In this paper, a general production-inventory optimal control system is proposed which can be used in most cases that might arise in the theory of production-inventory control. The proposed general form is considered and approximately solved using neural networks. Since the obtained solutions are achieved based on neural networks, they have several advantages in practice. One of the important advantages is that the solutions can be easily used for post optimality and sensitivity analyses. The solutions of this model are compared with those of other existing methods and some illustrating notes are presented.
Accepté le :
DOI : 10.1051/ro/2016044
Mots clés : Optimal control, production planning, production-inventory systems, neural networks
@article{RO_2017__51_3_577_0,
author = {Pooya, Alireza and Pakdaman, Morteza},
title = {Analysing the solution of production-inventory optimal control systems by neural networks},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {577--590},
publisher = {EDP-Sciences},
volume = {51},
number = {3},
year = {2017},
doi = {10.1051/ro/2016044},
mrnumber = {3880513},
zbl = {1384.49020},
language = {en},
url = {http://www.numdam.org/articles/10.1051/ro/2016044/}
}
TY - JOUR AU - Pooya, Alireza AU - Pakdaman, Morteza TI - Analysing the solution of production-inventory optimal control systems by neural networks JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2017 SP - 577 EP - 590 VL - 51 IS - 3 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/ro/2016044/ DO - 10.1051/ro/2016044 LA - en ID - RO_2017__51_3_577_0 ER -
%0 Journal Article %A Pooya, Alireza %A Pakdaman, Morteza %T Analysing the solution of production-inventory optimal control systems by neural networks %J RAIRO - Operations Research - Recherche Opérationnelle %D 2017 %P 577-590 %V 51 %N 3 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/ro/2016044/ %R 10.1051/ro/2016044 %G en %F RO_2017__51_3_577_0
Pooya, Alireza; Pakdaman, Morteza. Analysing the solution of production-inventory optimal control systems by neural networks. RAIRO - Operations Research - Recherche Opérationnelle, Tome 51 (2017) no. 3, pp. 577-590. doi : 10.1051/ro/2016044. http://www.numdam.org/articles/10.1051/ro/2016044/
P. Aengchuan and B. Phruksaphanrat, Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FISANN) and FIS with adaptive neuro-fuzzy inference system (FISANFIS) for inventory control. J. Intell. Manufact. (2015) . | DOI
, Production-inventory system with finite production rate, stock-dependent demand, and variable holding cost. RAIRO: Oper. Res. 48 (2014) 135–150. | DOI | Numdam | MR | Zbl
, and , Optimal Control of Production, Remanufacturing and Refurbishing Activities in a Finite Planning Horizon Inventory System. J. Optim. Theory Appl. 30 (2015) | MR | Zbl
, Optimal production–inventory strategies for a HMMS-type reverse logistics system. Int. J. Prod. Econ. 81 (2003) 351–360. | DOI
and , Artificial neural network approach for solving fuzzy differential equations. Inform. Sci. 180 (2010) 1434–1457. | DOI | MR | Zbl
and , Optimal control problem via neural networks. Neural Comput. Appl. 23 (2013) 2093–2100. | DOI
, and , Optimal and self-tuning optimal control of a periodic-review hybrid production inventory system. Nonlin. Anal.: Hybrid Systems 1 (2007) 68–80. | MR | Zbl
, and Model predictive production planning in a three-stock reverse-logistics system with deteriorating items. Int. J. Syst. Sci. 2 (2015) 187–198.
Y. Huang and H. Jiang, Neural network observer-based optimal control for unknown nonlinear systems with control constraints. IEEE 2015 International Joint Conference on Neural Networks (2015) 1–7.
K.-P. Kistner and I. Dobos, Optimal production-inventory strategies for a reverse logistics system. Optimization, Dynamics, and Economic Analysis. Physica-Verlag HD (2000). | MR | Zbl
, and , Optimal control of nonlinear discrete time-varying systems using a new neural network approximation structure. Neurocomputing 156 (2015) 157–165. | DOI
and , Neural Networks Solution of Optimal Control Problems with Discrete Time Delays and Time-Dependent Learning of Infinitesimal Dynamic System. Springer Series in Bio-/Neuroinformatics 4 (2015) 315–332. | DOI | Zbl
, and , Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans. Neural Net. 9 (1998) 987–1000. | DOI
, , , and , Production quantity allocation for order fulfilment in the supply chain: a neural network based approach. Prod. Plan. Control 17 (2006) 378–389. | DOI
, Optimal control of the production – inventory system with deteriorating items and tradable emission permits. Int. J. Syst. Sci. 45 (2014) 2390–2401. | DOI | MR | Zbl
and , Optimal control of a stochastic production – inventory system under deteriorating items and environmental constraints. Int. J. Prod. Res. 53 (2015) 607–628. | DOI
and , Classifying inventory using an artificial neural network approach. Comput. Ind. Eng. 41 (2002) 389-404. | DOI
and , An artificial neural network model for optimization of finished goods inventory. Int. J. Ind. Eng. Comput. 2 (2011) 431–438.
S.P. Sethi and G.L. Thompson, Optimal control theory applications to management science and economics, 2nd edition. Springer ScienceBusiness Media, Inc. (2006). | MR | Zbl
and , A time dependent deteriorating order level inventory model for exponentially declining demand. Appl. Math. Sci. 2 (2008) 2795–2802. | Zbl
, and , Optimal control of a production inventory system with deteriorating items. Int. J. Syst. Sci. 37 (2006) 1111–1121. | DOI | MR | Zbl
, and , A neural network for the reduction of a product-driven system emulation model. Prod. Plan. Control 22 (2011) 767–781. | DOI
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