A Robust Optimization Approach to Supply Chain Management. Bertsimas, D. & Thiele, A. In Kanade, T., Kittler, J., Kleinberg, J. M., Mattern, F., Mitchell, J. C., Nierstrasz, O., Pandu Rangan, C., Steffen, B., Sudan, M., Terzopoulos, D., Tygar, D., Vardi, M. Y., Weikum, G., Bienstock, D., & Nemhauser, G., editors, Integer Programming and Combinatorial Optimization, volume 3064, pages 86–100. Springer Berlin Heidelberg, Berlin, Heidelberg, 2004. Series Title: Lecture Notes in Computer Science![A Robust Optimization Approach to Supply Chain Management [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex We propose a general methodology based on robust optimization to address the problem of optimally controlling a supply chain subject to stochastic demand in discrete time. The attractive features of the proposed approach are: (a) It incorporates a wide variety of phenomena, including demands that are not identically distributed over time and capacity on the echelons and links; (b) it uses very little information on the demand distributions; (c) it leads to qualitatively similar optimal policies (basestock policies) as in dynamic programming; (d) it is numerically tractable for large scale supply chain problems even in networks, where dynamic programming methods face serious dimensionality problems; (e) in preliminary computational experiments, it often outperforms dynamic programming based solutions for a wide range of parameters.
@incollection{kanade_robust_2004,
address = {Berlin, Heidelberg},
title = {A {Robust} {Optimization} {Approach} to {Supply} {Chain} {Management}},
volume = {3064},
isbn = {978-3-540-22113-5 978-3-540-25960-2},
url = {http://link.springer.com/10.1007/978-3-540-25960-2_7},
abstract = {We propose a general methodology based on robust optimization to address the problem of optimally controlling a supply chain subject to stochastic demand in discrete time. The attractive features of the proposed approach are: (a) It incorporates a wide variety of phenomena, including demands that are not identically distributed over time and capacity on the echelons and links; (b) it uses very little information on the demand distributions; (c) it leads to qualitatively similar optimal policies (basestock policies) as in dynamic programming; (d) it is numerically tractable for large scale supply chain problems even in networks, where dynamic programming methods face serious dimensionality problems; (e) in preliminary computational experiments, it often outperforms dynamic programming based solutions for a wide range of parameters.},
language = {en},
urldate = {2022-02-25},
booktitle = {Integer {Programming} and {Combinatorial} {Optimization}},
publisher = {Springer Berlin Heidelberg},
author = {Bertsimas, Dimitris and Thiele, Aurélie},
editor = {Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Dough and Vardi, Moshe Y. and Weikum, Gerhard and Bienstock, Daniel and Nemhauser, George},
year = {2004},
doi = {10.1007/978-3-540-25960-2_7},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {/unread},
pages = {86--100},
}
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