Ind. Eng. Chem. Res. 46(1), 351–358 (2007)CrossRefGoogle Scholar
  20. 20.
    Xu, G., Papageorgiou, L.G.: Process plant layout using an improvement-type algorithm. Chem. Eng. Res. Des. 87(6), 780–788 (2009)CrossRefGoogle Scholar
  21. 21.
    Zhu, D., Latombe, J.C.: Pipe routing-path planning (with many constraints). In: Proceedings of 1991 IEEE International Conference on Robotics and Automation, vol. 3, pp. 1940–1947 (1991)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gleb Belov
    • 1
    Email author
  • Tobias Czauderna
    • 1
  • Amel Dzaferovic
    • 2
  • Maria Garcia de la Banda
    • 1
  • Michael Wybrow
    • 1
  • Mark Wallace
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityMelbourneAustralia
  2. 2.Woodside Energy Ltd.PerthAustralia

Personalised recommendations