In an online environment, jobs arrive over time and there is no information in advance about how many jobs are going to be processed and what their processing times are going to be. In this paper, we study the online scheduling of Boolean Satisfiability (SAT) and Mixed Integer Programming (MIP) instances that are well-known NP-complete problems. Typical online machine scheduling approaches assume that jobs are completed at some point in order to minimize functions related to completion time (e.g., makespan, minimum lateness, total weighted tardiness, etc). In this work, we formalize and present an online over time problem where arriving instances are subject to waiting time constraints. We propose computational approaches that combine the use of machine learning, MIP, and instance interruption heuristics. Unlike other approaches, we attempt to maximize the number of solved instances using single and multiple machine configurations. Our empirical evaluation with well-known SAT and MIP instances, suggest that our interruption heuristics can improve generic ordering policies to solve up to 21.6x and 12.2x more SAT and MIP instances. Additionally, our hybrid approach observed up to 90% of solved instances with respect to a semi clairvoyant policy (SCP).
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The authors would like to thank the anonymous reviewers for their comments and suggestions which helped to improve the paper. Robinson Duque is supported by the Universidad del Valle and also by Colciencias, the Colombian Administrative Department of Science, Technology and Innovation under the PhD scholarship program.
This article belongs to the Topical Collection: Topical Collection on Integration of Constraint Programming, Artificial Intelligence, and Operations Research
Guest Editor: Willem-Jan van Hoeve
Appendix A: Experiment results across all datasets
Appendix A: Experiment results across all datasets
In this appendix we present further results of our online scheduling approach tested with SAT and MIP instances with waiting time constraints. The results of our approach include six different datasets and we managed to extend some of our experiments using 40–60% and 50–50% partitions:
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Duque, R., Arbelaez, A. & Díaz, J.F. Online over time processing of combinatorial problems. Constraints 23, 310–334 (2018). https://doi.org/10.1007/s10601-018-9287-4
- Online scheduling
- Combinatorial problems
- Machine learning
- Regression models
- Classification models
- Runtime estimation
- Mixed integer programming