Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach
References
- (2018) Sparse: A more modern sparse array library. Akici F, Lippa D, Niederhut D, Pacer M, eds. Proc. 17th Python in Sci. Conf. (SciPy, Austin, TX), 27–30.Google Scholar
- (2002) Statistical mechanics of complex networks. Rev. Modern Phys. 74(1):47.Crossref, Google Scholar
- (2017) Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine 34(6):26–38.Crossref, Google Scholar
- (2017) On perturbed proximal gradient algorithms. J. Machine Learn. Res. 18(10):1–33.Google Scholar
- (2018) Automatic differentiation in machine learning: A survey. J. Machine Learn. Res. 18(153):1–43.Google Scholar
- (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1):183–202.Crossref, Google Scholar
- (2018) Optimization methods for large-scale machine learning. SIAM Rev. 60(2):223–311.Crossref, Google Scholar
- (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Science & Business Media, New York).Crossref, Google Scholar
- (2009) Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks. auf der Heide FM, Bender MA, eds. Proc. 21st Annual Sympos. on Parallelism in Algorithms and Architectures (ACM, New York), 233–244.Google Scholar
- (2014) Integrality in stochastic inventory models. Production Oper. Management 23(9):1646–1663.Crossref, Google Scholar
- (1960) Optimal policies for a multi-echelon inventory problem. Management Sci. 6(4):475–490.Link, Google Scholar
- (1999) Stock positioning and performance estimation in serial production-transportation systems. Manufacturing Service Oper. Management 1(1):77–88.Link, Google Scholar
- (2006) Smoking adjoints: Fast Monte Carlo Greeks. Risk 19(1):88–92.Google Scholar
- (2003) Monte Carlo Methods in Financial Engineering, vol. 53 (Springer Science & Business Media, New York).Crossref, Google Scholar
- (1994) The stability of a capacitated, multi-echelon production-inventory system under a base-stock policy. Oper. Res. 42(5):913–925.Link, Google Scholar
- (1995) Sensitivity analysis for base-stock levels in multiechelon production-inventory systems. Management Sci. 41(2):263–281.Link, Google Scholar
- (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
- (1985) On the history of the minimum spanning tree problem. Ann. History Comput. 7(1):43–57.Crossref, Google Scholar
- (2000) Optimizing strategic safety stock placement in supply chains. Manufacturing Service Oper. Management 2(1):68–83.Link, Google Scholar
- (2003) Supply chain design: Safety stock placement and supply chain configuration. Supply Chain Management: Design, Coordination and Operation, vol. 11 of Handbooks in Operations Research and Management Science (Elsevier, New York), 95–132.Crossref, Google Scholar
- (2010) Capacity allocation and scheduling in supply chains. Oper. Res. 58(6):1711–1725.Link, Google Scholar
- (2020) Array programming with NumPy. Nature 585(7825):357–362.Crossref, Google Scholar
- (1983) Infinitesimal and finite perturbation analysis for queueing networks. Automatica J. IFAC 19(4):439–445.Crossref, Google Scholar
- (2011) Optimizing strategic safety stock placement in general acyclic networks. Oper. Res. 59(3):781–787.Link, Google Scholar
- (2013) Incorporating stochastic lead times into the guaranteed service model of safety stock optimization. Interfaces 43(5):421–434.Link, Google Scholar
- (2021) Reinforcement learning provides a flexible approach for realistic supply chain safety stock optimisation. Preprint, submitted July 2, https://arxiv.org/abs/2107.00913.Google Scholar
- (2005) The complexity of safety stock placement in general-network supply chains. Working paper, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
- (2007) Relaxed lasso. Comput. Statist. Data Anal. 52(1):374–393.Crossref, Google Scholar
- (2015) EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding. Proc. IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE, New York), 167–174.Google Scholar
- (2010) Recurrent neural network based language model. Kobayashi T, Hirose K, Nakamura S, eds. Proc. 11th Annual Conf. of the Internat. Speech Comm. Assoc. (ISCA, Baixas, France), 1045–1048.Google Scholar
- (1983) A method of solving a convex programming problem with convergence rate o(1/k2). Soviet Math. Doklady 27:372–376.Google Scholar
- (2022) A deep q-network for the beer game: Deep reinforcement learning for inventory optimization. Manufacturing Service Oper. Managment 24(1):285–304.Link, Google Scholar
- (2014) Proximal algorithms. Foundations Trends Optim. 1(3):127–239.Crossref, Google Scholar
- (2020) Simultaneous decision making for stochastic multi-echelon inventory optimization with deep neural networks as decision makers. Preprint, submitted June 10, https://arxiv.org/abs/2006.05608.Google Scholar
- (1951) A stochastic approximation method. Ann. Math. Statist. 22(3):400–407.Crossref, Google Scholar
- (1989) Optimal inventory policies for assembly systems under random demands. Oper. Res. 37(4):565–579.Link, Google Scholar
- (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536.Crossref, Google Scholar
- (2019) On the performance of the stochastic fista. Working paper, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.Google Scholar
- (2012) Minimization Methods for Non-Differentiable Functions, vol. 3 (Springer Science & Business Media, New York).Google Scholar
- (1958) In-process inventories. Oper. Res. 6(6):863–873.Link, Google Scholar
- (2019) Fundamentals of Supply Chain Theory (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
- (2018) Theoretical insights into the optimization landscape of over-parameterized shallow neural networks. IEEE Trans. Inform. Theory 65(2):742–769.Crossref, Google Scholar
- (2009) Coordinating the use of GPU and CPU for improving performance of compute intensive applications. Proc. IEEE Internat. Conf. on Cluster Comput. and Workshops (IEEE, New York), 1–10.Google Scholar
- (1996) Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B 58(1):267–288.Crossref, Google Scholar
- (2011) The anatomy of the facebook social graph. Preprint, submitted November 18, https://arxiv.org/abs/1111.4503.Google Scholar
- (2009) Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ1-constrained quadratic programming (lasso). IEEE Trans. Inform. Theory 55(5):2183–2202.Crossref, Google Scholar
- (2012) Network Science: An Introduction (Higher Education Press, Beijing).Google Scholar
- (1990) Backpropagation through time: What it does and how to do it. Proc. IEEE 78(10):1550–1560.Crossref, Google Scholar
- (2009) Dual averaging method for regularized stochastic learning and online optimization. Adv. Neural Inform. Processing Systems 22:2116–2124.Google Scholar
- (2016) Image captioning with semantic attention. Proc. IEEE Conf. on Comput. Vision and Pattern Recognition (IEEE, New York), 4651–4659.Google Scholar

