---
title: "Large Neighborhood Search and Adaptive Randomized Decompositions for Flexible Jobshop Scheduling"
authors:
  - "Dario Pacino"
  - "Pascal Van Hentenryck"
doi: "10.5591/978-1-57735-516-8/IJCAI11-333"
url: "https://ijcai.org/proceedings/2011/333"
pdf: "https://ijcai.org/proceedings/2011/0333.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1997-2001"
track: ""
keywords:
---

# Large Neighborhood Search and Adaptive Randomized Decompositions for Flexible Jobshop Scheduling

Dario Pacino, Pascal Van Hentenryck

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1997-2001. DOI: [10.5591/978-1-57735-516-8/IJCAI11-333](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-333)

## Abstract

This paper considers a constraint-based scheduling approach to the flexible jobshop, a generalization of the traditional jobshop scheduling where activities have a choice of machines. It studies both large neighborhood (LNS) and adaptive randomized decomposition (ARD) schemes, using random, temporal, and machine decompositions. Empirical results on standard benchmarks show that, within 5 minutes, both LNS and ARD produce many new best solutions and are about 0.5% in average from the best-known solutions. Moreover, over longer runtimes, they improve 60% of the best-known solutions and match the remaining ones. The empirical results also show the importance of hybrid decompositions in LNS and ARD.