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Operations-Research-Spektrum

, Volume 17, Issue 2–3, pp 183–191 | Cite as

A genetic search algorithm to optimize job sequencing under a technological constraint in a rolling-mill facility

  • Étienne Mayrand
  • Pierre Lefrançois
  • Ossama Kettani
  • Marie -Hélène Jobin
Article

Abstract

This article presents some results from the application of a genetic search algorithm to solve a job scheduling problem where setup costs depend on the order of the jobs. An empirical study shows that, for small problems, the solutions given by the genetic algorithm are as good as those obtained with a mixed-integer linear program. For larger problems that are computationally infeasible, we benchmark the genetic solutions against traditional scheduling heuristics. We also study different population management strategies that can improve the performance of the algorithm. Finally, future research avenues are discussed.

Key words

Genetic search algorithm job scheduling sequence dependent setup costs 

Zusammenfassung

Betrachtet wird ein dynamisches Problem der Reihenfolgeplanung in einem Walzwerk. Ziel ist die Minimierung der Summe aus Lagerkosten für Halbfertigfabrikate und reihenfolgeabhängigen Rüstkosten. Zur Lösung wird ein genetischer Algorithmus benutzt. Zur Beurteilung der Leistungsfähigkeit des Verfahrens werden für kleinere Probleme exakte Lösungen herangezogen, für größere Probleme erfolgt ein Vergleich mit prioritätsregelbasierten Verfahren.

Schlüsselwörter

Genetische Algorithmen Maschinenbelegungsplanung reihenfolgeabhängige Rüstkosten 

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References

  1. Baker KR (1977) An Experimental Study of the Effectiveness of Rolling Schedules in Production Planning. Dec Sci 8:(1) 19–27Google Scholar
  2. Bookbinder JH, Tan J-Y (1988) Strategies for the Probabilistic Lot-Sizing Problem with Service-Level Constraint. Manag Sci 34:(9) 1096–1108Google Scholar
  3. Davis L (1991) Handbook og Genetic Algorithms. Van Nostrand Reinhold, New YorkGoogle Scholar
  4. DeJong KA (1975) An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Dissertation Abstracts Int 36:5140BGoogle Scholar
  5. Della Croce F, Tadei R, Volta G (1992) A Genetic Algorithm for the Job Shop Problem. D.A.I. Politecnico di Milano, ItalyGoogle Scholar
  6. Glover FW (1990) Tabu Search: A Tutorial. Interfaces 20:(4)74–94Google Scholar
  7. Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MassGoogle Scholar
  8. Holland JH (1980) Adaptive Algorithms for Discovering and Using General Patterns in Growing Knowledge-Bases. Int J Policy Anal Inf Syst 4:217–240Google Scholar
  9. Jeffcoat DE, Bulfin RL (1993) Simulated Annealing for Resource-Constrained Scheduling. Eur J Oper Res 70:43–51Google Scholar
  10. Jobin M-H (1990) Optimisation des politiques d'ordonnancement et de réglage à l'aide d'un modèle de simulation visuelle pour un laminoir à froid. CEFRIO. Document E-3Google Scholar
  11. Laguna M, Barnes JW, Glover FW (1991) Tabu Search Methods for a Single Machine Scheduling Problem. J Intelligent Manufacturing 2:63–74Google Scholar
  12. Lefrançois P, Alie Y (1990) A Service-Level Oriented Heuristic Approach to Production Planning in a Robotized Spinning-Mill. Int J Product Res 28:(7) 1293–1304Google Scholar
  13. Lefrançois P, Montreuil B (1994) An Object-Oriented Knowledge Representation for Intelligent Control of Manufacturing Work-stations. IIE Transact 26:11–26Google Scholar
  14. Lefrançois P, Roy M-C, Gamache G (1990) Estimation of the Mean Flow Time in a Rollingmill Facility. J Manufacturing Oper Manag 3:134–152Google Scholar
  15. Matsuo H, Suh CJ, Sullivan RS (1988) A Controlled Search Simulated Annealing Method for the General Jobshop Scheduling Problem, Working Paper 03-04-88, Department of Management, The University of Texas at AustinGoogle Scholar
  16. Morton TE, Pentico DW (1993) Heuristic Scheduling Systems. John Wiley & Sons, New YorkGoogle Scholar
  17. Syswerda G (1991) Schedule Optimization Using Genetic Algorithms. Handbook of Genetic Algorithms. Davis L (ed) Van Nostrand Reinhold, New York, pp 332–349Google Scholar
  18. Van Laarhoven PJM, Aarts EHL (1987) Simulated Annealing: Theory and Applications. Kluwer, BostonGoogle Scholar
  19. Van Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job Shop Scheduling by Simulated Annealing. Oper Res 40:113–125Google Scholar
  20. Whitley D, Starkweather T, Shaner D (1991) The Traveling Salesman and Sequence Scheduling: Quality Solutions Using Genetic Edge Recombination. Handbook of Genetic Algorithms. Davis L (ed) Van Nostrand Reinhold, New York, pp 350–372Google Scholar

Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • Étienne Mayrand
    • 1
  • Pierre Lefrançois
    • 1
  • Ossama Kettani
    • 1
  • Marie -Hélène Jobin
    • 2
  1. 1.SORCIIER Research Center on the International Competitiveness and the Engineering of the Networked Enterprise, Faculté des sciences de l'administrationUniversité LavalSte-FoyCanada
  2. 2.Service de l'enseignement de la productionHautes Études CommercialesMontréalCanada

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