Single machine scheduling with job-dependent convex cost and arbitrary precedence constraints

Abstract

In this work we combine resource augmentation and alpha-point scheduling techniques, which have resulted in very good performance scheduling algorithms, to compute approximate solutions for a general family of scheduling problems: each job has a convex non-decreasing cost function and the goal is to compute a schedule that minimizes the total cost subject to precedence constraints.We show that our algorithm is a O(1)O(1)-speed 11-approximation algorithm and our numerical experiments show that the speed-scaling ratio required is close to 1.1.

Publication
Operations Research Letters
Rodrigo A. Carrasco
Rodrigo A. Carrasco
Associate Professor & the UC Data Science Initiative Director
Garud Iyengar
Garud Iyengar
Columbia University
Cliff Stein
Cliff Stein
Columbia University

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