{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:21:37Z","timestamp":1772583697692,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R &D Program of China","doi-asserted-by":"crossref","award":["2020YFB1712105"],"award-info":[{"award-number":["2020YFB1712105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Technology Research Development Joint Foundation of Henan Province","award":["225101610001"],"award-info":[{"award-number":["225101610001"]}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["242300420286"],"award-info":[{"award-number":["242300420286"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10845-024-02442-y","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:02:03Z","timestamp":1718409723000},"page":"3983-4003","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting"],"prefix":"10.1007","volume":"36","author":[{"given":"Lilin","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyu","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5335-9517","authenticated-orcid":false,"given":"Wentao","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiejun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fukang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,15]]},"reference":[{"key":"2442_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106435","volume":"143","author":"H Abbasimehr","year":"2020","unstructured":"Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using lstm network for demand forecasting. Computers & industrial engineering, 143, 106435.","journal-title":"Computers & industrial engineering"},{"key":"2442_CR2","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.scs.2018.12.013","volume":"45","author":"T Ahmad","year":"2019","unstructured":"Ahmad, T., & Chen, H. (2019). Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems. Sustainable Cities and Society, 45, 460\u2013473.","journal-title":"Sustainable Cities and Society"},{"key":"2442_CR3","doi-asserted-by":"crossref","unstructured":"Arrow, K.J., Harris, T., & Marschak, J. (1951). Optimal inventory policy. Econometrica: Journal of the Econometric Society, 250\u2013272","DOI":"10.2307\/1906813"},{"issue":"4","key":"2442_CR4","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/S0169-2070(00)00066-2","volume":"16","author":"V Assimakopoulos","year":"2000","unstructured":"Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International journal of forecasting, 16(4), 521\u2013530.","journal-title":"International journal of forecasting"},{"issue":"2","key":"2442_CR5","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.ijpe.2011.09.004","volume":"143","author":"MZ Babai","year":"2013","unstructured":"Babai, M. Z., Ali, M. M., Boylan, J. E., & Syntetos, A. A. (2013). Forecasting and inventory performance in a two-stage supply chain with arima (0, 1, 1) demand: Theory and empirical analysis. International Journal of Production Economics, 143(2), 463\u2013471.","journal-title":"International Journal of Production Economics"},{"issue":"2","key":"2442_CR6","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.ijpe.2011.04.017","volume":"140","author":"A-L Beutel","year":"2012","unstructured":"Beutel, A.-L., & Minner, S. (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics, 140(2), 637\u2013645.","journal-title":"International Journal of Production Economics"},{"issue":"2","key":"2442_CR7","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ijpe.2006.10.005","volume":"107","author":"JE Boylan","year":"2007","unstructured":"Boylan, J. E., & Syntetos, A. A. (2007). The accuracy of a modified croston procedure. International Journal of Production Economics, 107(2), 511\u2013517.","journal-title":"International Journal of Production Economics"},{"issue":"3","key":"2442_CR8","first-page":"264","volume":"27","author":"C Chatfield","year":"1978","unstructured":"Chatfield, C. (1978). The holt-winters forecasting procedure. Journal of the Royal Statistical Society: Series C (Applied Statistics), 27(3), 264\u2013279.","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"key":"2442_CR9","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.omega.2015.04.003","volume":"58","author":"M Chica","year":"2016","unstructured":"Chica, M., Bautista, J., Cord\u00f3n, \u00d3., & Damas, S. (2016). A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand. Omega, 58, 55\u201368.","journal-title":"Omega"},{"issue":"1","key":"2442_CR10","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jom.2009.04.003","volume":"28","author":"DJ Closs","year":"2010","unstructured":"Closs, D. J., Nyaga, G. N., & Voss, M. D. (2010). The differential impact of product complexity, inventory level, and configuration capacity on unit and order fill rate performance. Journal of Operations Management, 28(1), 47\u201357.","journal-title":"Journal of Operations Management"},{"key":"2442_CR11","unstructured":"Crone, S. F. (2003). Artificial neural networks for time series prediction-a novel approach to inventory management using asymmetric cost functions. In: IC-AI, pp. 193\u2013199"},{"issue":"3","key":"2442_CR12","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1057\/jors.1972.50","volume":"23","author":"JD Croston","year":"1972","unstructured":"Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289\u2013303.","journal-title":"Journal of the Operational Research Society"},{"key":"2442_CR13","doi-asserted-by":"crossref","unstructured":"Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, September 18\u201320, 2000 Proceedings 6, pp. 849\u2013858","DOI":"10.1007\/3-540-45356-3_83"},{"issue":"1","key":"2442_CR14","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.ejor.2007.03.008","volume":"187","author":"TTH Duc","year":"2008","unstructured":"Duc, T. T. H., Luong, H. T., & Kim, Y.-D. (2008). A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process. European Journal of Operational Research, 187(1), 243\u2013256.","journal-title":"European Journal of Operational Research"},{"issue":"2","key":"2442_CR15","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1287\/mnsc.1040.0298","volume":"51","author":"V Gaur","year":"2005","unstructured":"Gaur, V., Fisher, M. L., & Raman, A. (2005). An econometric analysis of inventory turnover performance in retail services. Management science, 51(2), 181\u2013194.","journal-title":"Management science"},{"issue":"2","key":"2442_CR16","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/j.ejor.2020.07.024","volume":"289","author":"F Ghadimi","year":"2021","unstructured":"Ghadimi, F., & Aouam, T. (2021). Planning capacity and safety stocks in a serial production-distribution system with multiple products. European Journal of Operational Research, 289(2), 533\u2013552.","journal-title":"European Journal of Operational Research"},{"issue":"2","key":"2442_CR17","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.ijpe.2007.01.007","volume":"111","author":"RS Gutierrez","year":"2008","unstructured":"Gutierrez, R. S., Solis, A. O., & Mukhopadhyay, S. (2008). Lumpy demand forecasting using neural networks. International journal of production economics, 111(2), 409\u2013420.","journal-title":"International journal of production economics"},{"issue":"2","key":"2442_CR18","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1016\/j.amc.2006.01.064","volume":"181","author":"Z Hua","year":"2006","unstructured":"Hua, Z., & Zhang, B. (2006). A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation, 181(2), 1035\u20131048.","journal-title":"Applied Mathematics and Computation"},{"key":"2442_CR19","doi-asserted-by":"crossref","unstructured":"Jha, B.K., & Pande, S. (2021). Time series forecasting model for supermarket sales using fb-prophet. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 547\u2013554 IEEE","DOI":"10.1109\/ICCMC51019.2021.9418033"},{"key":"2442_CR20","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1108\/01443571211250112","volume":"32","author":"TJ Kampen","year":"2012","unstructured":"Kampen, T. J., Akkerman, R., & Donk, D. P. (2012). Sku classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32, 850\u2013876.","journal-title":"International Journal of Operations & Production Management"},{"key":"2442_CR21","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s10845-023-02112-5","volume":"35","author":"A Kania","year":"2024","unstructured":"Kania, A., Afsar, B., Miettinen, K., & Sipil\u00e4, J. (2024). Desmils: a decision support approach for multi-item lot sizing using interactive multiobjective optimization. Journal of Intelligent Manufacturing, 35, 1373\u20131387.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2442_CR22","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.eswa.2019.06.060","volume":"137","author":"JP Karmy","year":"2019","unstructured":"Karmy, J. P., & Maldonado, S. (2019). Hierarchical time series forecasting via support vector regression in the european travel retail industry. Expert Systems with Applications, 137, 59\u201373.","journal-title":"Expert Systems with Applications"},{"key":"2442_CR23","first-page":"3149","volume":"30","author":"G Ke","year":"2017","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 3149\u20133157.","journal-title":"Advances in neural information processing systems"},{"issue":"20","key":"2442_CR24","doi-asserted-by":"publisher","first-page":"6359","DOI":"10.1016\/j.apm.2015.01.037","volume":"39","author":"GA Keskin","year":"2015","unstructured":"Keskin, G. A., Omurca, S. \u0130, Ayd\u0131n, N., & Ekinci, E. (2015). A comparative study of production-inventory model for determining effective production quantity and safety stock level. Applied Mathematical Modelling, 39(20), 6359\u20136374.","journal-title":"Applied Mathematical Modelling"},{"key":"2442_CR25","doi-asserted-by":"crossref","unstructured":"Kiefer, D., Grimm, F., Bauer, M., & Van Dinther, C. (2021). Demand forecasting intermittent and lumpy time series: Comparing statistical, machine learning and deep learning methods","DOI":"10.24251\/HICSS.2021.172"},{"key":"2442_CR26","volume-title":"Demand forecasting and inventory planning: A practitioner\u2019s perspective","author":"B Liu","year":"2022","unstructured":"Liu, B. (2022). Demand forecasting and inventory planning: A practitioner\u2019s perspective. Beijing: China Machine Press."},{"key":"2442_CR27","unstructured":"Loffredo, A., May, M.C., Matta, A., & Lanza, G. (2023). Reinforcement learning for sustainability enhancement of production lines. Journal of Intelligent Manufacturing, 1\u20137"},{"issue":"06","key":"2442_CR28","first-page":"91","volume":"32","author":"XM Luo","year":"2014","unstructured":"Luo, X. M., Li, J. B., & Hu, P. (2014). E-commerce inventory optimization strategy based on time series forecasting. Systems Engineering, 32(06), 91\u201398.","journal-title":"Systems Engineering"},{"key":"2442_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112478","volume":"208","author":"W Mao","year":"2023","unstructured":"Mao, W., Chen, Z., Zhang, Y., & Liang, X. (2023). Tensor-daad: When tensor meets online early fault detection with transfer learning. Measurement, 208, 112478.","journal-title":"Measurement"},{"key":"2442_CR30","unstructured":"Mello, J. (2013). Demand and supply integration: The key to world-class demand forecasting by mark a. moon. Foresight: The International Journal of Applied Forecasting (31), 35\u201337"},{"key":"2442_CR31","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.jmsy.2022.01.010","volume":"62","author":"G Nain","year":"2022","unstructured":"Nain, G., Pattanaik, K. K., & Sharma, G. K. (2022). Towards edge computing in intelligent manufacturing: Past, present and future. Journal of Manufacturing Systems, 62, 588\u2013611.","journal-title":"Journal of Manufacturing Systems"},{"key":"2442_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2021.105651","volume":"139","author":"M Nematollahi","year":"2022","unstructured":"Nematollahi, M., Hosseini-Motlagh, S.-M., C\u00e1rdenas-Barr\u00f3n, L. E., & Tiwari, S. (2022). Coordinating visit interval and safety stock decisions in a two-level supply chain with shelf-life considerations. Computers & Operations Research, 139, 105651.","journal-title":"Computers & Operations Research"},{"key":"2442_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2021.102513","volume":"105","author":"\u00c7 Pin\u00e7e","year":"2021","unstructured":"Pin\u00e7e, \u00c7., Turrini, L., & Meissner, J. (2021). Intermittent demand forecasting for spare parts: a critical review. Omega, 105, 102513.","journal-title":"Omega"},{"issue":"2","key":"2442_CR34","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.ijforecast.2020.08.010","volume":"37","author":"SD Prestwich","year":"2021","unstructured":"Prestwich, S. D., Tarim, S. A., & Rossi, R. (2021). Intermittency and obsolescence: A croston method with linear decay. International Journal of Forecasting, 37(2), 708\u2013715.","journal-title":"International Journal of Forecasting"},{"issue":"12","key":"2442_CR35","doi-asserted-by":"publisher","first-page":"4462","DOI":"10.1111\/poms.13869","volume":"31","author":"H Qin","year":"2022","unstructured":"Qin, H., Simchi-Levi, D., Ferer, R., Mays, J., Merriam, K., Forrester, M., & Hamrick, A. (2022). Trading safety stock for service response time in inventory positioning. Production and Operations Management, 31(12), 4462\u20134474.","journal-title":"Production and Operations Management"},{"issue":"3","key":"2442_CR36","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181\u20131191.","journal-title":"International Journal of Forecasting"},{"issue":"18","key":"2442_CR37","doi-asserted-by":"publisher","first-page":"8189","DOI":"10.1016\/j.eswa.2014.07.003","volume":"41","author":"I Saracoglu","year":"2014","unstructured":"Saracoglu, I., Topaloglu, S., & Keskinturk, T. (2014). A genetic algorithm approach for multi-product multi-period continuous review inventory models. Expert Systems with Applications, 41(18), 8189\u20138202.","journal-title":"Expert Systems with Applications"},{"key":"2442_CR38","unstructured":"Scarf, H., Arrow, K., Karlin, S., & Suppes, P. (1960). The optimality of (s, s) policies in the dynamic inventory problem. Optimal pricing, inflation, and the cost of price adjustment, 49\u201356"},{"issue":"4","key":"2442_CR39","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1287\/msom.1080.0245","volume":"11","author":"T Schoenmeyr","year":"2009","unstructured":"Schoenmeyr, T., & Graves, S. C. (2009). Strategic safety stocks in supply chains with evolving forecasts. Manufacturing & Service Operations Management, 11(4), 657\u2013673.","journal-title":"Manufacturing & Service Operations Management"},{"issue":"6","key":"2442_CR40","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1287\/opre.45.6.931","volume":"45","author":"SP Sethi","year":"1997","unstructured":"Sethi, S. P., & Cheng, F. (1997). Optimality of (s, s) policies in inventory models with markovian demand. Operations Research, 45(6), 931\u2013939.","journal-title":"Operations Research"},{"key":"2442_CR41","doi-asserted-by":"publisher","first-page":"5758","DOI":"10.1609\/aaai.v34i04.6032","volume":"34","author":"Q Shi","year":"2020","unstructured":"Shi, Q., Yin, J., Cai, J., Cichocki, A., Yokota, T., Chen, L., Yuan, M., & Zeng, J. (2020). Block hankel tensor arima for multiple short time series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 5758\u20135766.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"1","key":"2442_CR42","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/S0169-2070(01)00109-1","volume":"18","author":"RD Snyder","year":"2002","unstructured":"Snyder, R. D., Koehler, A. B., & Ord, J. K. (2002). Forecasting for inventory control with exponential smoothing. International Journal of Forecasting, 18(1), 5\u201318.","journal-title":"International Journal of Forecasting"},{"key":"2442_CR43","doi-asserted-by":"publisher","first-page":"3001","DOI":"10.1007\/s10845-022-01981-6","volume":"34","author":"J Song","year":"2022","unstructured":"Song, J., Lee, Y. C., & Lee, J. (2022). Deep generative model with time series-image encoding for manufacturing fault detection in die casting process. Journal of Intelligent Manufacturing, 34, 3001\u20133014.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"1\u20133","key":"2442_CR44","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/S0925-5273(00)00143-2","volume":"71","author":"AA Syntetos","year":"2001","unstructured":"Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. International journal of production economics, 71(1\u20133), 457\u2013466.","journal-title":"International journal of production economics"},{"key":"2442_CR45","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ijforecast.2004.10.001","volume":"21","author":"AA Syntetos","year":"2005","unstructured":"Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21, 303\u2013314.","journal-title":"International Journal of Forecasting"},{"key":"2442_CR46","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1057\/palgrave.jors.2601841","volume":"56","author":"AA Syntetos","year":"2005","unstructured":"Syntetos, A. A., Boylan, J. E., & Croston, J. (2005). On the categorization of demand patterns. Journal of the operational research society, 56, 495\u2013503.","journal-title":"Journal of the operational research society"},{"issue":"3","key":"2442_CR47","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.ejor.2011.05.018","volume":"214","author":"RH Teunter","year":"2011","unstructured":"Teunter, R. H., Syntetos, A. A., & Babai, M. Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606\u2013615.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"2442_CR48","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.ejor.2021.09.041","volume":"301","author":"S Transchel","year":"2022","unstructured":"Transchel, S., Buisman, M. E., & Haijema, R. (2022). Joint assortment and inventory optimization for vertically differentiated products under consumer-driven substitution. European Journal of Operational Research, 301(1), 163\u2013179.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"2442_CR49","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.ijforecast.2018.05.009","volume":"35","author":"JR Trapero","year":"2019","unstructured":"Trapero, J. R., Card\u00f3s, M., & Kourentzes, N. (2019). Quantile forecast optimal combination to enhance safety stock estimation. International Journal of Forecasting, 35(1), 239\u2013250.","journal-title":"International Journal of Forecasting"},{"issue":"2","key":"2442_CR50","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.ejor.2008.02.039","volume":"195","author":"C-S Tsou","year":"2009","unstructured":"Tsou, C.-S. (2009). Evolutionary pareto optimizers for continuous review stochastic inventory systems. European Journal of Operational Research, 195(2), 364\u2013371.","journal-title":"European Journal of Operational Research"},{"key":"2442_CR51","unstructured":"Wang, C., Sun, Y., & Wang, X. (2023). Image deep learning in fault diagnosis of mechanical equipment. Journal of Intelligent Manufacturing, 1\u201341"},{"issue":"3","key":"2442_CR52","doi-asserted-by":"publisher","first-page":"205","DOI":"10.23919\/JCC.2020.03.017","volume":"17","author":"Y Wang","year":"2020","unstructured":"Wang, Y., & Guo, Y. (2020). Forecasting method of stock market volatility in time series data based on mixed model of arima and xgboost. China Communications, 17(3), 205\u2013221.","journal-title":"China Communications"},{"issue":"4","key":"2442_CR53","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/0169-2070(94)90021-3","volume":"10","author":"TR Willemain","year":"1994","unstructured":"Willemain, T. R., Smart, C. N., Shockor, J. H., & DeSautels, P. A. (1994). Forecasting intermittent demand in manufacturing: a comparative evaluation of croston\u2019s method. International Journal of forecasting, 10(4), 529\u2013538.","journal-title":"International Journal of forecasting"},{"key":"2442_CR54","doi-asserted-by":"crossref","unstructured":"Yildirim, E., & Denizhan, B. (2022). A two-echelon pharmaceutical supply chain optimization via genetic algorithm. Recent Advances in Intelligent Manufacturing and Service Systems, 77\u201387","DOI":"10.1007\/978-981-16-7164-7_7"},{"key":"2442_CR55","doi-asserted-by":"crossref","unstructured":"Yokota, T., Erem, B., Guler, S., Warfield, S.K., & Hontani, H. (2018). Missing slice recovery for tensors using a low-rank model in embedded space. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8251\u20138259","DOI":"10.1109\/CVPR.2018.00861"},{"key":"2442_CR56","doi-asserted-by":"crossref","unstructured":"Yokota, T., Hontani, H., Zhao, Q., & Cichocki, A. (2020). Manifold modeling in embedded space: An interpretable alternative to deep image prior. IEEE Transactions on Neural Networks and Learning Systems, 33(3), 1022\u20131036.","DOI":"10.1109\/TNNLS.2020.3037923"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02442-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-024-02442-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-024-02442-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T21:38:30Z","timestamp":1757108310000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-024-02442-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,15]]},"references-count":56,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2442"],"URL":"https:\/\/doi.org\/10.1007\/s10845-024-02442-y","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,15]]},"assertion":[{"value":"26 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No potential Conflict of interest was reported by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}