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Online Linear Optimization with Inventory Management Constraints

Yang, Lin and Hajiesmaili, Mohammad H. and Sitaraman, Ramesh and Wierman, Adam and Mallada, Enrique and Wong, Wing S. (2020) Online Linear Optimization with Inventory Management Constraints. In: Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery , New York, NY, p. 7. ISBN 9781450379854. https://resolver.caltech.edu/CaltechAUTHORS:20210303-140911921

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Abstract

This paper considers the problem of online linear optimization with inventory management constraints. Specifically, we consider an online scenario where a decision maker needs to satisfy her timevarying demand for some units of an asset, either from a market with a time-varying price or from her own inventory. In each time slot, the decision maker is presented a (linear) price and must immediately decide the amount to purchase for covering the demand and/or for storing in the inventory for future use. The inventory has a limited capacity and can be used to buy and store assets at low price and cover the demand when the price is high. The ultimate goal of the decision maker is to cover the demand at each time slot while minimizing the cost of buying assets from the market. We propose ARP, an online algorithm for linear programming with inventory constraints, and ARPRate, an extended version that handles rate constraints to/from the inventory. Both ARP and ARPRate achieve optimal competitive ratios, meaning that no other online algorithm can achieve a better theoretical guarantee. To illustrate the results, we use the proposed algorithms in a case study focused on energy procurement and storage management strategies for data centers.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3393691.3394207DOIArticle
https://arxiv.org/abs/1901.04372arXivDiscussion Paper
ORCID:
AuthorORCID
Mallada, Enrique0000-0003-1568-1833
Alternate Title:Online Inventory Management with Application to Energy Procurement in Data Centers
Additional Information:© 2020 Copyright held by the owner/author(s). This work was funded by the National Science Foundation through the CNS-1908298, CNS-1763617, AitF-1637598, CNS-1518941, CPS-1544771, EPCN-1711188, CAREER-1752362, AMPS-1736448, TRIPODS-1934979 grants, and ARO: W911NF-17-1-0092, DoE: ENERGISE-DEEE0008006 grants, and a Google Faculty Research Award. Lin Yang wants to acknowledge the support from Schneider Electric, Lenovo Group (China) Limited and the Hong Kong Innovation and Technology Fund (ITS/066/17FP) under the HKUST-MIT Research Alliance Consortium.
Funders:
Funding AgencyGrant Number
NSFCNS-1908298
NSFCNS-1763617
NSFCCF-1637598
NSFCNS-1518941
NSFCPS-1544771
NSFEPCN-1711188
NSFECCS-1752362
NSFDMS-1736448
NSFCCF-1934979
Army Research Office (ARO)W911NF-17-1-0092
Department of Energy (DOE)DE-EE0008006
Google Faculty Research AwardUNSPECIFIED
Schneider ElectricUNSPECIFIED
Lenovo Group LimitedUNSPECIFIED
Hong Kong Innovation and Technology FundITS/066/17FP
Subject Keywords:Online linear optimization; inventory management; competitive online algorithms; energy procurement; data center
Record Number:CaltechAUTHORS:20210303-140911921
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210303-140911921
Official Citation:Lin Yang, Mohammad H. Hajiesmaili, Ramesh Sitaraman, Adam Wierman, Enrique Mallada, and Wing S.Wong. 2020. Online Linear Optimization with Inventory Management Constraints. In ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS ’20 Abstracts), June 8–12, 2020, Boston, MA, USA. ACM, New York, NY, USA, 1 page. https://doi.org/10.1145/3393691.3394207
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108295
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Mar 2021 22:33
Last Modified:03 Mar 2021 22:33

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