Weighted Tardiness Scheduling with Sequence-Dependent Setups
This set of benchmark instances for the problem known as Weighted Tardiness Scheduling with Sequence Dependent Setups originated in as part of the Ph.D. Dissertation research of Vincent A. Cicirello from Carnegie Mellon University's Robotics Institute. Weighted Tardiness Scheduling with Sequence-Dependent Setups is an NP-Hard single machine scheduling problem. The problem instances in this benchmark set have varying levels of duedate tightness, duedate range, and setup time severity. This benchmark set includes the instances, the current best known solutions, a problem generator, and links to relevant publications. The benchmark set is licensed under the MIT license.
There are multiple ways of downloading the benchmark instances:
- You can download the benchmark instances here as a zip file (wtsds-instances.zip), containing the instances in plain text files.
- The benchmark set is also available in a GitHub repository, which contains the instances in plain text files.
- Alternatively, the benchmark set is available from: Harvard Dataverse.
For Java source code to generate instances of the problem, see the Open Source Software tab.
How to Cite
To cite this benchmark problem set in your research, cite the following technical report:
- Cicirello, V.A. (2003). Weighted tardiness scheduling with sequence-dependent setups: A benchmark library [Technical Report]. Intelligent Coordination and Logistics Laboratory, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.
Publications Associated with this Benchmark Set:
Original appearance, description of the benchmark set:
- Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance.
Vincent A. Cicirello.
PhD thesis, The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, July 2003.
[PDF] [BIB] - Weighted Tardiness Scheduling with Sequence-Dependent Setups: A Benchmark Library.
Vincent A. Cicirello.
Technical Report, Intelligent Coordination and Logistics Laboratory, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, February 2003.
[PDF] [BIB]
The Java implementation of the problem set generator was first available in conjunction with the following publication:
- The Challenge of Sequence-Dependent Setups: Proposal for a Scheduling Competition Track on One Machine Sequencing Problems.
Vincent A. Cicirello.
In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS) Workshop on Scheduling a Scheduling Competition. AAAI Press, September 2007.
[PDF] [BIB] [PUB]
Various updates of the best known solutions:
- Heuristic Sequencing Crossover: Integrating Problem Dependent Heuristic Knowledge into a Genetic Algorithm.
Vincent A. Cicirello.
In Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, FLAIRS-23, pages 14-19. AAAI Press, May 2010.
[PDF] [BIB] [PUB] - Weighted Tardiness Scheduling with Sequence-Dependent Setups: A Benchmark Problem for Soft Computing.
Vincent A. Cicirello.
In Applications of Soft Computing: Updating the State of the Art, volume 52 of Advances in Soft Computing, pages 189-198. Springer, 2009. doi:10.1007/978-3-540-88079-0_19
[PDF] [BIB] [DOI] - On the Design of an Adaptive Simulated Annealing Algorithm.
Vincent A. Cicirello.
In Proceedings of the International Conference on Principles and Practice of Constraint Programming First Workshop on Autonomous Search. AAAI Press, September 2007.
[PDF] [BIB] [PUB] - Non-Wrapping Order Crossover: An Order Preserving Crossover Operator that Respects Absolute Position.
Vincent A. Cicirello.
In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'06), volume 2, pages 1125-1131. ACM Press, July 2006. doi:10.1145/1143997.1144177
Nominated for the Genetic Algorithms Track Best Paper Award.
[PDF] [BIB] [DOI] [PUB] - The Max K-Armed Bandit: A New Model of Exploration Applied to Search Heuristic Selection.
Vincent A. Cicirello and Stephen F. Smith.
In The Proceedings of the Twentieth National Conference on Artificial Intelligence, volume 3, pages 1355-1361. AAAI Press, July 2005.
Winner of the AAAI 2005 Outstanding Paper Award.
[PDF] [BIB] [PUB] - Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics.
Vincent A. Cicirello and Stephen F. Smith.
Journal of Heuristics, 11(1): 5-34, January 2005. doi:10.1007/s10732-005-6997-8
[PDF] [BIB] [DOI]