Synthesis and stochastic assessment of cost-optimal schedules

  • Angelika Mader
  • Henrik Bohnenkamp
  • Yaroslav S. Usenko
  • David N. Jansen
  • Johann Hurink
  • Holger Hermanns
Regular Contribution

Abstract

We treat the problem of generating cost-optimal schedules for orders with individual due dates and cost functions based on earliness/tardiness. Orders can run in parallel in a resource-constrained manufacturing environment, where resources are subject to stochastic breakdowns. The goal is to generate schedules while minimizing the expected costs. First, we estimate the distribution of each order type by simulation (assuming a reasonable machine/load model) and derive from the cost-function an optimal offset from the due date of each individual order. Second, these optimal offsets are then used to guide the generation of schedules which are responsible to resolve resource conflicts. Third, we evaluate the generated schedules by simulation. The approach is demonstrated by means of a non-trivial case-study from lacquer production. Optimal offsets are derived with the Modest/Möbius tool, schedules are generated using Uppaal Cora. The experimental results show that our approach achieves good results in all considered scenarios, and better results than an approach based on adding slack to processing times.

Keywords

Model checking Scheduling Heuristic methods Discrete event simulation 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Angelika Mader
    • 1
  • Henrik Bohnenkamp
    • 2
  • Yaroslav S. Usenko
    • 3
  • David N. Jansen
    • 4
  • Johann Hurink
    • 1
  • Holger Hermanns
    • 5
  1. 1.Faculty EWIUniversity of TwenteEnschedeThe Netherlands
  2. 2.Modeling and Verification of SoftwareRWTH Aachen UniversityAachenGermany
  3. 3.Centrum Wiskunde & Informatica Science Park 123AmsterdamThe Netherlands
  4. 4.Computing Science InstituteRadboud University NijmegenNijmegenThe Netherlands
  5. 5.Dependable Systems and Software GroupSaarland UniversitySaarbrückenGermany

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