Abstract
To improve the quality of after-sale service that is a new aspect for all manufacturing enterprises, the allocation of inventory reserves as well as reasonable dispatch between inventories have become the key to meet customer demand and reduce inventory cost. In this paper, a multi-stage safety inventory optimization model is constructed for after-sales service demand. The order quantity of inventory in the model is set to consider the changes of customer demand and fault loss under the influence of different quarters and regions. The cost and transportation time are also optimized by using multi-objective particle swarm optimization algorithm at the same time. Simulational results show that the proposed model can not only respond to the demand changes in different regions and different quarters timely, but also reduce the cost and time loss to meet customer demand. Compared with the methods that merely considers time and cost respectively, the proposed model is more suitable to solve the multi-stage inventory optimization problem across regions.
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References
Neale, J.J., Willems, S.P.: Managing Inventory in Supply Chains with Nonstationary Demand. Interfaces 39(5), 388–399 (2009)
Boyaci, T., Ray, S.: The Impact of Capacity Costs on Product Differentiation in Delivery Time, Delivery Reliability, and Price. Prod. Oper. Manage. 15(2), 179–197 (2006)
Dziri, E., Hammami, R., Jemai, Z.: Dynamic inventory optimization for a serial supply chain with stochastic and lead-time sensitive demand. IFAC PapersOnLine 52(13), 1034–1039 (2019)
Wei, Z.: Multi objective optimization model for collaborative multi-echelon inventory control in supply chain. Acta Automatica Sinica 33(2), 181–187 (2007)
Yang, L., Li, H., Campbell, J.F., Sweeney, D.C.: Integrated multi-period dynamic inventory classification and control. Int. J. Prod. Econ. 189, 86–96 (2017)
Chinello, E., Herbert-Hansen, Z.N.L., Khalid, W.: Assessment of the impact of inventory optimization drivers in a multi-echelon supply chain: Case of a toy manufacturer. Comput. Ind. Eng. 141, 106232 (2020)
Baiqiang, Z.: Research on Multi-stage Inventory Optimization of Supply Chain Based on Time Competition. Shenyang University (2013)
van Steenbergen, R., Mes, M.: Forecasting demand profiles of new products. Decis. Support Syst. 139, 113401 (2020)
Rau, H., Wu, M.: Integrated inventory model for deteriorating items under a multi-echelon supply chain environment. Int. J. Prod. Econ. 86(2), 155–168 (2003)
Durán, O., Carrasco, A., Afonso, P.S., Durán, P.A.: Evolutionary optimization of spare parts inventory policies: a life cycle costing perspective. IFAC PapersOnLine 52(13), 2243–2248 (2019)
Acknowledgement
This work was supported by the National Key R&D Program of China (2018YFB1701400), the National Natural Science Foundation of China (No.U1704158).
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Ma, M., Lang, Y., Liu, X., Mao, W., Fan, L., Liu, C. (2021). Multi-objective Collaborative Optimization of Multi-level Inventory: A Model Driven by After-Sales Service. In: Zu, Q., Tang, Y., Mladenović, V. (eds) Human Centered Computing. HCC 2020. Lecture Notes in Computer Science(), vol 12634. Springer, Cham. https://doi.org/10.1007/978-3-030-70626-5_38
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DOI: https://doi.org/10.1007/978-3-030-70626-5_38
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