The Article Has Been Retracted
Reason for the Retraction
*This article was reported to plagiarism. *
Details: The author copied several sections from another article.
Retraction Notice:
It has come to our attention that the article titled "Research on the dual source inventory system of perishable product warehouse using recurrent neural networks" published in Applied and Computational Engineering Vol. 55 - Proceedings of the 4th International Conference on Signal Processing and Machine Learning, and authored by Fangyu Sun contains content from another article authored by Lucas Böttcher, which was published in the INFORMS Journal of Computing in 2023.
Upon thorough investigation and in adherence to the guidelines set by COPE (Committee on Publication Ethics) and our publishing ethics and standards, we have made the decision to retract the article from our series. We extend our sincerest apologies to the original authors, readers, and the academic community for any inconvenience or confusion caused by this oversight.
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References
[1]. Whitin T M. The theory of Inventory management [M]. N. J: Princeton University Press,1957.
[2]. Nahmias S. Perishable inventory theory: a review [J]. Operations Research,1982,30(4): 680-708.
[3]. Raafat F. Survey of literature on continously deteriorating inventory models [J]. Journal of the Operational Research Society,1991,42:27-37.
[4]. Scarf H, Karlin S (1958) Inventory models of the Arrow-Harris-Marschak type with time lag. Arrow KJ, Karlin S, Scarf HE, eds. Studies in the Mathematical Theory of Inventory and Production (Stanford University Press, Stanford, CA).
[5]. Sun J, Van Mieghem JA (2019) Robust dual sourcing inventory management: Optimality of capped dual index policies and smoothing. Manufacturing Service Oper. Management 21(4):912–931.
[6]. Goldberg DA, Reiman MI, Wang Q (2021) A survey of recent progress in the asymptotic analysis of inventory systems. Production Oper. Management 30(6):1718–1750
[7]. Barankin E (1961) A delivery-lag inventory model with an emergency provision. Naval Res. Logistics Quart. 8:285–311.
[8]. Fukuda Y (1964) Optimal policies for the inventory problem with negotiable leadtime. Management Sci. 10(4):690–708.
[9]. Bo¨ttcher L, Asikis T, Fragkos I (2023) Control of Dual-Sourcing Inventory Systems Using Recurrent Neural Networks. INFORMS journal on computing, 2023
[10]. Song JS, van Houtum GJ, Van Mieghem JA (2020) Capacity and inventory management: Review, trends, and projections. Manufacturing Service Oper. Management 22(1):36–46.
[11]. Gijsbrechts J, Boute RN, Van Mieghem JA, Zhang DJ (2022) Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems. Manufacturing Service Oper. Management 24(3):1349–1368.
[12]. Linnainmaa S (1976) Taylor expansion of the accumulated rounding error. BIT 16(2):146–160.
[13]. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch. This work was part of the NIPS 2017 Autodiff workshop.
[14]. Baydin AG, Pearlmutter BA, Radul AA, Siskind JM (2018) Automatic differentiation in machine learning: A survey. J. Machine Learn. Res. 18(1):5595–5637.
[15]. Wang W, Axelrod S, Go´mez-Bombarelli R (2020) Differentiable molecular simulations for control and learning. Preprint, submitted February 27, https://arxiv.org/abs/2003.00868.
[16]. Asikis T, Bo¨ttcher L, Antulov-Fantulin N (2022) Neural ordinary differential equation control of dynamics on graphs. Physical Rev. Res. 4(1):013221.
[17]. Bo¨ttcher L, Antulov-Fantulin N, Asikis T (2022) AI Pontryagin or how neural networks learn to control dynamical systems. Nature Comm. 13:333.
[18]. Asikis T (2021) Multi-objective optimization for value-sensitive and sustainable basket recommendations. Preprint, submitted November 10, https://arxiv.org/abs/2111.05944.
[19]. Diabt A,Jabbarzadeh A,Khosrojerdi A. A perishable product supply chain network d-esign problem with reliability and disruption considerations[J].International Journal of Prod-uction Economics, 2019, 212: [12] 125–138.
[20]. Zhang Tianxia, Li Hui (2016) Optimization of perishable food supply chain network considering transportation speed and carbon emissions. Industrial Engineering, 19 (4): 83-89, 97
[21]. Biuki M, Kazemi A, Alinezhad A (2020) An integrated location-routing-inventory model f-or sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260: 120842.
[22]. Whittemore AS, Saunders S (1977) Optimal inventory under stochastic demand with two supply options. SIAM J. Appl. Math. 32(2):293–305.
[23]. Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, vol. 703 (John Wiley & Sons, New York).
[24]. Scheller-Wolf A, Veeraraghavan S, van Houtum GJ (2007) Effective dual sourcing with a single index policy. Working paper, Carnegie Mellon University, Pittsburgh, PA.
[25]. Veeraraghavan S, Scheller-Wolf A (2008) Now or later: A simple policy for effective dual sourcing in capacitated systems. Oper. Res. 56(4):850–864Bengio Y, Le´onard N, Courville A (2013) Estimating or propagating gradients through stochastic neurons for conditional computation. Preprint, submitted August 15, https://arxiv.org/abs/1308.3432.
[26]. Sheopuri A, Janakiraman G, Seshadri S (2010) New policies for the stochastic inventory control problem with two supply sources. Oper. Res. 58(3):734–745.
[27]. Hua Z, Yu Y, Zhang W, Xu X (2015) Structural properties of the optimal policy for dual-sourcing systems with general lead times. IIE Trans. 47(8):841–850.
[28]. Allon, Gad ; Van Mieghem, Jan A (2010) Global Dual Sourcing: Tailored Base-Surge Allocation to Near- and Offshore Production. Management science, Vol.56 (1), p.110-124
[29]. Xin L, Goldberg DA (2018) Asymptotic optimality of tailored base-surge policies in dual-sourcing inventory systems. Management Sci. 64(1):437–452.
[30]. Dong C, Transchel S (2020) A dual sourcing inventory model for modal split transport: Structural properties and optimal solution. European journal of operational research, Vol.283 (3), p.883-900
[31]. Janakiraman G, Seshadri S, Sheopuri A (2015) Analysis of tailored base-surge policies in dual sourcing inventory systems. Management Sci. 61(7):1547–1561.
[32]. Lutter M, Ritter C, Peters J (2019) Deep Lagrangian networks: Using physics as model prior for deep learning. 7th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).
[33]. Zhong YD, Dey B, Chakraborty A (2020) Symplectic ODE-Net: Learning hamiltonian dynamics with control. 8th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).
[34]. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L (2021) Physics-informed machine learning. Nature Rev. Phys. 3(6):422–440.
[35]. Mowlavi S, Nabi S (2023) Optimal control of PDEs using physics-informed neural networks. J. Comput. Phys. 473:111731.
[36]. Roehrl MA, Runkler TA, Brandtstetter V, Tokic M, Obermayer S (2020) Modeling system dynamics with physics-informed neural networks based on Lagrangian mechanics. IFAC-PapersOnLine 53(2):9195–9200.
[37]. Bo¨ttcher L, Asikis T (2022) Near-optimal control of dynamical systems with neural ordinary differential equations. Machine Learn. Sci. Tech. 3(4):045004.
[38]. Bengio Y, Lodi A, Prouvost A (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon, European journal of operational research, Vol.290 (2), p.405-421
[39]. Bengio Y, Courvile A, Vincent P (2013) Representation Learning: A Review and New Perspectives, IEEE transactions on pattern analysis and machine intelligence, Vol.35 (8), p.1798-1828
[40]. Yin P, Lyu J, Zhang S, Osher S, Qi Y, Xin J (2018) Understanding straight-through estimator in training activation quantized neural nets. 7th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).
[41]. Williams RJ, Peng J (1990) An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Comput. 2(4):490–501.
[42]. Werbos PJ (1990) Backpropagation through time: What it does and how to do it. Proc. IEEE. 78(10):1550–1560.
[43]. Feldkamp L, Puskorius G (1993) Neural network control of an unstable process. Proc. 36th Midwest Sympos. Circuits Systems (IEEE, Piscataway, NJ), 35–40
[44]. Arrow KJ, Harris T, Marschak J (1951) Optimal inventory policy. Econometrica 19(3):250–272.
[45]. Douglas SC, Yu J (2018) Why ReLU units sometimes die: Analysis of single-unit error backpropagation in neural networks. 52nd Asilomar Conf. Signals Systems Comput. (IEEE, Piscataway, NJ), 864–868
Cite this article
Sun,F. (2024). RETRACTED ARTICLE: Research on the dual source inventory system of perishable product warehouse using recurrent neural networks. Applied and Computational Engineering,55,240-240.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Whitin T M. The theory of Inventory management [M]. N. J: Princeton University Press,1957.
[2]. Nahmias S. Perishable inventory theory: a review [J]. Operations Research,1982,30(4): 680-708.
[3]. Raafat F. Survey of literature on continously deteriorating inventory models [J]. Journal of the Operational Research Society,1991,42:27-37.
[4]. Scarf H, Karlin S (1958) Inventory models of the Arrow-Harris-Marschak type with time lag. Arrow KJ, Karlin S, Scarf HE, eds. Studies in the Mathematical Theory of Inventory and Production (Stanford University Press, Stanford, CA).
[5]. Sun J, Van Mieghem JA (2019) Robust dual sourcing inventory management: Optimality of capped dual index policies and smoothing. Manufacturing Service Oper. Management 21(4):912–931.
[6]. Goldberg DA, Reiman MI, Wang Q (2021) A survey of recent progress in the asymptotic analysis of inventory systems. Production Oper. Management 30(6):1718–1750
[7]. Barankin E (1961) A delivery-lag inventory model with an emergency provision. Naval Res. Logistics Quart. 8:285–311.
[8]. Fukuda Y (1964) Optimal policies for the inventory problem with negotiable leadtime. Management Sci. 10(4):690–708.
[9]. Bo¨ttcher L, Asikis T, Fragkos I (2023) Control of Dual-Sourcing Inventory Systems Using Recurrent Neural Networks. INFORMS journal on computing, 2023
[10]. Song JS, van Houtum GJ, Van Mieghem JA (2020) Capacity and inventory management: Review, trends, and projections. Manufacturing Service Oper. Management 22(1):36–46.
[11]. Gijsbrechts J, Boute RN, Van Mieghem JA, Zhang DJ (2022) Can deep reinforcement learning improve inventory management? Performance on lost sales, dual-sourcing, and multi-echelon problems. Manufacturing Service Oper. Management 24(3):1349–1368.
[12]. Linnainmaa S (1976) Taylor expansion of the accumulated rounding error. BIT 16(2):146–160.
[13]. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch. This work was part of the NIPS 2017 Autodiff workshop.
[14]. Baydin AG, Pearlmutter BA, Radul AA, Siskind JM (2018) Automatic differentiation in machine learning: A survey. J. Machine Learn. Res. 18(1):5595–5637.
[15]. Wang W, Axelrod S, Go´mez-Bombarelli R (2020) Differentiable molecular simulations for control and learning. Preprint, submitted February 27, https://arxiv.org/abs/2003.00868.
[16]. Asikis T, Bo¨ttcher L, Antulov-Fantulin N (2022) Neural ordinary differential equation control of dynamics on graphs. Physical Rev. Res. 4(1):013221.
[17]. Bo¨ttcher L, Antulov-Fantulin N, Asikis T (2022) AI Pontryagin or how neural networks learn to control dynamical systems. Nature Comm. 13:333.
[18]. Asikis T (2021) Multi-objective optimization for value-sensitive and sustainable basket recommendations. Preprint, submitted November 10, https://arxiv.org/abs/2111.05944.
[19]. Diabt A,Jabbarzadeh A,Khosrojerdi A. A perishable product supply chain network d-esign problem with reliability and disruption considerations[J].International Journal of Prod-uction Economics, 2019, 212: [12] 125–138.
[20]. Zhang Tianxia, Li Hui (2016) Optimization of perishable food supply chain network considering transportation speed and carbon emissions. Industrial Engineering, 19 (4): 83-89, 97
[21]. Biuki M, Kazemi A, Alinezhad A (2020) An integrated location-routing-inventory model f-or sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260: 120842.
[22]. Whittemore AS, Saunders S (1977) Optimal inventory under stochastic demand with two supply options. SIAM J. Appl. Math. 32(2):293–305.
[23]. Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, vol. 703 (John Wiley & Sons, New York).
[24]. Scheller-Wolf A, Veeraraghavan S, van Houtum GJ (2007) Effective dual sourcing with a single index policy. Working paper, Carnegie Mellon University, Pittsburgh, PA.
[25]. Veeraraghavan S, Scheller-Wolf A (2008) Now or later: A simple policy for effective dual sourcing in capacitated systems. Oper. Res. 56(4):850–864Bengio Y, Le´onard N, Courville A (2013) Estimating or propagating gradients through stochastic neurons for conditional computation. Preprint, submitted August 15, https://arxiv.org/abs/1308.3432.
[26]. Sheopuri A, Janakiraman G, Seshadri S (2010) New policies for the stochastic inventory control problem with two supply sources. Oper. Res. 58(3):734–745.
[27]. Hua Z, Yu Y, Zhang W, Xu X (2015) Structural properties of the optimal policy for dual-sourcing systems with general lead times. IIE Trans. 47(8):841–850.
[28]. Allon, Gad ; Van Mieghem, Jan A (2010) Global Dual Sourcing: Tailored Base-Surge Allocation to Near- and Offshore Production. Management science, Vol.56 (1), p.110-124
[29]. Xin L, Goldberg DA (2018) Asymptotic optimality of tailored base-surge policies in dual-sourcing inventory systems. Management Sci. 64(1):437–452.
[30]. Dong C, Transchel S (2020) A dual sourcing inventory model for modal split transport: Structural properties and optimal solution. European journal of operational research, Vol.283 (3), p.883-900
[31]. Janakiraman G, Seshadri S, Sheopuri A (2015) Analysis of tailored base-surge policies in dual sourcing inventory systems. Management Sci. 61(7):1547–1561.
[32]. Lutter M, Ritter C, Peters J (2019) Deep Lagrangian networks: Using physics as model prior for deep learning. 7th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).
[33]. Zhong YD, Dey B, Chakraborty A (2020) Symplectic ODE-Net: Learning hamiltonian dynamics with control. 8th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).
[34]. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L (2021) Physics-informed machine learning. Nature Rev. Phys. 3(6):422–440.
[35]. Mowlavi S, Nabi S (2023) Optimal control of PDEs using physics-informed neural networks. J. Comput. Phys. 473:111731.
[36]. Roehrl MA, Runkler TA, Brandtstetter V, Tokic M, Obermayer S (2020) Modeling system dynamics with physics-informed neural networks based on Lagrangian mechanics. IFAC-PapersOnLine 53(2):9195–9200.
[37]. Bo¨ttcher L, Asikis T (2022) Near-optimal control of dynamical systems with neural ordinary differential equations. Machine Learn. Sci. Tech. 3(4):045004.
[38]. Bengio Y, Lodi A, Prouvost A (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon, European journal of operational research, Vol.290 (2), p.405-421
[39]. Bengio Y, Courvile A, Vincent P (2013) Representation Learning: A Review and New Perspectives, IEEE transactions on pattern analysis and machine intelligence, Vol.35 (8), p.1798-1828
[40]. Yin P, Lyu J, Zhang S, Osher S, Qi Y, Xin J (2018) Understanding straight-through estimator in training activation quantized neural nets. 7th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).
[41]. Williams RJ, Peng J (1990) An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Comput. 2(4):490–501.
[42]. Werbos PJ (1990) Backpropagation through time: What it does and how to do it. Proc. IEEE. 78(10):1550–1560.
[43]. Feldkamp L, Puskorius G (1993) Neural network control of an unstable process. Proc. 36th Midwest Sympos. Circuits Systems (IEEE, Piscataway, NJ), 35–40
[44]. Arrow KJ, Harris T, Marschak J (1951) Optimal inventory policy. Econometrica 19(3):250–272.
[45]. Douglas SC, Yu J (2018) Why ReLU units sometimes die: Analysis of single-unit error backpropagation in neural networks. 52nd Asilomar Conf. Signals Systems Comput. (IEEE, Piscataway, NJ), 864–868