Logistics optimization for a coal supply chain

  • Gleb BelovEmail author
  • Natashia L. Boland
  • Martin W. P. Savelsbergh
  • Peter J. Stuckey


The Hunter Valley coal export supply chain in New South Wales, Australia, is of great importance to the Australian economy. Effectively managing its logistics, however, is challenging, because it is a complex system, covering a large geographic area and comprising a rail network, three coal terminals, and a port, and has many stakeholders, e.g., mining companies, port authorities, coal terminal operators, rail infrastructure providers, and above rail operators. We develop a matheuristic logistics planning system which integrates, amongst other concerns, train scheduling, stockpile management, and vessel scheduling. Different components of the supply chain are modeled at different levels of granularity. An extensive computational study has generated insights into the bottlenecks in the logistics system, which are used to guide changes in operating policies and future investments. The planning system uses a solver-independent modeling technology. This allowed us to observe differences between the performance of constraint programming and mixed-integer programming in the context of a rolling-horizon approach, due to custom search heuristics.


Scheduling Packing Solver-independent modeling Large neighborhood search Rolling horizon Custom search strategy Tidal constraints 



We would like to thank the strategic planning team at the HVCCC for many insightful and helpful suggestions, as well as to Opturion Ltd for providing their version of the CPX solver under an academic license.


  1. Aggoun, A., Beldiceanu, N.: Extending CHIP in order to solve complex scheduling and placement problems. Math. Comput. Model. 17(7), 57–73 (1993)CrossRefGoogle Scholar
  2. Belov, G., Stuckey, P.J., Tack, G., Wallace, M.: Improved linearization of Constraint Programming models. In: Rueher M (ed) Principles and Practice of Constraint Programming, pp. 49–65. Springer (2016)Google Scholar
  3. Belov, G., Boland, N., Savelsbergh, M.W.P., Stuckey, P.J.: Local search for a cargo assembly planning problem. In: Simonis, H. (ed.) Integration of AI and OR Techniques in Constraint Programming. Lecture Notes in Computer Science, vol. 8451, pp. 159–175. Springer, Berlin (2014)CrossRefGoogle Scholar
  4. Boland, N.L., Savelsbergh, M.W.P.: Optimizing the Hunter Valley coal chain. In: Gurnani, H., Mehrotra, A., Ray, S. (eds.) Supply Chain Disruptions, pp. 275–302. Springer, London (2012)CrossRefGoogle Scholar
  5. Boland, N., Savelsbergh, M., Waterer, H.: A decision support tool for generating shipping data for the Hunter Valley coal chain. Comput. Oper. Res. 53, 54–67 (2015)CrossRefGoogle Scholar
  6. Copil, K., Wörbelauer, M., Meyr, H., Tempelmeier, H.: Simultaneous lotsizing and scheduling problems: a classification and review of models. OR Spectrum 39(1), 1–64 (2017)MathSciNetCrossRefGoogle Scholar
  7. Ernst, A.T., Oğuz, C., Singh, G., Taherkhani, G.: Mathematical models for the berth allocation problem in dry bulk terminals. J. Sched. 20(5), 459–473 (2017)MathSciNetCrossRefGoogle Scholar
  8. Fattahi, M., Govindan, K., Keyvanshokooh, E.: A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands. Comput. Oper. Res. 100, 314–332 (2018)MathSciNetCrossRefGoogle Scholar
  9. Fung, J., Singh, G., Zinder, Y.: Capacity planning in supply chains of mineral resources. Inf. Sci. 316, 397–418 (2015)CrossRefGoogle Scholar
  10. Google: OR-Tools– Google optimization tools (2019).
  11. Gurobi Optimization, Inc: Gurobi optimizer reference manual (2019).
  12. HVCCC: HVCC declared capacity (2014)Google Scholar
  13. HVCCC: Hunter Valley Coal Chain Coordinator’s website (2019). Accessed 27 Aug 2019
  14. IBM Software: IBM ILOG CPLEX optimizer (2019)Google Scholar
  15. Joslin, D.E., Clements, D.P.: “Squeaky Wheel” optimization. J. Artif. Int. Res. 10(1), 353–373 (1999)MathSciNetzbMATHGoogle Scholar
  16. Kalinowski, T., Kapoor, R., Savelsbergh, M.W.P.: Scheduling reclaimers serving a stock pad at a coal terminal. J. Sched. 20(1), 85–101 (2017)MathSciNetCrossRefGoogle Scholar
  17. Kelareva, E., Tierney, K., Kilby, P.: CP methods for scheduling and routing with time-dependent task costs. EURO J. Comput. Optim. 2(3), 147–194 (2014)CrossRefGoogle Scholar
  18. Leite, J.M.L.G., Arruda, E.F., Bahiense, L., Marujo, L.G.: Modeling the integrated mine-to-client supply chain: a survey. Int. J. Min. Reclam. Environ. 1–47 (2019).
  19. Menezes, G.C., Mateus, G.R., Ravetti, M.G.: A branch and price algorithm to solve the integrated production planning and scheduling in bulk ports. Eur. J. Oper. Res. 258, 926–937 (2017)MathSciNetCrossRefGoogle Scholar
  20. Moons, S., Ramaekers, K., Caris, A., Arda, Y.: Integrating production scheduling and vehicle routing decisions at the operational decision level: a review and discussion. Comput. Ind. Eng. 104, 224–245 (2017)CrossRefGoogle Scholar
  21. Nethercote, N., Stuckey, P., Becket, R., Brand, S., Duck, G., Tack, G.: MiniZinc: Towards a standard CP modelling language. In: Bessiere, C. (ed.) Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, vol. 4741, pp. 529–543. Springer, LNCS (2007)Google Scholar
  22. Ohrimenko, O., Stuckey, P., Codish, M.: Propagation via lazy clause generation. Constraints 14(3), 357–391 (2009)MathSciNetCrossRefGoogle Scholar
  23. Opturion Pty Ltd: Opturion CPX user’s guide: version 1.0.2. (2013). Accessed 22 May 2015
  24. Peng, H., Zhou, M., Liu, M., Zhang, Y., Huang, Y.: A dynamic optimization model of an integrated coal supply chain system and its application. Min. Sci. Technol. (China) 19(6), 842–846 (2009)CrossRefGoogle Scholar
  25. Reisi Ardali, M.: Optimising throughput in the Hunter Valley coal chain using integer programming techniques. PhD thesis, University of Newcastle, Australia (2015)Google Scholar
  26. Rocha de Paula, M., Boland, N., Ernst, A.T., Mendes, A., Savelsbergh, M.: Throughput optimisation in a coal export system with multiple terminals and shared resources. Comput. Ind. Eng. 134, 37–51 (2019)CrossRefGoogle Scholar
  27. Sabet, E., Yazdani, B., Kian, R., Galanakis, K.: A strategic and global manufacturing capacity management optimisation model: a scenario-based multi-stage stochastic programming approach. Omega pp. 1–20 (2019).
  28. Savelsbergh, M.W.P., Smith, O.: Cargo assembly planning. Eur. J. Transp. Sci. Logist. 4, 321–354 (2015)CrossRefGoogle Scholar
  29. Schulte, C., Tack, G., Lagerkvist, M.Z.: Modeling and programming with Gecode (2019). Accessed 8 Jan 2020
  30. Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.: Explaining the cumulative propagator. Constraints 16(3), 250–282 (2011)MathSciNetCrossRefGoogle Scholar
  31. Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: International Conference on Principles and Practice of Constraint Programming, pp. 417–431. Springer (1998)Google Scholar
  32. Singh, G., Sier, D., Ernst, A.T., Gavriliouk, O., Oyston, R., Giles, T., Welgama, P.: A mixed integer programming model for long term capacity expansion planning: a case study from The Hunter Valley Coal Chain. Eur. J. Oper. Res. 220(1), 210–224 (2012)MathSciNetCrossRefGoogle Scholar
  33. Singh, G., Ernst, A.T., Baxter, M., Sier, D.: Rail schedule optimisation in the Hunter Valley coal chain. RAIRO-Oper Res 49(2), 413–434 (2015)MathSciNetCrossRefGoogle Scholar
  34. Stuckey, P.J., Feydy, T., Schutt, A., Tack, G., Fischer, J.: The MiniZinc Challenge 2008–2013. AI Mag. 35(2), 55–60 (2014)CrossRefGoogle Scholar
  35. Unsal, O., Oguz, C.: An exact algorithm for integrated planning of operations in dry bulk terminals. Transp. Res. Part E Logist. Transp. Rev. 126(C), 103–121 (2019)CrossRefGoogle Scholar
  36. Xie, F., Potts, C.N., Bektaş, T.: Iterated local search for workforce scheduling and routing problems. J. Heuristics 23(6), 471–500 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia
  2. 2.Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations