Management Science and Financial Engineering

The Korean Operations Research and Management Science Society (한국경영과학회)
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Compromising Multiple Objectives in Production Scheduling: A Data Mining Approach

  • Hwang, Wook-Yeon (Department of Mechanical and Industrial Engineering, Qatar University) ;
  • Lee, Jong-Seok (Department of Systems Management Engineering, Sungkyunkwan University)
  • Received : 2014.04.04
  • Accepted : 2014.05.13
  • Published : 2014.05.30
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Abstract

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

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References

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