Boosting the Performance of Iterative Flattening Search

  • Angelo Oddi
  • Nicola Policella
  • Amedeo Cesta
  • Stephen F. Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4733)


Iterative Flattening search is a local search schema introduced for solving scheduling problems with a makespan minimization objective. It is an iterative two-step procedure, where on each cycle of the search a subset of ordering decisions on the critical path in the current solution are randomly retracted and then recomputed to produce a new solution. Since its introduction, other variations have been explored and shown to yield substantial performance improvement over the original formulation. In this spirit, we propose and experimentally evaluate further improvements to this basic local search schema. Specifically, we examine the utility of operating with a more flexible solution representation, and of integrating iterative-flattening search with a complementary tabu search procedure. We evaluate these extensions on large benchmark instances of the Multi-Capacity Job-Shop Scheduling Problem (mcjssp) which have been used in previous studies of iterative flattening search procedures.


Schedule Problem Tabu Search Critical Path Precedence Constraint Tabu Search Algorithm 
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 2007

Authors and Affiliations

  • Angelo Oddi
    • 1
  • Nicola Policella
    • 2
  • Amedeo Cesta
    • 1
  • Stephen F. Smith
    • 3
  1. 1.ISTC-CNR, RomeItaly
  2. 2.European Space Agency, DarmstadtGermany
  3. 3.The Robotics Institute, Carnegie Mellon UniversityUSA

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