# Heuristics for Job Scheduling Reoptimization

## Elad Iwanir, Tami Tamir

DOI: http://dx.doi.org/10.15439/2016F201

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 561–570 (2016)

Abstract. Many real-life applications involve systems that change dynamically over time. Thus, throughout the continuous operation of such a system, it is required to compute solutions for new problem instances, derived from previous instances. Since the transition from one solution to another incurs some cost, a natural goal is to have the solution for the new instance close to the original one (under a certain distance measure). We study reoptimization problems arising in scheduling systems. Formally, due to changes in the environment (out-of-order or new machines, modified jobs' processing requirements, etc.), the schedule needs to be modified. That is, jobs might be migrated from their current machine to a different one. Migrations are associated with a cost -- due to relocation overhead and machine set-up times. In some systems, a migration is also associated with job extension. The goal is to find a good modified schedule, with a low transition cost from the initial one.

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