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Summary of "Randomized Problem-Relaxation Solving for Over-Constrained Schedules" (Extended Abstract)

Authors Patrick Rodler , Erich Teppan , Dietmar Jannach

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Author Details

Patrick Rodler
  • Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Austria
Erich Teppan
  • Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Austria
Dietmar Jannach
  • Department of Artificial Intelligence and Cybersecurity, University of Klagenfurt, Austria

Funding

This work was supported by FFG, contract FO999910235.

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Patrick Rodler, Erich Teppan, and Dietmar Jannach. Summary of "Randomized Problem-Relaxation Solving for Over-Constrained Schedules" (Extended Abstract). In 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024). Open Access Series in Informatics (OASIcs), Volume 125, pp. 33:1-33:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.DX.2024.33

Abstract

We present a general framework for tackling over-constrained job shop scheduling problems (JSSP) where the volume of jobs (orders) exceeds the production capacity for a given planning horizon. The goal is to process as many or as utile jobs as possible within the available time. The suggested framework approaches this optimization problem by solving multiple randomly modified relaxed problem instances, thereby taking a sample in a solution space that covers all optimal solutions. By continuously storing the best solution found so far, the result is a complete anytime algorithm that incrementally approximates an optimal solution. The proposed framework allows for highly parallel computations, and all of its modules are treated as black-boxes, allowing them to be instantiated with the most performant algorithms for the respective sub-problems. Using IBM’s cutting-edge CP Optimizer suite, experiments on well-known JSSP benchmark problems demonstrate that the proposed framework consistently schedules more jobs in less computation time compared to a standalone constraint solver approach. Due to the generality of the proposed approach and its applicability to computing minimum-cardinality or most preferred minimal diagnoses, this work has the potential to positively impact the field of model-based diagnosis.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
  • Computing methodologies → Randomized search
  • Computing methodologies → Optimization algorithms
  • Computing methodologies → Parallel algorithms
  • Computing methodologies → Modeling and simulation
  • Applied computing → Industry and manufacturing
Keywords
  • diagnosis computation
  • randomized diagnosis computation
  • minimum-cardinality diagnoses
  • most preferred diagnoses
  • maximum-probability diagnoses
  • applications of diagnosis (over-constrained scheduling problems)
  • diagnosis-based optimization
  • constraint programming
  • CP Optimizer
  • job shop scheduling problem
  • job set optimization problem
  • operations research
  • scheduling
  • industry use cases
  • minimal subset subject to a monotone predicate (MSMP) problem
  • problem relaxation
  • sampling for optimization
  • anytime algorithm

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References

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