Inventory control under demands and returns dependent supply disruptions

被引：0
|
作者：
Lou S.-Z. [1 ]
Rong X.-W. [1 ]
机构：
[1] School of Control Science and Engineering, Shandong University, Jinan
来源：
Kongzhi yu Juece/Control and Decision | 2021年 / 36卷 / 04期
关键词：
Disruption; Inventory; Level crossing; Lévy process; Markov-modulated; Return;
D O I：
10.13195/j.kzyjc.2019.0980
中图分类号：
学科分类号：
摘要：
Due to inventory fluctuating violently under demands and returns dependent supply disruptions, it is a very difficult problem to control the inventory effectively. Under the condition that the inventory level process represented as a Markov-modulated Lévy process, the expected cycle time and cost functions are developed by employing level crossing method, renewal process and martingale theories. Subsequently the functions are used to derive the long-run average cost rate model. Finally, the impact of the correlations between the demandsM (the returns) and the supply disruptions on the optimal inventory control policies is investigated, and the changes of the optimal policies under different disruption and return types are analyzed. Consequently, some new inventory-management insights are obtained. Copyright ©2021 Control and Decision.
引用
收藏
页码：1003 / 1009
页数：6
相关论文
共 11 条
  • [1] Lucker F, Seifert R W, Bicer I., Roles of inventory and reserve capacity in mitigating supply chain disruption risk, International Journal of Production Research, 57, 4, pp. 1238-1249, (2019)
  • [2] Azad N, Hassini E., Recovery strategies from major supply disruptions in single and multiple sourcing networks, European Journal of Operational Research, 275, 2, pp. 481-501, (2019)
  • [3] Kamalahmadi M, Parast M M., An assessment of supply chain disruption mitigation strategies, International Journal of Production Economics, 184, 2, pp. 210-230, (2017)
  • [4] Snyder L V, Atan Z, Peng P, Et al., OR/MS models for supply chain disruptions: A review, ⅡE Transactions, 48, 2, pp. 89-109, (2016)
  • [5] Tsao Y C, Raj P V, Yu V., Product substitution in different weights and brands considering customer segmentation and panic buying behavior, Industrial Marketing Management, 77, 2, pp. 209-220, (2019)
  • [6] Tran T, Gurnani H, Desiraju R., Optimal design of return policies, Marketing Science, 37, 4, pp. 649-667, (2018)
  • [7] Lou S Z, Tian X C., Modeling and control for inventory problems under stochastic supply disruptions and returns, Acta Automatica Sinica, 40, 11, pp. 2436-2444, (2014)
  • [8] Torabi S A, Namdar J, Hatefi S M, Et al., An enhanced possibilistic programming approach for reliable closed-loop supply chain network design, International Journal of Production Research, 54, 5, pp. 1358-1387, (2016)
  • [9] Avili M G, Naeini S G J, Moghaddam R T, Et al., A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions, Journal of Cleaner Production, 188, 3, pp. 425-442, (2018)
  • [10] Asmussen A, Kella O., A multi-dimensional martingale for Markov additive processes and its applications, Advance in Applied Probability, 32, 2, pp. 376-393, (2000)
12