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Optimal control of production-inventory systems with correlated demand inter-arrival and processing times

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  • Manafzadeh Dizbin, Nima
  • Tan, Barış

Abstract

We consider the production control problem of a production-inventory system with correlated demand inter-arrival and processing times that are modeled as Markovian Arrival Processes. The control problem is minimizing the expected average cost of the system in the steady-state by controlling when to produce an available part. We prove that the optimal control policy is the state-dependent threshold policy. We evaluate the performance of the system controlled by the state-dependent threshold policy by using the Matrix Geometric method. We determine the optimal threshold levels of the system by using policy iteration. We then investigate how the autocorrelation of the arrival and service processes impact the performance of the system. Finally, we compare the performance of the optimal policy with 3 benchmark policies: a state-dependent policy that uses the distribution of the inter-event times but assumes i.i.d.inter-event times, a single-threshold policy that uses both the distribution and also the autocorrelation, and a single-threshold policy that uses the distribution of the inter-event times but assumes they are not correlated. Our analysis demonstrates that ignoring autocorrelation in setting the parameters of the production policy causes significant errors in the expected inventory and backlog costs. A single-threshold policy that sets the threshold based on the distribution and also the autocorrelation performs satisfactorily for systems with negative autocorrelation. However, ignoring positive correlation yields high errors for the total cost. Our study shows that an effective production control policy must take correlations in service and demand processes into account.

Suggested Citation

  • Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
  • Handle: RePEc:eee:proeco:v:228:y:2020:i:c:s0925527320300839
    DOI: 10.1016/j.ijpe.2020.107692
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    1. Suresh P. Sethi & Feng Cheng, 1997. "Optimality of ( s , S ) Policies in Inventory Models with Markovian Demand," Operations Research, INFORMS, vol. 45(6), pages 931-939, December.
    2. Nasr, Walid W. & Maddah, Bacel, 2015. "Continuous (s, S) policy with MMPP correlated demand," European Journal of Operational Research, Elsevier, vol. 246(3), pages 874-885.
    3. C. Duri & Y. Frein & M. Di Mascolo, 2000. "Comparison among three pull control policies: kanban, base stock, and generalized kanban," Annals of Operations Research, Springer, vol. 93(1), pages 41-69, January.
    4. George Liberopoulos & Yves Dallery, 2000. "A unified framework for pull control mechanisms in multi‐stage manufacturing systems," Annals of Operations Research, Springer, vol. 93(1), pages 325-355, January.
    5. Gürkan, G. & Karaesmen, F. & Ozdemir, O., 2007. "Optimal threshold levels in stochastic fluid models via simulation-based optimization," Other publications TiSEM 8af032bd-47a7-4363-9649-8, Tilburg University, School of Economics and Management.
    6. Nima Manafzadeh Dizbin & Barış Tan, 2019. "Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes," Flexible Services and Manufacturing Journal, Springer, vol. 31(4), pages 1042-1076, December.
    7. Oktay Karabağ & Bariş Tan, 2019. "Purchasing, production, and sales strategies for a production system with limited capacity, fluctuating sales and purchasing prices," IISE Transactions, Taylor & Francis Journals, vol. 51(9), pages 921-942, September.
    8. Fangruo Chen & Jing-Sheng Song, 2001. "Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated Demand," Operations Research, INFORMS, vol. 49(2), pages 226-234, April.
    9. Jing‐Sheng Song & Paul H. Zipkin, 1996. "Evaluation of base‐stock policies in multiechelon inventory systems with state‐dependent demands. Part II: State‐dependent depot policies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(3), pages 381-396, April.
    10. Erhan Bayraktar & Michael Ludkovski, 2010. "Inventory management with partially observed nonstationary demand," Annals of Operations Research, Springer, vol. 176(1), pages 7-39, April.
    11. Jing-Sheng Song & Paul Zipkin, 1993. "Inventory Control in a Fluctuating Demand Environment," Operations Research, INFORMS, vol. 41(2), pages 351-370, April.
    12. Ganesh Janakiraman & John A. Muckstadt, 2009. "A Decomposition Approach for a Class of Capacitated Serial Systems," Operations Research, INFORMS, vol. 57(6), pages 1384-1393, December.
    13. Kevin B. Hendricks & John O. McClain, 1993. "The Output Process of Serial Production Lines of General Machines with Finite Buffers," Management Science, INFORMS, vol. 39(10), pages 1194-1201, October.
    14. He, Q. -M. & Jewkes, E. M. & Buzacott, J., 2002. "Optimal and near-optimal inventory control policies for a make-to-order inventory-production system," European Journal of Operational Research, Elsevier, vol. 141(1), pages 113-132, August.
    15. D. Beyer & S. P. Sethi & M. Taksar, 1998. "Inventory Models with Markovian Demands and Cost Functions of Polynomial Growth," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 281-323, August.
    16. Alp Muharremoglu & John N. Tsitsiklis, 2008. "A Single-Unit Decomposition Approach to Multiechelon Inventory Systems," Operations Research, INFORMS, vol. 56(5), pages 1089-1103, October.
    17. D. Beyer & S. P. Sethi, 1997. "Average Cost Optimality in Inventory Models with Markovian Demands," Journal of Optimization Theory and Applications, Springer, vol. 92(3), pages 497-526, March.
    18. Zhao, Ning & Lian, Zhaotong, 2011. "A queueing-inventory system with two classes of customers," International Journal of Production Economics, Elsevier, vol. 129(1), pages 225-231, January.
    19. Liu, Mingwu & Feng, Mengying & Wong, Chee Yew, 2014. "Flexible service policies for a Markov inventory system with two demand classes," International Journal of Production Economics, Elsevier, vol. 151(C), pages 180-185.
    20. Oded Berman & Eungab Kim, 2001. "Dynamic order replenishment policy in internet-based supply chains," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 53(3), pages 371-390, July.
    21. Francis de Véricourt & Fikri Karaesmen & Yves Dallery, 2002. "Optimal Stock Allocation for a Capacitated Supply System," Management Science, INFORMS, vol. 48(11), pages 1486-1501, November.
    22. Jianqiang Hu & Cheng Zhang & Chenbo Zhu, 2016. "( s , S ) Inventory Systems with Correlated Demands," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 603-611, November.
    23. S. Özekici & M. Parlar, 1999. "Inventory models with unreliable suppliersin a random environment," Annals of Operations Research, Springer, vol. 91(0), pages 123-136, January.
    24. Barış Tan & Svenja Lagershausen, 2017. "On the output dynamics of production systems subject to blocking," IISE Transactions, Taylor & Francis Journals, vol. 49(3), pages 268-284, March.
    25. Qingsong Jiang & Wei Xing & Ruihuan Hou & Baoping Zhou, 2015. "An Optimization Model for Inventory System and the Algorithm for the Optimal Inventory Costs Based on Supply-Demand Balance," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, December.
    26. Stanley Gershwin & Bariş Tan & Michael Veatch, 2009. "Production control with backlog-dependent demand," IISE Transactions, Taylor & Francis Journals, vol. 41(6), pages 511-523.
    27. Michael H. Veatch & Lawrence M. Wein, 1994. "Optimal Control of a Two-Station Tandem Production/Inventory System," Operations Research, INFORMS, vol. 42(2), pages 337-350, April.
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