Scalable Neighborhood Local Search for Single-Machine Scheduling with Family Setup Times
Kaja Balzereit, Niels Gr\"uttemeier, Nils Morawietz, Dennis Reinhardt,, Stefan Windmann, Petra Wolf

TL;DR
This paper introduces a scalable local search heuristic for single-machine scheduling with family setup times, balancing solution quality and computational efficiency for large instances.
Contribution
It proposes a parameterized local search method with analysis of trade-offs between neighborhood size and running time, applicable to NP-hard scheduling problems.
Findings
Effective heuristic for large instances
Trade-off analysis between neighborhood size and runtime
Preliminary experimental validation shows promising results
Abstract
In this work, we study the task of scheduling jobs on a single machine with sequence dependent family setup times under the goal of minimizing the makespan, that is, the completion time of the last job in the schedule. This notoriously NP-hard problem is highly relevant in practical productions and requires heuristics that provide good solutions quickly in order to deal with large instances. In this paper, we present a heuristic based on the approach of parameterized local search. That is, we aim to replace a given solution by a better solution having distance at most in a pre-defined distance measure. This is done multiple times in a hill-climbing manner, until a locally optimal solution is reached. We analyze the trade-off between the allowed distance and the algorithm's running time for four natural distance measures. Example of allowed operations for our considered distance…
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Taxonomy
TopicsScheduling and Optimization Algorithms · Advanced Manufacturing and Logistics Optimization · Advanced Wireless Network Optimization
