Strong LP Formulations for Scheduling Splittable Jobs on Unrelated Machines

Abstract

We study a natural generalization of the problem of minimizing makespan on unrelated machines in which jobs may be split into parts. The different parts of a job can be (simultaneously) processed on different machines, but each part requires a setup time before it can be processed. First we show that a natural adaptation of the seminal approximation algorithm for unrelated machine scheduling [11] yields a 3-approximation algorithm, equal to the integrality gap of the corresponding LP relaxation. Through a stronger LP relaxation, obtained by applying a lift-and-project procedure, we are able to improve both the integrality gap and the implied approximation factor to 1 + φ, where φ ≈ 1.618 is the golden ratio. This ratio decreases to 2 in the restricted assignment setting, matching the result for the classic version. Interestingly, we show that our problem cannot be approximated within a factor better than $\frac{e}{e-1}\approx 1.582$ (unless $\mathcal{P}=\mathcal{NP}$). This provides some evidence that it is harder than the classic version, which is only known to be inapproximable within a factor 1.5 − ε. Since our 1 + φ bound remains tight when considering the seemingly stronger machine configuration LP, we propose a new job based configuration LP that has an infinite number of variables, one for each possible way a job may be split and processed on the machines. Using convex duality we show that this infinite LP has a finite representation and can be solved in polynomial time to any accuracy, rendering it a promising relaxation for obtaining better algorithms.

This work was partially supported by Nucleo Milenio Información y Coordinación en Redes ICM/FIC P10-024F, by EU-IRSES grant EUSACOU, by the DFG Priority Programme ”Algorithm Engineering” (SPP 1307), by the Berlin Mathematical School, by ERC Starting Grant 335288-OptApprox, and by FONDECYT project 3130407.