On the Fine-Grained Parameterized Complexity of Partial Scheduling to Minimize the Makespan.

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We study a natural variant of scheduling that we call partial scheduling: In this variant an instance of a scheduling problem along with an integer k is given and one seeks an optimal schedule where not all, but only k jobs, have to be processed. Specifically, we aim to determine the fine-grained parameterized complexity of partial scheduling problems parameterized by k for all variants of scheduling problems that minimize the makespan and involve unit/arbitrary processing times, identical/unrelated parallel machines, release/due dates, and precedence constraints. That is, we investigate whether algorithms with runtimes of the type f (k) n^ 풪 (1) or n^ 풪 (f (k)) exist for a function f that is as small as possible. Our contribution is two-fold: First, we categorize each variant to be either in 햯, NP-complete and fixed-parameter tractable by k, or 햶 [1]-hard parameterized by k. Second, for many interesting cases we further investigate the run time on a finer scale and obtain run times that are (almost) optimal assuming the Exponential Time Hypothesis. As one of our main technical contributions, we give an 풪 (8^ kk (| V|+| E|)) time algorithm to solve instances of partial scheduling problems minimizing the makespan with unit length jobs, precedence constraints and release dates, where G=(V, E) is the graph with precedence constraints.
Original languageUndefined
Publication statusPublished - 2020

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