Abstract
We study the management of containers in a logistic chain between a supplier and a manufacturer in a ramp-up scenario where the demand is stochastic and expected to increase. This paper extends our previous study with deterministic demand. We consider a periodic review system with T periods of R time steps. The supplier sends full containers at every step and receives empty containers every period. We consider positive lead times. To face demand increase, the manufacturer can purchase reusable containers at a setup cost while the supplier can buy single-use disposables. Using a dynamic programming framework, we develop an online exact algorithm and an offline heuristic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jami, N., Schroeder, M., Küfer, K.: A model and polynomial algorithm for purchasing and repositioning containers. In: Management and Control of Production and Logistics (upcoming), vol. 7 (accepted 2016)
Janakiraman, G., Muckstadt, J.A.: Periodic review inventory control with lost sales and fractional lead times. Cornell University, School of Operations Research and Industrial Engineering (2004)
Halman, N., Klabjan, D., Mostagir, M., Orlin, J., Simchi-Levi, D.: A fully polynomial-time approximation scheme for single-item stochastic inventory control with discrete demand. Math. Oper. Res. 34(3), 674–685 (2009)
Murota, K.: Discrete Convex Analysis. SIAM, Philadelphia (2003)
Zipkin, P.: On the structure of lost-sales inventory models. Oper. Res. 56(4), 937–944 (2008)
Simchi-Levi, D., Chen, X., Bramel, J.: The Logic of Logistics: Theory, Algorithms, and Applications for Logistics Management. Springer Science & Business Media, New York (2013)
Chen, W., Dawande, M., Janakiraman, G.: Fixed-dimensional stochastic dynamic programs: an approximation scheme and an inventory application. Oper. Res. 62(1), 81–103 (2014)
Wagner, H.M., Whitin, T.M.: Dynamic version of the economic lot size model. Manag. Sci. 5(1), 89–96 (1958)
Bookbinder, J.H., Tan, J.Y.: Strategies for the probabilistic lot-sizing problem with service-level constraints. Manag. Sci. 34(9), 1096–1108 (1988)
Levi, R., Shi, C.: Approximation algorithms for the stochastic lot-sizing problem with order lead times. Oper. Res. 61(3), 593–602 (2013)
Vargas, V.: An optimal solution for the stochastic version of the Wagner-Whitin dynamic lot-size model. Eur. J. Oper. Res. 198(2), 447–451 (2009)
Özen, U., Doğru, M.K., Tarim, S.A.: Static-dynamic uncertainty strategy for a single-item stochastic inventory control problem. Omega 40(3), 348–357 (2012)
Erera, A.L., Morales, J.C., Savelsbergh, M.: Global intermodal tank container management for the chemical industry. Transp. Res. Part E Logistics Transp. Rev. 41(6), 551–566 (2005)
Erera, A.L., Morales, J.C., Savelsbergh, M.: Robust optimization for empty repositioning problems. Oper. Res. 57(2), 468–483 (2009)
Li, J.A., Leung, S.C., Wu, Y., Liu, K.: Allocation of empty containers between multi-ports. Eur. J. Oper. Res. 182(1), 400–412 (2007)
Lam, S.W., Lee, L.H., Tang, L.C.: An approximate dynamic programming approach for the empty container allocation problem. Transp. Res. Part C Emerg. Technol. 15(4), 265–277 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jami, N., Schröder, M., Küfer, KH. (2016). Online and Offline Container Purchasing and Repositioning Problem. In: Paias, A., Ruthmair, M., Voß, S. (eds) Computational Logistics. ICCL 2016. Lecture Notes in Computer Science(), vol 9855. Springer, Cham. https://doi.org/10.1007/978-3-319-44896-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-44896-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44895-4
Online ISBN: 978-3-319-44896-1
eBook Packages: Computer ScienceComputer Science (R0)