Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging
Year: 2020
Type: preprint
Abstract: We introduce and study a general version of the fractional online knapsack problem with multiple knapsacks, heterogeneous constraints on which items can be assigned to which knapsack, and rate-limiting constraints on the assignment of items to knapsacks. This problem generalizes variations of the knapsack problem and of the one-way trading problem that have previously been treated separately, and additionally finds application to the real-time control of electric vehicle (EV) charging. We introduce a new algorithm that achieves a competitive ratio within an additive factor of one of the best achievable competitive ratios for the general problem and matches or improves upon the best-known competitive ratio for special cases in the knapsack and one-way trading literatures. Moreover, our analysis provides a novel approach to online algorithm design based on an instance-dependent primal-dual analysis that connects the identification of worst-case instances to the design of algorithms. Finally, we illustrate the proposed algorithm via trace-based experiments of EV charging.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CPS: TTP Option: Small: Adaptive Charging Network Research Portal, CNS: Core: Small: Energy and Load Management in Data Centers: Online Optimization and Learning, G417826940, AitF: Algorithmic challenges in smart grids: control, optimization & learning, NeTS: Large: Networked Markets: Theory and Applications, CPS: Medium: Collaborative Research: Demand Response & Workload Management for Data Centers with Increased Renewable Penetration
OpenAlex ID: W4288498358