Surrogate-based optimization of a periodic rescheduling algorithm

Teemu Ikonen, Keijo Heljanko, Iiro Harjunkoski

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
43 Downloads (Pure)


Periodic rescheduling is an iterative method for real-time decision-making on industrial process operations. The design of such methods involves high-level when-to-schedule and how-to-schedule decisions, the optimal choices of which depend on the operating environment. The evaluation of the choices typically requires computationally costly simulation of the process, which—if not sufficiently efficient—may result in a failure to deploy the system in practice. We propose the continuous control parameter choices, such as the re-optimization frequency and horizon length, to be determined using surrogate-based optimization. We demonstrate the method on real-time rebalancing of a bike sharing system. Our results on three test cases indicate that the method is useful in reducing the computational cost of optimizing an online algorithm in comparison to the full factorial sampling.

Original languageEnglish
Article numbere17656
Number of pages16
JournalAIChE Journal
Issue number6
Early online date9 Mar 2022
Publication statusPublished - Jun 2022
MoE publication typeA1 Journal article-refereed


  • online scheduling
  • re-optimization
  • rolling horizon
  • surrogate modeling
  • Kriging


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