A User Experiment on Interactive Reoptimization Using Iterated Local Search

  • David MeignanEmail author
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


This article presents an experimental study conducted with subjects on an interactive reoptimization method applied to a shift scheduling problem . The studied task is the adjustment, by a user, of candidate solutions provided by an optimization system in order to introduce a missing constraint. Two procedures are compared on this task. The first one is a manual adjustment of solutions assisted by a software that dynamically computes the cost of the current solution. The second procedure is based on reoptimization. For this procedure, the user defines some desired changes on a solution, and then a reoptimization method is applied to integrate the changes and reoptimize the rest of the solution. This process is iterated with additional desired changes until a satisfactory solution is obtained. For this interactive approach, the proposed reoptimization procedure is an iterated local search metaheuristic. The experiment, conducted with 16 subjects, provides a quantitative evaluation of the manual and reoptimization approaches. The results show that, even for small local adjustments, the manual modification of a solution has an important impact on the quality of the solution. In addition, the experiment demonstrates the efficiency of the interactive reoptimization approach and the adequacy of the iterated local search method for reoptimizing solutions. Finally, the experiment revealed some limitations of interactive reoptimization that are discussed in this article.


Interactive optimization Shift scheduling Heuristic Reoptimization 



This work was supported by the Deutsche Forschungsgemeinschaft (DFG), under grant ME 4045/2-1, for the project “Interactive metaheuristics for optimization-based decision support systems”. I acknowledge the support of Google, through the Google Focused Grant Program on “Mathematical Optimization and Combinatorial Optimization in Europe” (2012), which allowed us to initiate this study. I want to thank Sigrid Knust for her support throughout the project.


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Universität OsnabrückInstitut für InformatikOsnabrückGermany

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