Scheduling and Packing Under Uncertainty

Franziska Eberle ()
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Franziska Eberle: London School of Economics and Political Science

A chapter in Operations Research Proceedings 2021, 2022, pp 9-14 from Springer

Abstract: Abstract Uncertainty in the input parameters is a major hurdle when trying to directly apply classical results from combinatorial optimization to real-word challenges. Hence, designing algorithms that handle incomplete knowledge provably well becomes a necessity. In view of the above, the author’s thesis [5] focuses on scheduling and packing problems under three models of uncertainty: stochastic, online, and dynamic. For this report, we highlight the results in online throughput maximization as well as dynamic multiple knapsack.

Keywords: Scheduling; Packing; Approximation algorithms; Online algorithms; Dynamic algorithms; Uncertainty (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-031-08623-6_2

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