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Logistics optimization for a coal supply chain

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Abstract

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.

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Notes

  1. We adjusted the parameters of our model accordingly to compute the results published in (Rocha de Paula et al. 2019). LNS was switched on.

References

  • Aggoun, A., Beldiceanu, N.: Extending CHIP in order to solve complex scheduling and placement problems. Math. Comput. Model. 17(7), 57–73 (1993)

    Article  Google Scholar 

  • 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)

  • 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)

    Chapter  Google Scholar 

  • 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)

    Chapter  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • Fung, J., Singh, G., Zinder, Y.: Capacity planning in supply chains of mineral resources. Inf. Sci. 316, 397–418 (2015)

    Article  Google Scholar 

  • Google: OR-Tools– Google optimization tools (2019). https://github.com/google/or-tools

  • Gurobi Optimization, Inc: Gurobi optimizer reference manual (2019). http://www.gurobi.com

  • HVCCC: HVCC declared capacity (2014)

  • HVCCC: Hunter Valley Coal Chain Coordinator’s website (2019). http://www.hvccc.com.au/. Accessed 27 Aug 2019

  • IBM Software: IBM ILOG CPLEX optimizer (2019)

  • Joslin, D.E., Clements, D.P.: “Squeaky Wheel” optimization. J. Artif. Int. Res. 10(1), 353–373 (1999)

    MathSciNet  MATH  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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). https://doi.org/10.1080/17480930.2019.1579693

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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)

  • Ohrimenko, O., Stuckey, P., Codish, M.: Propagation via lazy clause generation. Constraints 14(3), 357–391 (2009)

    Article  MathSciNet  Google Scholar 

  • Opturion Pty Ltd: Opturion CPX user’s guide: version 1.0.2. (2013). www.opturion.com/cpx.html. Accessed 22 May 2015

  • 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)

    Article  Google Scholar 

  • Reisi Ardali, M.: Optimising throughput in the Hunter Valley coal chain using integer programming techniques. PhD thesis, University of Newcastle, Australia (2015)

  • 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)

    Article  Google Scholar 

  • 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). https://doi.org/10.1016/j.omega.2019.01.004

  • Savelsbergh, M.W.P., Smith, O.: Cargo assembly planning. Eur. J. Transp. Sci. Logist. 4, 321–354 (2015)

    Article  Google Scholar 

  • Schulte, C., Tack, G., Lagerkvist, M.Z.: Modeling and programming with Gecode (2019). www.gecode.org. Accessed 8 Jan 2020

  • Schutt, A., Feydy, T., Stuckey, P.J., Wallace, M.G.: Explaining the cumulative propagator. Constraints 16(3), 250–282 (2011)

    Article  MathSciNet  Google Scholar 

  • 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)

  • 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)

    Article  MathSciNet  Google Scholar 

  • 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)

    Article  MathSciNet  Google Scholar 

  • Stuckey, P.J., Feydy, T., Schutt, A., Tack, G., Fischer, J.: The MiniZinc Challenge 2008–2013. AI Mag. 35(2), 55–60 (2014)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Xie, F., Potts, C.N., Bektaş, T.: Iterated local search for workforce scheduling and routing problems. J. Heuristics 23(6), 471–500 (2017)

    Article  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, VIC, Australia

    Gleb Belov & Peter J. Stuckey

  2. Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA

    Natashia L. Boland & Martin W. P. Savelsbergh

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  1. Gleb Belov
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  2. Natashia L. Boland
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  3. Martin W. P. Savelsbergh
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  4. Peter J. Stuckey
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Correspondence to Gleb Belov.

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The research presented here was supported by ARC linkage Grant LP110200524.

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Belov, G., Boland, N.L., Savelsbergh, M.W.P. et al. Logistics optimization for a coal supply chain. J Heuristics 26, 269–300 (2020). https://doi.org/10.1007/s10732-019-09435-8

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  • DOI: https://doi.org/10.1007/s10732-019-09435-8

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