Recent Improvements Using Constraint Integer Programming for Resource Allocation and Scheduling

  • Stefan Heinz
  • Wen-Yang Ku
  • J. Christopher Beck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7874)


Recently, we compared the performance of mixed-integer programming (MIP), constraint programming (CP), and constraint integer programming (CIP) to a state-of-the-art logic-based Benders manual decomposition (LBBD) for a resource allocation/scheduling problem. For a simple linear relaxation, the LBBD and CIP models deliver comparable performance with MIP also performing well. Here we show that algorithmic developments in CIP plus the use of an existing tighter relaxation substantially improve one of the CIP approaches. Furthermore, the use of the same relaxation in LBBD and MIP models significantly improves their performance. While such a result is known for LBBD, to the best of our knowledge, the other results are novel. Our experiments show that both CIP and MIP approaches are competitive with LBBD in terms of the number of problems solved to proven optimality, though MIP is about three times slower on average. Further, unlike the LBBD and CIP approaches, the MIP model is able to obtain provably high-quality solutions for all problem instances.


Schedule Problem Constraint Programming Master Problem Linear Programming Relaxation Primal Heuristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Heinz
    • 1
  • Wen-Yang Ku
    • 2
  • J. Christopher Beck
    • 2
  1. 1.Zuse Institute BerlinBerlinGermany
  2. 2.Department of Mechanical & Industrial EngineeringUniversity of TorontoTorontoCanada

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