{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:18:06Z","timestamp":1770337086632,"version":"3.49.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Sciences and Engineering Research Council (NSERC) of Canada CRD","award":["CRDPJ 500414-16"],"award-info":[{"award-number":["CRDPJ 500414-16"]}]},{"name":"NSERC Discovery Grant","award":["239019"],"award-info":[{"award-number":["239019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A mining complex is an integrated value chain where the materials extracted from a group of mineral deposits are sent to different processing streams to produce sellable products. A major short-term decision in a mining complex is to determine the flow of materials that first includes deciding which handling facilities to send the extracted materials and then determining how to utilize the processing facilities. The flow of materials through the mining complex is significantly dependent on the performance of and interaction between its different components. New digital technologies, including the development of advanced sensors and monitoring devices, have enabled a mining complex to acquire new information about the performance of its different components. This paper proposes a new continuous updating framework that combines policy gradient reinforcement learning and an extended ensemble Kalman filter to adapt the short-term flow of materials in a mining complex with incoming information. The framework first uses a new extended ensemble Kalman filter to update the uncertainty models of the different components of a mining complex with new incoming information. Then, the updated uncertainty models are fed to a neural network trained using a policy gradient reinforcement learning algorithm to adapt the short-term flow of materials in a mining complex. The proposed framework is applied to a copper mining complex and shows its ability to efficiently adapt the short-term flow of materials in an operational mining environment with new incoming information. The framework better meets the different production targets while improving the cumulative cash flow compared to industry standard approaches.<\/jats:p>","DOI":"10.1007\/s10845-020-01562-5","type":"journal-article","created":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T14:02:36Z","timestamp":1585836156000},"page":"1795-1811","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Adaptive self-learning mechanisms for updating short-term production decisions in an industrial mining complex"],"prefix":"10.1007","volume":"31","author":[{"given":"Ashish","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roussos","family":"Dimitrakopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Maulen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"issue":"6","key":"1562_CR1","doi-asserted-by":"publisher","first-page":"2513","DOI":"10.1007\/s10845-011-0580-y","volume":"23","author":"N Aissani","year":"2012","unstructured":"Aissani, N., Bekrar, A., Trentesaux, D., & Beldjilali, B. (2012). Dynamic scheduling for multi-site companies: A decisional approach based on reinforcement multi-agent learning. Journal of Intelligent Manufacturing, 23(6), 2513\u20132529. https:\/\/doi.org\/10.1007\/s10845-011-0580-y.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1562_CR2","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.resourpol.2016.05.005","volume":"49","author":"MWA Asad","year":"2016","unstructured":"Asad, M. W. A., Qureshi, M. A., & Jang, H. (2016). A review of cut-off grade policy models for open pit mining operations. Resources Policy, 49, 142\u2013152. https:\/\/doi.org\/10.1016\/j.resourpol.2016.05.005.","journal-title":"Resources Policy"},{"issue":"1","key":"1562_CR3","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s10845-016-1237-7","volume":"30","author":"SRA Barde","year":"2019","unstructured":"Barde, S. R. A., Yacout, S., & Shin, H. (2019). Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks. Journal of Intelligent Manufacturing, 30(1), 147\u2013161. https:\/\/doi.org\/10.1007\/s10845-016-1237-7.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"5","key":"1562_CR4","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s11004-014-9561-y","volume":"47","author":"J Benndorf","year":"2015","unstructured":"Benndorf, J. (2015). Making use of online production data: Sequential updating of mineral resource models. Mathematical Geosciences, 47(5), 547\u2013563. https:\/\/doi.org\/10.1007\/s11004-014-9561-y.","journal-title":"Mathematical Geosciences"},{"issue":"1","key":"1562_CR5","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1080\/14749009.2015.1107342","volume":"125","author":"J Benndorf","year":"2016","unstructured":"Benndorf, J., & Buxton, M. W. N. (2016). Sensor-based real-time resource model reconciliation for improved mine production control-a conceptual framework. Mining Technology, 125(1), 54\u201364. https:\/\/doi.org\/10.1080\/14749009.2015.1107342.","journal-title":"Mining Technology"},{"issue":"5","key":"1562_CR6","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1080\/17480930.2018.1448248","volume":"33","author":"M Blom","year":"2019","unstructured":"Blom, M., Pearce, A. R., & Stuckey, P. J. (2019). Short-term planning for open pit mines: A review. International Journal of Mining, Reclamation and Environment, 33(5), 318\u2013339. https:\/\/doi.org\/10.1080\/17480930.2018.1448248.","journal-title":"International Journal of Mining, Reclamation and Environment"},{"key":"1562_CR7","doi-asserted-by":"publisher","unstructured":"Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of the COMPSTAT\u20192010 (pp. 177\u2013186). https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"issue":"3","key":"1562_CR8","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1023\/A:1008995707211","volume":"10","author":"A Brewer","year":"1999","unstructured":"Brewer, A., Nancy, S., & Thomas, L. (1999). Intelligent tracking in manufacturing. Journal of Intelligent Manufacturing, 10(3), 245\u2013250. https:\/\/doi.org\/10.1023\/A:1008995707211.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"1","key":"1562_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11004-011-9376-z","volume":"44","author":"Y Chen","year":"2012","unstructured":"Chen, Y., & Oliver, D. S. (2012). Ensemble randomized maximum likelihood method as an iterative ensemble smoother. Mathematical Geosciences, 44(1), 1\u201326. https:\/\/doi.org\/10.1007\/s11004-011-9376-z.","journal-title":"Mathematical Geosciences"},{"issue":"7","key":"1562_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11004-018-9758-6","volume":"51","author":"M Dalm","year":"2018","unstructured":"Dalm, M., Buxton, M. W. N., & van Ruitenbeek, F. J. A. (2018). Ore\u2013waste discrimination in epithermal deposits using near-infrared to short-wavelength infrared (NIR-SWIR) hyperspectral imagery. Mathematical Geosciences, 51(7), 1\u201327. https:\/\/doi.org\/10.1007\/s11004-018-9758-6.","journal-title":"Mathematical Geosciences"},{"key":"1562_CR11","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.mineng.2013.12.016","volume":"58","author":"M Dalm","year":"2014","unstructured":"Dalm, M., Buxton, M. W. N., Van Ruitenbeek, F. J. A., & Voncken, J. H. L. (2014). Application of near-infrared spectroscopy to sensor based sorting of a porphyry copper ore. Minerals Engineering, 58, 7\u201316. https:\/\/doi.org\/10.1016\/j.mineng.2013.12.016.","journal-title":"Minerals Engineering"},{"issue":"8","key":"1562_CR12","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1023\/A:1007570402430","volume":"32","author":"AJ Desbarats","year":"2000","unstructured":"Desbarats, A. J., & Dimitrakopoulos, R. (2000). Geostatistical simulation of regionalized pore-size distributions using min\/max autocorrelation factors. Mathematical Geology, 32(8), 919\u2013942. https:\/\/doi.org\/10.1023\/A:1007570402430.","journal-title":"Mathematical Geology"},{"issue":"1","key":"1562_CR13","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1179\/mnt.2002.111.1.82","volume":"111","author":"R Dimitrakopoulos","year":"2002","unstructured":"Dimitrakopoulos, R., Farrelly, C. T., & Godoy, M. (2002). Moving forward from traditional optimisation: Grade uncertainty and risk effects in open pit design. Mining Technology, 111(1), 82\u201388. https:\/\/doi.org\/10.1179\/mnt.2002.111.1.82.","journal-title":"Mining Technology"},{"issue":"2","key":"1562_CR14","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1179\/1743286314Y.0000000062","volume":"123","author":"R Dimitrakopoulos","year":"2014","unstructured":"Dimitrakopoulos, R., & Godoy, M. (2014). Grade control based on economic ore\/waste classification functions and stochastic simulations: Examples, comparisons and applications. Mining Technology, 123(2), 90\u2013106. https:\/\/doi.org\/10.1179\/1743286314Y.0000000062.","journal-title":"Mining Technology"},{"issue":"5","key":"1562_CR15","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1023\/B:MATG.0000037737.11615.df","volume":"36","author":"R Dimitrakopoulos","year":"2004","unstructured":"Dimitrakopoulos, R., & Luo, X. (2004). Generalized sequential Gaussian simulation on group size v and screen-effect approximations for large field simulations. Mathematical Geology, 36(5), 567\u2013590. https:\/\/doi.org\/10.1023\/B:MATG.0000037737.11615.df.","journal-title":"Mathematical Geology"},{"issue":"2","key":"1562_CR16","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s10596-010-9205-3","volume":"15","author":"L Dovera","year":"2011","unstructured":"Dovera, L., & Della Rossa, E. (2011). Multimodal ensemble Kalman filtering using Gaussian mixture models. Computational Geosciences, 15(2), 307\u2013323. https:\/\/doi.org\/10.1007\/s10596-010-9205-3.","journal-title":"Computational Geosciences"},{"issue":"C5","key":"1562_CR17","doi-asserted-by":"publisher","first-page":"10143","DOI":"10.1029\/94JC00572","volume":"99","author":"G Evensen","year":"1994","unstructured":"Evensen, G., Carlo, M., & Carlo, M. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143\u201310162.","journal-title":"Journal of Geophysical Research: Oceans"},{"key":"1562_CR18","unstructured":"Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th international conference on artificial intelligence and statistics (pp. 249\u2013256). http:\/\/proceedings.mlr.press\/v9\/glorot10a.html"},{"issue":"5","key":"1562_CR19","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.mineng.2008.12.013","volume":"22","author":"AFH Goetz","year":"2009","unstructured":"Goetz, A. F. H., Curtiss, B., & Shiley, D. A. (2009). Rapid gangue mineral concentration measurement over conveyors by NIR reflectance spectroscopy. Minerals Engineering, 22(5), 490\u2013499. https:\/\/doi.org\/10.1016\/j.mineng.2008.12.013.","journal-title":"Minerals Engineering"},{"key":"1562_CR20","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.asoc.2015.11.038","volume":"40","author":"R Goodfellow","year":"2016","unstructured":"Goodfellow, R., & Dimitrakopoulos, R. (2016). Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing, 40, 292\u2013304. https:\/\/doi.org\/10.1016\/j.asoc.2015.11.038.","journal-title":"Applied Soft Computing"},{"issue":"3","key":"1562_CR21","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s11004-017-9680-3","volume":"49","author":"R Goodfellow","year":"2017","unstructured":"Goodfellow, R., & Dimitrakopoulos, R. (2017). Simultaneous stochastic optimization of mining complexes and mineral value chains. Mathematical Geosciences, 49(3), 341\u2013360. https:\/\/doi.org\/10.1007\/s11004-017-9680-3.","journal-title":"Mathematical Geosciences"},{"key":"1562_CR22","unstructured":"Hinton, G. E., Srivastava, N., & Swersky, K. (2012). Neural netwrok for machine learning-Lecture 6a: Overview of mini-batch gradient descent. Retrieved January 1, 2016, from http:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf."},{"issue":"1","key":"1562_CR23","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s12182-014-0005-6","volume":"12","author":"J Hou","year":"2015","unstructured":"Hou, J., Zhou, K., Zhang, X. S., Kang, X. D., & Xie, H. (2015). A review of closed-loop reservoir management. Petroleum Science, 12(1), 114\u2013128. https:\/\/doi.org\/10.1007\/s12182-014-0005-6.","journal-title":"Petroleum Science"},{"key":"1562_CR24","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.mineng.2015.10.020","volume":"85","author":"S Iyakwari","year":"2016","unstructured":"Iyakwari, S., Glass, H. J., Rollinson, G. K., & Kowalczuk, P. B. (2016). Application of near infrared sensors to preconcentration of hydrothermally-formed copper ore. Minerals Engineering, 85, 148\u2013167. https:\/\/doi.org\/10.1016\/j.mineng.2015.10.020.","journal-title":"Minerals Engineering"},{"issue":"2","key":"1562_CR25","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.cageo.2010.04.008","volume":"37","author":"A Jewbali","year":"2011","unstructured":"Jewbali, A., & Dimitrakopoulos, R. (2011). Implementation of conditional simulation by successive residuals. Computers & Geosciences, 37(2), 129\u2013142. https:\/\/doi.org\/10.1016\/j.cageo.2010.04.008.","journal-title":"Computers & Geosciences"},{"key":"1562_CR26","doi-asserted-by":"publisher","unstructured":"Kargupta, H., Srakar, K., & Gilligan, M. (2010). MineFleet\u00ae: An overview of a widely adopted distributed vehicle performance data mining system. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 37\u201346). https:\/\/doi.org\/10.1145\/1835804.1835812.","DOI":"10.1145\/1835804.1835812"},{"issue":"2","key":"1562_CR27","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1109\/TIE.2004.825263","volume":"51","author":"WG Koellner","year":"2004","unstructured":"Koellner, W. G., Brown, G. M., Rodr\u00edguez, J., Pontt, J., Cort\u00e9s, P., & Miranda, H. (2004). Recent advances in mining haul trucks. IEEE Transactions on Industrial Electronics, 51(2), 321\u2013329. https:\/\/doi.org\/10.1109\/TIE.2004.825263.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"1","key":"1562_CR28","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s11004-018-9762-x","volume":"51","author":"D Kumar","year":"2019","unstructured":"Kumar, D., & Srinivasan, S. (2019). Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics. Mathematical Geosciences, 51(1), 75\u2013107. https:\/\/doi.org\/10.1007\/s11004-018-9762-x.","journal-title":"Mathematical Geosciences"},{"issue":"3","key":"1562_CR29","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11004-017-9676-z","volume":"49","author":"A Lamghari","year":"2017","unstructured":"Lamghari, A. (2017). Mine planning and oil field development: A survey and research potentials. Mathematical Geosciences, 49(3), 395\u2013437. https:\/\/doi.org\/10.1007\/s11004-017-9676-z.","journal-title":"Mathematical Geosciences"},{"key":"1562_CR30","unstructured":"Lane, K. F. (1984). Cutoff grades for two minerals. In Proceedings of the 18th international symposium on application of computers and operations research in mineral the industries (pp. 485\u2013492)."},{"key":"1562_CR31","volume-title":"The economic definition of ore: Cut-off grades in theory and practice","author":"KF Lane","year":"1988","unstructured":"Lane, K. F. (1988). The economic definition of ore: Cut-off grades in theory and practice. London: Mining Journal Books Limited."},{"key":"1562_CR32","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.resourpol.2018.11.004","volume":"62","author":"NL Mai","year":"2019","unstructured":"Mai, N. L., Topal, E., Erten, O., & Sommerville, B. (2019). A new risk-based optimisation method for the iron ore production scheduling using stochastic integer programming. Resources Policy, 62, 571\u2013579. https:\/\/doi.org\/10.1016\/j.resourpol.2018.11.004.","journal-title":"Resources Policy"},{"issue":"3","key":"1562_CR33","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.ejor.2016.05.050","volume":"255","author":"MEV Matamoros","year":"2016","unstructured":"Matamoros, M. E. V., & Dimitrakopoulos, R. (2016). Stochastic short-term mine production schedule accounting for fleet allocation, operational considerations and blending restrictions. European Journal of Operational Research, 255(3), 911\u2013921. https:\/\/doi.org\/10.1016\/j.ejor.2016.05.050.","journal-title":"European Journal of Operational Research"},{"key":"1562_CR34","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., et al. (2013). Playing Atari with deep reinforcement learning. arXiv preprint, 1312.5602. http:\/\/arxiv.org\/abs\/1312.5602."},{"issue":"1","key":"1562_CR35","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.ejor.2015.05.002","volume":"247","author":"L Montiel","year":"2015","unstructured":"Montiel, L., & Dimitrakopoulos, R. (2015). Optimizing mining complexes with multiple processing and transportation alternatives: An uncertainty-based approach. European Journal of Operational Research, 247(1), 166\u2013178. https:\/\/doi.org\/10.1016\/j.ejor.2015.05.002.","journal-title":"European Journal of Operational Research"},{"issue":"5","key":"1562_CR36","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s10732-017-9349-6","volume":"23","author":"L Montiel","year":"2017","unstructured":"Montiel, L., & Dimitrakopoulos, R. (2017). A heuristic approach for the stochastic optimization of mine production schedules. Journal of Heuristics, 23(5), 397\u2013415. https:\/\/doi.org\/10.1007\/s10732-017-9349-6.","journal-title":"Journal of Heuristics"},{"issue":"12","key":"1562_CR37","doi-asserted-by":"publisher","first-page":"48","DOI":"10.19150\/me.8645","volume":"70","author":"L Montiel","year":"2018","unstructured":"Montiel, L., & Dimitrakopoulos, R. (2018). Simultaneous stochastic: Optimization of production scheduling at Twin Creeks mining complex, Nevada. Mining Enginnering, 70(12), 48\u201356.","journal-title":"Mining Enginnering"},{"key":"1562_CR38","unstructured":"Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (pp. 807\u2013814)."},{"key":"1562_CR39","doi-asserted-by":"publisher","unstructured":"Nguyen, D., & Bui, X. (2015). A real-time regulation model in multi-agent decision support system for open pit mining. In Proceedings of the 12th international symposium continuous surface mining-Aachen (pp. 255\u2013262). https:\/\/doi.org\/10.1007\/978-3-319-12301-1.","DOI":"10.1007\/978-3-319-12301-1"},{"issue":"1","key":"1562_CR40","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1080\/14749009.2017.1341142","volume":"127","author":"C Paduraru","year":"2018","unstructured":"Paduraru, C., & Dimitrakopoulos, R. (2018). Adaptive policies for short-term material flow optimization in a mining complex. Mining Technology, 127(1), 56\u201363. https:\/\/doi.org\/10.1080\/14749009.2017.1341142.","journal-title":"Mining Technology"},{"issue":"3","key":"1562_CR41","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/25726668.2019.1577596","volume":"128","author":"C Paduraru","year":"2019","unstructured":"Paduraru, C., & Dimitrakopoulos, R. (2019). Responding to new information in a mining complex: Fast mechanisms using machine learning. Mining Technology, 128(3), 129\u2013142. https:\/\/doi.org\/10.1080\/25726668.2019.1577596.","journal-title":"Mining Technology"},{"issue":"3","key":"1562_CR42","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s10596-015-9540-5","volume":"20","author":"M Panzeri","year":"2016","unstructured":"Panzeri, M., Della Rossa, E. L., Dovera, L., Riva, M., & Guadagnini, A. (2016). Integration of Markov mesh models and data assimilation techniques in complex reservoirs. Computational Geosciences, 20(3), 637\u2013653. https:\/\/doi.org\/10.1007\/s10596-015-9540-5.","journal-title":"Computational Geosciences"},{"key":"1562_CR43","doi-asserted-by":"publisher","DOI":"10.1080\/17480930.2019.1658923","author":"M Quigley","year":"2019","unstructured":"Quigley, M., & Dimitrakopoulos, R. (2019). Incorporating geological and equipment performance uncertainty while optimizing short-term mine production schedules. International Journal of Mining, Reclamation and Environment. https:\/\/doi.org\/10.1080\/17480930.2019.1658923.","journal-title":"International Journal of Mining, Reclamation and Environment"},{"key":"1562_CR44","volume-title":"An introduction to cut-off grade estimation","author":"J-M Rendu","year":"2014","unstructured":"Rendu, J.-M. (2014). An introduction to cut-off grade estimation. Englewood, CO: Society for Mining, Metallurgy & Exploration."},{"key":"1562_CR45","unstructured":"Rosa, L., David, Valery, W., Wortley, M., Ozkocak, T., & Pike, M. (2007). The use of radio frequency ID tags to track ore in mining operations. In Proceedings of the 33rd application of computers and operations research in the mineral Industries (pp. 601\u2013606)."},{"key":"1562_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-5717-5_1","volume-title":"Mineral resource estimation","author":"ME Rossi","year":"2013","unstructured":"Rossi, M. E., & Deutsch, C. V. (2013). Mineral resource estimation. New York: Springer. https:\/\/doi.org\/10.1007\/978-1-4020-5717-5_1."},{"key":"1562_CR47","doi-asserted-by":"publisher","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. https:\/\/doi.org\/10.1111\/j.0006-341X.1999.00591.x.","DOI":"10.1111\/j.0006-341X.1999.00591.x"},{"issue":"1","key":"1562_CR48","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10596-005-9009-z","volume":"10","author":"P Sarma","year":"2006","unstructured":"Sarma, P., Durlofsky, L. J., Aziz, K., & Chen, W. H. (2006). Efficient real-time reservoir management using adjoint-based optimal control and model updating. Computational Geosciences, 10(1), 3\u201336. https:\/\/doi.org\/10.1007\/s10596-005-9009-z.","journal-title":"Computational Geosciences"},{"key":"1562_CR49","unstructured":"Shirangi, M. G. (2017). Advanced techniques for closed-loop reservoir optimization under uncertainty (Doctoral dissertation). Stanford: Stanford University."},{"issue":"7587","key":"1562_CR50","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484\u2013489. https:\/\/doi.org\/10.1038\/nature16961.","journal-title":"Nature"},{"key":"1562_CR51","unstructured":"Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. In Proceedings of the advances in neural information processing systems (pp. 1057\u20131063). http:\/\/papers.nips.cc\/paper\/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf."},{"issue":"5","key":"1562_CR52","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1023\/A:1016099029432","volume":"34","author":"JA Vargas-Guzm\u00e1n","year":"2002","unstructured":"Vargas-Guzm\u00e1n, J. A., & Dimitrakopoulos, R. (2002). Conditional simulation of random fields by successive residuals. Mathematical Geology, 34(5), 597\u2013611. https:\/\/doi.org\/10.1023\/A:1016099029432.","journal-title":"Mathematical Geology"},{"issue":"5","key":"1562_CR53","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s11004-005-6660-9","volume":"37","author":"G Verly","year":"2005","unstructured":"Verly, G. (2005). Grade control classification of ore and waste: A critical review of estimation and simulation based procedures. Mathematical Geology, 37(5), 451\u2013475. https:\/\/doi.org\/10.1007\/s11004-005-6660-9.","journal-title":"Mathematical Geology"},{"issue":"7","key":"1562_CR54","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s11004-014-9541-2","volume":"46","author":"HX Vo","year":"2014","unstructured":"Vo, H. X., & Durlofsky, L. J. (2014). A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models. Mathematical Geosciences, 46(7), 775\u2013813. https:\/\/doi.org\/10.1007\/s11004-014-9541-2.","journal-title":"Mathematical Geosciences"},{"issue":"7","key":"1562_CR55","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1007\/s11004-018-9740-3","volume":"50","author":"T Wambeke","year":"2018","unstructured":"Wambeke, T., & Benndorf, J. (2018). A study of the influence of measurement volume, blending ratios and sensor precision on real-time reconciliation of grade control models. Mathematical Geosciences, 50(7), 801\u2013826. https:\/\/doi.org\/10.1007\/s11004-018-9740-3.","journal-title":"Mathematical Geosciences"},{"key":"1562_CR56","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.advwatres.2017.12.011","volume":"112","author":"T Xu","year":"2019","unstructured":"Xu, T., & Hern\u00e1ndez, J. G. (2019). Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman filter. Advances in Water Resources, 112, 106\u2013123.","journal-title":"Advances in Water Resources"},{"issue":"5","key":"1562_CR57","doi-asserted-by":"publisher","first-page":"4197","DOI":"10.1002\/2013WR014525","volume":"50","author":"L Xue","year":"2014","unstructured":"Xue, L., & Zhang, D. (2014). A multimodel data assimilation framework via the ensemble Kalman filter. Water Resources Research, 50(5), 4197\u20134219. https:\/\/doi.org\/10.1002\/2013WR014525.","journal-title":"Water Resources Research"},{"issue":"7","key":"1562_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11004-018-9770-x","volume":"51","author":"C Y\u00fcksel","year":"2018","unstructured":"Y\u00fcksel, C., Minnecker, C., Shishvan, M. S., Benndorf, J., & Buxton, M. (2018). Value of information introduced by a resource model updating framework. Mathematical Geosciences, 51(7), 1\u201319. https:\/\/doi.org\/10.1007\/s11004-018-9770-x.","journal-title":"Mathematical Geosciences"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01562-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10845-020-01562-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-020-01562-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:03:08Z","timestamp":1617321788000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10845-020-01562-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,2]]},"references-count":58,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["1562"],"URL":"https:\/\/doi.org\/10.1007\/s10845-020-01562-5","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,2]]},"assertion":[{"value":"26 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}