{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:46:08Z","timestamp":1743021968055,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031477232"},{"type":"electronic","value":"9783031477249"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-47724-9_10","type":"book-chapter","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:29:08Z","timestamp":1713472148000},"page":"132-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Models for Inventory Decisions: A Comparative Analysis"],"prefix":"10.1007","author":[{"given":"Thais","family":"de Castro Moraes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue-Ming","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ek Peng","family":"Chew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Choi, T.M., Yu, Y., Au, K.F.: A hybrid SARIMA wavelet transform method for sales forecasting. Decis. Support Syst. 51(1), 130\u201340 (2011)","DOI":"10.1016\/j.dss.2010.12.002"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.tre.2019.05.007","volume":"127","author":"TM Choi","year":"2019","unstructured":"Choi, T.M., Wen, X., Sun, X., Chung, S.H.: The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era. Transp. Res. Part E: Logist. Transp. Rev. 127, 178\u2013191 (2019)","journal-title":"Transp. Res. Part E: Logist. Transp. Rev."},{"issue":"16","key":"10_CR3","doi-asserted-by":"publisher","first-page":"4964","DOI":"10.1080\/00207543.2020.1735666","volume":"58","author":"S Punia","year":"2020","unstructured":"Punia, S., Nikolopoulos, K., Singh, S.P., Madaan, J.K., Litsiou, K.: Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. Int. J. Prod. Res. 58(16), 4964\u20134979 (2020)","journal-title":"Int. J. Prod. Res."},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10479-016-2204-6","volume":"257","author":"S Ren","year":"2017","unstructured":"Ren, S., Chan, H.L., Ram, P.: A comparative study on fashion demand forecasting models with multiple sources of uncertainty. Ann. Oper. Res.Oper. Res. 257(1), 335\u2013355 (2017)","journal-title":"Ann. Oper. Res.Oper. Res."},{"issue":"3","key":"10_CR5","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.ejor.2016.07.015","volume":"264","author":"D Barrow","year":"2018","unstructured":"Barrow, D., Kourentzes, N.: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days. Eur. J. Oper. Res.Oper. Res. 264(3), 967\u2013977 (2018)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"issue":"3","key":"10_CR6","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.ejor.2019.09.018","volume":"281","author":"M Kraus","year":"2020","unstructured":"Kraus, M., Feuerriegel, S., Oztekin, A.: Deep learning in business analytics and operations research: models, applications and managerial implications. Eur. J. Oper. Res.Oper. Res. 281(3), 628\u2013641 (2020)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"issue":"3","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.ejor.2006.12.004","volume":"184","author":"R Carbonneau","year":"2008","unstructured":"Carbonneau, R., Laframboise, K., Vahidov, R.: Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res.Oper. Res. 184(3), 1140\u20131154 (2008)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"issue":"4","key":"10_CR8","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1080\/24725854.2019.1632502","volume":"52","author":"A Oroojlooyjadid","year":"2020","unstructured":"Oroojlooyjadid, A., Snyder, L.V., Tak\u00e1\u010d, M.: Applying deep learning to the newsvendor problem. IISE Trans. 52(4), 444\u2013463 (2020)","journal-title":"IISE Trans."},{"issue":"4","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1016\/j.ijforecast.2019.06.004","volume":"38","author":"R Fildes","year":"2022","unstructured":"Fildes, R., Ma, S., Kolassa, S.: Retail forecasting: research and practice. Int. J. Forecast. 38(4), 1283\u20131318 (2022)","journal-title":"Int. J. Forecast."},{"key":"10_CR10","unstructured":"Box, G.E.P, Jenkins, G.M., Reinsel, G.C, Ljung, G.M.: Time Series Analysis: Forecasting and Control. 5th edn. Wiley, New York (2015)"},{"issue":"2","key":"10_CR11","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.ijpe.2007.06.013","volume":"114","author":"KF Au","year":"2008","unstructured":"Au, K.F., Choi, T.M., Yu, Y.: Fashion retail forecasting by evolutionary neural networks. Int. J. Prod. Econ. 114(2), 615\u2013630 (2008)","journal-title":"Int. J. Prod. Econ."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Yu, Y., Choi, T.M., Hui, C.L.: An intelligent fast sales forecasting model for fashion products. Expert Syst. Appl. 38(6) (2011)","DOI":"10.1016\/j.eswa.2010.12.089"},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.knosys.2012.07.002","volume":"36","author":"M Xia","year":"2012","unstructured":"Xia, M., Zhang, Y., Weng, L., Ye, X.: Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowl.-Based Syst..-Based Syst. 36, 253\u2013259 (2012)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"issue":"3","key":"10_CR14","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.ejor.2012.11.013","volume":"226","author":"\u00d6 G\u00fcr Ali","year":"2013","unstructured":"G\u00fcr Ali, \u00d6., Yaman, K.: Selecting rows and columns for training support vector regression models with large retail datasets. Eur. J. Oper. Res.Oper. Res. 226(3), 471\u2013480 (2013)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Priore, P., Ponte, B., Rosillo, R., de la Fuente, D.: Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. Int. J. Prod. Res. 57(11) (2019)","DOI":"10.1080\/00207543.2018.1552369"},{"issue":"11","key":"10_CR16","doi-asserted-by":"publisher","first-page":"3330","DOI":"10.1080\/00207543.2019.1685705","volume":"58","author":"A Brintrup","year":"2020","unstructured":"Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., et al.: Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. Int. J. Prod. Res. 58(11), 3330\u20133341 (2020)","journal-title":"Int. J. Prod. Res."},{"issue":"6","key":"10_CR17","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1080\/00207543.2021.1871675","volume":"60","author":"Y Guo","year":"2022","unstructured":"Guo, Y., Zhang, Y., Boulaksil, Y., Tian, N.: Multi-dimensional spatiotemporal demand forecasting and service vehicle dispatching for online car-hailing platforms. Int. J. Prod. Res. 60(6), 1832\u20131853 (2022)","journal-title":"Int. J. Prod. Res."},{"key":"10_CR18","first-page":"1","volume":"1","author":"H Larochelle","year":"2009","unstructured":"Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 1, 1\u201340 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"7553","key":"10_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013445 (2015)","journal-title":"Nature"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.dss.2018.08.010","volume":"114","author":"ALD Loureiro","year":"2018","unstructured":"Loureiro, A.L.D., Migu\u00e9is, V.L., da Silva, L.F.M.: Exploring the use of deep neural networks for sales forecasting in fashion retail. Decis. Support. Syst.. Support. Syst. 114, 81\u201393 (2018)","journal-title":"Decis. Support. Syst.. Support. Syst."},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Chaudhuri, N., Gupta, G., Vamsi, V., Bose, I.: On the platform but will they buy? Predicting customers\u2019 purchase behavior using deep learning. Int. J. Prod. Res. 149 (2021)","DOI":"10.1016\/j.dss.2021.113622"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Shajalal, M., Hajek, P., Abedin, M.Z.: Product backorder prediction using deep neural network on imbalanced data. Int. J. Prod. Res. (2021)","DOI":"10.1080\/00207543.2021.1901153"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Chien, C.F., Lin, Y.S., Lin, S.K.: Deep reinforcement learning for selecting demand forecast models to empower industry 3.5 and an empirical study for a semiconductor component distributor. Int. J. Prod. Res. 58(9), 2784\u2013804 (2020)","DOI":"10.1080\/00207543.2020.1733125"},{"issue":"1","key":"10_CR24","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ejor.2020.05.038","volume":"288","author":"S Ma","year":"2021","unstructured":"Ma, S., Fildes, R.: Retail sales forecasting with meta-learning. Eur. J. Oper. Res.Oper. Res. 288(1), 111\u2013128 (2021)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Moraes, T.D.C, Yuan, X.-M.: Data-driven solutions to the newsvendor problem: a systematic literature review. In: IFIP Advances in Information and Communication Technology, pp. 149\u201358. Nantes, France (2021)","DOI":"10.1007\/978-3-030-85910-7_16"},{"issue":"2","key":"10_CR26","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1214\/aoms\/1177706264","volume":"30","author":"H Scarf","year":"1959","unstructured":"Scarf, H.: Bayes solutions of the statistical inventory problem. Ann. Math. Stat. 30(2), 490\u2013508 (1959)","journal-title":"Ann. Math. Stat."},{"issue":"4","key":"10_CR27","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.orl.2004.08.003","volume":"33","author":"LH Liyanage","year":"2005","unstructured":"Liyanage, L.H., Shanthikumar, J.G.: A practical inventory control policy using operational statistics. Oper. Res. Lett.. Res. Lett. 33(4), 341\u2013348 (2005)","journal-title":"Oper. Res. Lett.. Res. Lett."},{"issue":"1","key":"10_CR28","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.orl.2007.04.010","volume":"36","author":"LY Chu","year":"2008","unstructured":"Chu, L.Y., Shanthikumar, J.G., Shen, Z.J.M.: Solving operational statistics via a Bayesian analysis. Oper. Res. Lett.. Res. Lett. 36(1), 110\u2013116 (2008)","journal-title":"Oper. Res. Lett.. Res. Lett."},{"issue":"2","key":"10_CR29","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1111\/j.1937-5956.2011.01261.x","volume":"21","author":"V Ramamurthy","year":"2012","unstructured":"Ramamurthy, V., Shanthikumar, J.G., Shen, Z.J.M.: Inventory policy with parametric demand: operational statistics, linear correction, and regression. Prod. Oper. Manag.Oper. Manag. 21(2), 291\u2013308 (2012)","journal-title":"Prod. Oper. Manag.Oper. Manag."},{"issue":"3","key":"10_CR30","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1002\/nav.21623","volume":"62","author":"M Lu","year":"2015","unstructured":"Lu, M., Shanthikumar, J.G., Shen, Z.J.M.: Technical note\u2014operational statistics: properties and the risk-averse case. Nav. Res. Logist.Logist. 62(3), 206\u2013214 (2015)","journal-title":"Nav. Res. Logist.Logist."},{"issue":"2","key":"10_CR31","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.ijpe.2011.04.017","volume":"140","author":"AL Beutel","year":"2012","unstructured":"Beutel, A.L., Minner, S.: Safety stock planning under causal demand forecasting. Int. J. Prod. Econ. 140(2), 637\u2013645 (2012)","journal-title":"Int. J. Prod. Econ."},{"key":"10_CR32","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ijpe.2013.04.039","volume":"149","author":"AL Sachs","year":"2014","unstructured":"Sachs, A.L., Minner, S.: The data-driven newsvendor with censored demand observations. Int. J. Prod. Econ. 149, 28\u201336 (2014)","journal-title":"Int. J. Prod. Econ."},{"issue":"2","key":"10_CR33","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1287\/moor.1080.0367","volume":"34","author":"WT Huh","year":"2009","unstructured":"Huh, W.T., Janakiraman, G., Muckstadt, J.A., Rusmevichientong, P.: An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand. Math. Oper. Res.Oper. Res. 34(2), 397\u2013416 (2009)","journal-title":"Math. Oper. Res.Oper. Res."},{"issue":"4","key":"10_CR34","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1287\/opre.1100.0906","volume":"59","author":"WT Huh","year":"2011","unstructured":"Huh, W.T., Levi, R., Rusmevichientong, P., Orlin, J.B.: Adaptive data-driven inventory control with censored demand based on Kaplan-Meier estimator. Oper. Res.. Res. 59(4), 929\u2013941 (2011)","journal-title":"Oper. Res.. Res."},{"issue":"4","key":"10_CR35","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1287\/moor.1070.0272","volume":"32","author":"R Levi","year":"2007","unstructured":"Levi, R., Roundy, R.O., Shmoys, D.B.: Provably near-optimal sampling-based policies for stochastic inventory control models. Math. Oper. Res.Oper. Res. 32(4), 821\u2013839 (2007)","journal-title":"Math. Oper. Res.Oper. Res."},{"issue":"6","key":"10_CR36","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1287\/opre.2015.1422","volume":"63","author":"R Levi","year":"2015","unstructured":"Levi, R., Perakis, G., Uichanco, J.: The data-driven newsvendor problem: new bounds and insights. Oper. Res.. Res. 63(6), 1294\u20131306 (2015)","journal-title":"Oper. Res.. Res."},{"key":"10_CR37","unstructured":"Bertsimas, D., Thiele, A.: A data-driven approach to newsvendor problems. Technical Report, Massachusetts Institute of Technology (2005)"},{"issue":"6","key":"10_CR38","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.orl.2019.08.008","volume":"47","author":"Y Cao","year":"2019","unstructured":"Cao, Y., Shen, Z.J.M.: Quantile forecasting and data-driven inventory management under nonstationary demand. Oper. Res. Lett.. Res. Lett. 47(6), 465\u2013472 (2019)","journal-title":"Oper. Res. Lett.. Res. Lett."},{"issue":"3","key":"10_CR39","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1016\/j.ejor.2019.04.043","volume":"278","author":"J Huber","year":"2019","unstructured":"Huber, J., M\u00fcller, S., Fleischmann, M., Stuckenschmidt, H.: A data-driven newsvendor problem: from data to decision. Eur. J. Oper. Res.Oper. Res. 278(3), 904\u2013915 (2019)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Bertsimas, D., Kallus, N.: From predictive to prescriptive analytics. Manag Sci (2019)","DOI":"10.1287\/mnsc.2018.3253"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Bertsimas, D., Koduri, N.: Data-driven optimization: a reproducing Kernel Hilbert space approach. Oper. Res. (2021)","DOI":"10.1287\/opre.2020.2069"},{"key":"10_CR42","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). https:\/\/www.deeplearningbook.org\/"},{"issue":"9","key":"10_CR43","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput.Comput. 29(9), 2352\u20132449 (2017)","journal-title":"Neural Comput.Comput."},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Zhao, R., Yan, R., Wang, J., Mao, K.: Learning to monitor machine health with convolutional Bi-directional LSTM networks. Sensors 17(2) (2017)","DOI":"10.3390\/s17020273"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Fan, C., Zhang, Y., Pan, Y., Li, X., Zhang, C., Yuan, R., et al.: Multi-horizon time series forecasting with temporal attention learning. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2527\u201335. New York, USA (2019)","DOI":"10.1145\/3292500.3330662"},{"key":"10_CR46","unstructured":"Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: Procedings of the 30th International Conference on Machine Learning, pp. 1310\u20131318 (2013)"},{"key":"10_CR47","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 6, 107\u2013116 (1998)","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"10_CR48","unstructured":"Schmidt, R.M.: Recurrent Neural Networks (RNNs): a gentle introduction and overview. (2019). http:\/\/arxiv.org\/abs\/1912.05911"},{"key":"10_CR49","unstructured":"Chollet, F.: Keras: deep learning for humans. Keras (2022). https:\/\/github.com\/keras-team\/keras"},{"key":"10_CR50","volume-title":"Inventory and Production Management in Supply Chains","author":"EA Silver","year":"2017","unstructured":"Silver, E.A., Pyke, D.F., Thomas, D.J.: Inventory and Production Management in Supply Chains. Taylor and Francis, New York (2017)"},{"key":"10_CR51","unstructured":"Meller, J., Taigel, F.: Machine Learning for Inventory Management: Analyzing Two Concepts to Get From Data to Decisions. Rochester, NY (2019). https:\/\/papers.ssrn.com\/abstract=3256643"},{"issue":"2","key":"10_CR52","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s00186-012-0396-3","volume":"76","author":"S Lee","year":"2012","unstructured":"Lee, S., Homem-de-Mello, T., Kleywegt, A.J.: Newsvendor-type models with decision-dependent uncertainty. Math. Methods Oper. Res.Oper. Res. 76(2), 189\u2013221 (2012)","journal-title":"Math. Methods Oper. Res.Oper. Res."},{"key":"10_CR53","unstructured":"Kaggle: Store Item Demand Forecasting Challenge. Kaggle (2018). https:\/\/kaggle.com\/competitions\/demand-forecasting-kernels-only"},{"key":"10_CR54","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017). http:\/\/arxiv.org\/abs\/1412.6980"},{"issue":"56","key":"10_CR55","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47724-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T20:35:47Z","timestamp":1713472547000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47724-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031477232","9783031477249"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47724-9_10","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}