1. //
  2. Start
  3. //
  4. Publications
  5. Evolutionary generation of dispatching r...
  6. Output formats
  7. //

Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. / Pickardt, Christoph W.; Hildebrandt, Torsten; Branke, Jürgen et al.
In: International Journal of Production Economics, Vol. 145, No. 1, 09.2013, p. 67-77.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Pickardt, CW, Hildebrandt, T, Branke, J, Heger, J & Scholz-Reiter, B 2013, 'Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems', International Journal of Production Economics, vol. 145, no. 1, pp. 67-77. https://doi.org/10.1016/j.ijpe.2012.10.016

APA

Pickardt, C. W., Hildebrandt, T., Branke, J., Heger, J., & Scholz-Reiter, B. (2013). Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. International Journal of Production Economics, 145(1), 67-77. https://doi.org/10.1016/j.ijpe.2012.10.016

Vancouver

Pickardt CW, Hildebrandt T, Branke J, Heger J, Scholz-Reiter B. Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. International Journal of Production Economics. 2013 Sept;145(1):67-77. doi: 10.1016/j.ijpe.2012.10.016

Bibtex

@article{2e8d13078ebc435b9676ec092a569e83,
title = "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems",
abstract = "We propose a two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules. The approach combines a genetic programming (GP) algorithm that evolves a composite rule from basic job attributes with an evolutionary algorithm (EA) that searches for a good assignment of rules to work centres. The hyper-heuristic is tested against its two components and rules from the literature on a complex dynamic job shop problem from semiconductor manufacturing. Results show that all three hyper-heuristics are able to generate (sets of) rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules. Moreover, the two-stage approach proves to outperform the GP and EA hyper-heuristic as it optimises on two different heuristic search spaces that appear to tap different optimisation potentials. The resulting rule sets are also robust to most changes in the operating conditions.",
keywords = "Dispatching rules, Evolutionary algorithms, Genetic programming, Hyper-heuristics, Production scheduling, Semiconductor manufacturing, Engineering",
author = "Pickardt, {Christoph W.} and Torsten Hildebrandt and J{\"u}rgen Branke and Jens Heger and Bernd Scholz-Reiter",
year = "2013",
month = sep,
doi = "10.1016/j.ijpe.2012.10.016",
language = "English",
volume = "145",
pages = "67--77",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

AU - Pickardt, Christoph W.

AU - Hildebrandt, Torsten

AU - Branke, Jürgen

AU - Heger, Jens

AU - Scholz-Reiter, Bernd

PY - 2013/9

Y1 - 2013/9

N2 - We propose a two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules. The approach combines a genetic programming (GP) algorithm that evolves a composite rule from basic job attributes with an evolutionary algorithm (EA) that searches for a good assignment of rules to work centres. The hyper-heuristic is tested against its two components and rules from the literature on a complex dynamic job shop problem from semiconductor manufacturing. Results show that all three hyper-heuristics are able to generate (sets of) rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules. Moreover, the two-stage approach proves to outperform the GP and EA hyper-heuristic as it optimises on two different heuristic search spaces that appear to tap different optimisation potentials. The resulting rule sets are also robust to most changes in the operating conditions.

AB - We propose a two-stage hyper-heuristic for the generation of a set of work centre-specific dispatching rules. The approach combines a genetic programming (GP) algorithm that evolves a composite rule from basic job attributes with an evolutionary algorithm (EA) that searches for a good assignment of rules to work centres. The hyper-heuristic is tested against its two components and rules from the literature on a complex dynamic job shop problem from semiconductor manufacturing. Results show that all three hyper-heuristics are able to generate (sets of) rules that achieve a significantly lower mean weighted tardiness than any of the benckmark rules. Moreover, the two-stage approach proves to outperform the GP and EA hyper-heuristic as it optimises on two different heuristic search spaces that appear to tap different optimisation potentials. The resulting rule sets are also robust to most changes in the operating conditions.

KW - Dispatching rules

KW - Evolutionary algorithms

KW - Genetic programming

KW - Hyper-heuristics

KW - Production scheduling

KW - Semiconductor manufacturing

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84880918018&partnerID=8YFLogxK

U2 - 10.1016/j.ijpe.2012.10.016

DO - 10.1016/j.ijpe.2012.10.016

M3 - Journal articles

AN - SCOPUS:84880918018

VL - 145

SP - 67

EP - 77

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - 1

ER -

Related by journal

Just-in-Time Logistics of a Supplier in the Car Manufacturing Industry

Fandel, G. & Reese, J., 11.1991, In: International Journal of Production Economics. 24, 1-2, p. 55-64 10 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature

Beske, P., Land, A. & Seuring, S., 06.2014, In: International Journal of Production Economics. 152, p. 131-143 13 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Other publications by the same author(s)

Einsatz von bestärkendem Lernen in der Reihenfolgeplanung mit dem Ziel der platzeffizienten Produktion

Müller, K. & Heger, J., 2025, Simulation in Produktion und Logistik 2025. Rank, S., Kühn, M. & Schmidt, T. (eds.). Dresden: Dresden University of Technology, 10 p. 40. (ASIM-Mitteilung; no. 194)(Tagungsband ASIM-Fachtagung Simulation in Produktion und Logistik; vol. 21).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Enhancing SME Production Efficiency: A Case Study on Autonomous Manufacturing Systems

Prüfer, O. C. & Heger, J., 2025, In: Procedia CIRP. 134, p. 313-318 6 p.

Research output: Journal contributions › Conference article in journal › Research › peer-review

Failure prediction by using a recurrent neural network in incremental sheet forming with active medium

Thiery, S., Zein El Abdine, M., Heger, J. & Ben Khalifa, N., 30.10.2025, In: International Journal of Material Forming. 18, 4, 15 p., 91.

Research output: Journal contributions › Journal articles › Research › peer-review

Manufacturing of irregular shapes through force control in incremental sheet forming with active medium

Thiery, S., Zein El Abdine, M., Heger, J. & Ben Khalifa, N., 01.05.2025, In: Advances in Industrial and Manufacturing Engineering. 10, 11 p., 100157.

Research output: Journal contributions › Journal articles › Research › peer-review

Optimizing dataset design for data-driven models of the deep drawing process using active transfer learning

Heinzel, C., Wollschläger, L., Nurmatov, B.-M., Heger, J. & Khalifa, N. B., 2025, In: Journal of Physics: Conference Series. 3104, 1, 11 p., 012064.

Research output: Journal contributions › Conference article in journal › Research › peer-review

DOI

Recently viewed

Publications

  1. Finding Similar Movements in Positional Data Streams
  2. N-term approximation in anisotropic function spaces
  3. A change of values is in the air
  4. Entropy-guided feature generation for structured learning of Portuguese dependency parsing
  5. Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa
  6. Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images
  7. Digital Control of a Camless Engine Using Lyapunov Approach with Backward Euler Approximation
  8. Evaluating OWL 2 reasoners in the context of checking entity-relationship diagrams during software development
  9. Using trait-based filtering as a predictive framework for conservation
  10. Tracing exploratory modes in digital collections of museum Web sites using reverse information architecture
  11. Different approaches to learning from errors: Comparing the effectiveness of high reliability and error management approaches
  12. A two-step approach for the prediction of mood levels based on diary data
  13. Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times
  14. Evaluating the construct validity of Objective Personality Tests using a multitrait-multimethod-Multioccasion-(MTMM-MO)-approach
  15. Kinematic self-calibration of non-contact five-axis measuring machine using improved genetic algorithm.
  16. Cross-document coreference resolution using latent features
  17. Learning with animations and simulations in a computer-based learning environment about torques
  18. Analyzing different types of moderated method effects in confirmatory factor models for structurally different methods
  19. A Wavelet Packet Tree Denoising Algorithm for Images of Atomic-Force Microscopy