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A local branching heuristic for the open pit mine production scheduling problem

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  • Samavati, Mehran
  • Essam, Daryl
  • Nehring, Micah
  • Sarker, Ruhul

Abstract

This paper considers the well-known open pit mine production scheduling problem (OPMPSP). Given a discretisation of an orebody as a block model, this problem seeks a block extraction sequence that maximises the net present value (NPV) over a horizon of several periods. In practical applications, the number of blocks can be large, and therefore, the problem can be difficult to solve. It is even more challenging when it incorporates minimum resources requirements that are represented as lower bounds on resource constraints. In this study, we propose to tackle OPMPSP by using a novel metaheuristic technique known as local branching. To accelerate the search process, we combine local branching with a new adaptive branching scheme, and also develop a heuristic to quickly generate a starting feasible solution. Despite consideration of minimum requirements being seldom taken into account in the literature, this method yields near-optimal solutions for a series of data sets we have conceptually generated. To judge the performance of our methodology, the results are compared to those of two techniques from the literature, as well as to those obtained by a mixed integer linear programming (MILP) solver.

Suggested Citation

  • Samavati, Mehran & Essam, Daryl & Nehring, Micah & Sarker, Ruhul, 2017. "A local branching heuristic for the open pit mine production scheduling problem," European Journal of Operational Research, Elsevier, vol. 257(1), pages 261-271.
    • Handle: RePEc:eee:ejores:v:257:y:2017:i:1:p:261-271
    DOI: 10.1016/j.ejor.2016.07.004
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    References listed on IDEAS

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    1. Renaud Chicoisne & Daniel Espinoza & Marcos Goycoolea & Eduardo Moreno & Enrique Rubio, 2012. "A New Algorithm for the Open-Pit Mine Production Scheduling Problem," Operations Research, INFORMS, vol. 60(3), pages 517-528, June.
    2. W. Lambert & A. Newman, 2014. "Tailored Lagrangian Relaxation for the open pit block sequencing problem," Annals of Operations Research, Springer, vol. 222(1), pages 419-438, November.
    3. Jélvez, Enrique & Morales, Nelson & Nancel-Penard, Pierre & Peypouquet, Juan & Reyes, Patricio, 2016. "Aggregation heuristic for the open-pit block scheduling problem," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1169-1177.
    4. Ramazan, Salih, 2007. "The new Fundamental Tree Algorithm for production scheduling of open pit mines," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1153-1166, March.
    5. Underwood, Robert & Tolwinski, Boleslaw, 1998. "A mathematical programming viewpoint for solving the ultimate pit problem," European Journal of Operational Research, Elsevier, vol. 107(1), pages 96-107, May.
    6. Daniel Espinoza & Marcos Goycoolea & Eduardo Moreno & Alexandra Newman, 2013. "MineLib: a library of open pit mining problems," Annals of Operations Research, Springer, vol. 206(1), pages 93-114, July.
    7. Shishvan, Masoud Soleymani & Sattarvand, Javad, 2015. "Long term production planning of open pit mines by ant colony optimization," European Journal of Operational Research, Elsevier, vol. 240(3), pages 825-836.
    8. Guidong Zhu & Jonathan F. Bard & Gang Yu, 2006. "A Branch-and-Cut Procedure for the Multimode Resource-Constrained Project-Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 18(3), pages 377-390, August.
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    Cited by:

    1. Changyou Xu & Gang Chen & Huabo Lu & Qiuxia Zhang & Zhengke Liu & Jing Bian, 2024. "Integrated Optimization of Production Scheduling and Haulage Route Planning in Open-Pit Mines," Mathematics, MDPI, vol. 12(13), pages 1-24, July.
    2. Lin, Jingsi & Asad, Mohammad Waqar Ali & Topal, Erkan & Chang, Ping & Huang, Jinxin & Lin, Wei, 2024. "A novel model for sustainable production scheduling of an open-pit mining complex considering waste encapsulation," Resources Policy, Elsevier, vol. 91(C).
    3. Alipour, Aref & Khodaiari, Ali Asghar & Jafari, Ahmad & Tavakkoli-Moghaddam, Reza, 2022. "An integrated approach to open-pit mines production scheduling," Resources Policy, Elsevier, vol. 75(C).
    4. Filipe Rodrigues & Agostinho Agra & Lars Magnus Hvattum & Cristina Requejo, 2022. "Weighted iterated local branching for mathematical programming problems with binary variables," Journal of Heuristics, Springer, vol. 28(3), pages 329-350, June.
    5. Nancel-Penard, Pierre & Morales, Nelson & Cornillier, Fabien, 2022. "A recursive time aggregation-disaggregation heuristic for the multidimensional and multiperiod precedence-constrained knapsack problem: An application to the open-pit mine block sequencing problem," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1088-1099.
    6. Samavati, Mehran & Essam, Daryl & Nehring, Micah & Sarker, Ruhul, 2018. "A new methodology for the open-pit mine production scheduling problem," Omega, Elsevier, vol. 81(C), pages 169-182.
    7. Zeng, Lanyan & Liu, Shi Qiang & Kozan, Erhan & Corry, Paul & Masoud, Mahmoud, 2021. "A comprehensive interdisciplinary review of mine supply chain management," Resources Policy, Elsevier, vol. 74(C).
    8. Cinna Seifi & Marco Schulze & Jürgen Zimmermann, 2021. "Solution procedures for block selection and sequencing in flat-bedded potash underground mines," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(2), pages 409-440, June.
    9. Haonan, Zhou & Samavati, Mehran & Hill, Andrew J., 2021. "Heuristics for integrated blending optimisation in a mining supply chain," Omega, Elsevier, vol. 102(C).
    10. Samavati, Mehran & Essam, Daryl & Nehring, Micah & Sarker, Ruhul, 2017. "A methodology for the large-scale multi-period precedence-constrained knapsack problem: an application in the mining industry," International Journal of Production Economics, Elsevier, vol. 193(C), pages 12-20.
    11. Maenhout, Broos & Vanhoucke, Mario, 2018. "A perturbation matheuristic for the integrated personnel shift and task re-scheduling problem," European Journal of Operational Research, Elsevier, vol. 269(3), pages 806-823.

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