Incorporation of parameter prediction models of different fidelity into job shop scheduling

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Scheduling of industrial job shop processes is normally conducted using estimates of parameters (e.g. processing times) defining the optimization problem. Inaccuracy in these estimated parameters can significantly affect the optimality, or even feasibility, of the scheduling solution. In this work, we incorporate data-driven parameter prediction models of different fidelity into a unit-specific continuous time scheduling model, and investigate the dependency of the solution quality on the prediction model fidelity. Our high-fidelity prediction model is based on Gaussian processes (GP); more specifically we use the maximum a posteriori probability (MAP) estimate. The low and medium-fidelity prediction models rely on determining the average processing time or average processing rate, respectively, from the dataset. In our test case, involving prediction of taxi durations in New York City, the use of GP prediction model yielded, on average, 5.8% and 1.8% shorter realized make spans in comparison to using the low and medium-fidelity prediction models, respectively.
Original languageEnglish
Pages (from-to)142-147
Issue number1
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventIFAC Symposium on Dynamics and Control of Process Systems, including Biosystems - Florianopolis, Brazil
Duration: 23 Apr 201926 Apr 2019
Conference number: 12


  • Scheduling Algorithms
  • Optimization
  • Parameter Estimation
  • Machine Learning
  • Gaussian Processes


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