Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
We study the scheduling situation where n tasks, subjected to release dates and due dates, have to be scheduled on m parallel processors. We show that, when tasks have unit processing times and either require 1 or m processors simultaneously, the minimum maximal tardiness can be computed in polynomial time. Two algorithms are described. The first one is based on a linear programming formulation of the problem while the second one is a combinatorial algorithm. The complexity status of this "tall/small" task scheduling problem P|r i,p i = 1, size i ∈ {1, m}|T max was unknown before, even for two processors.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Victor Akinwande, Megan Macgregor, et al.
IJCAI 2024
Annina Riedhauser, Viacheslav Snigirev, et al.
CLEO 2023
Guy Even, Sudipto Guha, et al.
STOC 2000