LP-based Approximation for Generalized Malleable Scheduling

Abstract: In malleable scheduling, jobs can be executed simultaneously on multiple machines with the processing time depending on the number of allocated machines. Each job is required to be executed non-preemptively and in unison, i.e., it has to occupy the same time interval on all its allocated machines. In this talk, we discuss a generalization of malleable scheduling, in which a function f(S, j) determines the processing time of job j on machine subset S. Under different discrete concavity assumptions on 1/f(S,j), we show that the scheduling problem can be approximated by a considerably simpler assignment problem and derive LP-based approximation algorithms. This is joint work with Dimitris Fotakis and Orestis Papadigenopoulos.

game theorymachine learningmathematical softwarecomputer science theorycombinatoricsoptimization and control

Audience: researchers in the topic

Mixed Integer Programming Workshop 2021

Series comments: The 18th Mixed Integer Programming Workshop will be held online on May 24-27, 2021.

It will feature 21 distinguished invited speakers covering most aspects of Mathematical Optimization, an interactive, gamified MIP student poster session with 50 posters, and a casual business meeting.

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