Solving job shop scheduling with setup times through constraint-based iterative sampling: an experimental analysis

  • Angelo OddiEmail author
  • Riccardo Rasconi
  • Amedeo Cesta
  • Stephen F. Smith


This paper presents a heuristic algorithm for solving a job-shop scheduling problem with sequence dependent setup times and min/max separation constraints among the activities (SDST-JSSP/max). The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of most critical conflicts and coupled with a non-deterministic choice heuristic to guide the base conflict resolution process. This constraint-based search is then embedded within a larger iterative-sampling search framework to broaden search space coverage and promote solution optimization. The efficacy of the overall heuristic algorithm is demonstrated empirically both on a set of previously studied job-shop scheduling benchmark problems with sequence dependent setup times and by introducing a new benchmark with setups and generalized precedence constraints.


Random-restart Constraint-based reasoning Job-shop scheduling Setup times Generalized precedence constraints 

Mathematics Subject Classifications (2010)

68T20 68M20 68W20 


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Angelo Oddi
    • 1
    Email author
  • Riccardo Rasconi
    • 1
  • Amedeo Cesta
    • 1
  • Stephen F. Smith
    • 2
  1. 1.Istituto di Scienze e Tecnologie della CognizioneConsiglio Nazionale delle RicercheRomeItaly
  2. 2.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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