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Philipp Danzinger

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TIST Journal 2023 Journal Article

A System for Automated Industrial Test Laboratory Scheduling

  • Philipp Danzinger
  • Tobias Geibinger
  • David Janneau
  • Florian Mischek
  • Nysret Musliu
  • Christian Poschalko

Automated scheduling solutions are tremendously important for the efficient operation of industrial laboratories. The Test Laboratory Scheduling Problem (TLSP) is an extension of the well-known Resource Constrained Project Scheduling Problem (RCPSP) and captures the specific requirements of such laboratories. In addition to several new scheduling constraints, it features a grouping phase, where the jobs to be scheduled are assembled from smaller units. In this work, we introduce an innovative scheduling system that allows the efficient and flexible generation of schedules for TLSP. It features a new Constraint Programming model that covers both the grouping and the scheduling aspect, as well as a hybrid Very Large Neighborhood Search that internally uses the CP model. Our experimental results on generated and real-world benchmark instances show that good results can be obtained even compared to settings which have a good grouping already provided, including several new best known solutions for these instances. Our algorithms for TLSP have been successfully implemented in a real-world industrial test laboratory. We provide a detailed description of the deployed system as well as additional useful soft constraints supported by the solvers and general lessons learned. This includes a discussion of the choice of soft constraint weights, with an analysis on the impact and relation of different objectives to each other. Our experiments show that some soft constraints complement each other well, while others require explicit trade-offs via their relative weights.

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ICAPS Conference 2020 Conference Paper

Solving the Test Laboratory Scheduling Problem with Variable Task Grouping

  • Philipp Danzinger
  • Tobias Geibinger
  • Florian Mischek
  • Nysret Musliu

The Test Laboratory Scheduling Problem (TLSP) is an extension of the Resource-Constrained Project Scheduling Problem (RCPSP). Besides several additional constraints, it includes a grouping phase where the jobs to be scheduled have to be assembled from smaller tasks and derive their properties from this grouping. Previous solution approaches for TLSP have focused primarily on the scheduling subproblem (TLSP-S), for which it is assumed that a suitable grouping is already given as part of the input. In this paper, we provide for the first time a solution approach that encompasses the full problem including grouping. We propose both a Constraint Programming model for TLSP and a Very Large Neighborhood Search algorithm based on that model. Furthermore, we apply our algorithms to real-world instances as well as randomly generated ones and compare our results to the best existing solutions. Experimental results show that our solution methods consistently outperform those for TLSP-S when both are initialised with a good grouping and in many cases even when this grouping is provided only to the latter.

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