Skip to main content
SpringerLink
Log in

An artificial immune system for solving production scheduling problems: a review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

This article reviews the production scheduling problems focusing on those related to flexible job-shop scheduling. Job-shop and flexible job-shop scheduling problems are one of the most frequently encountered and hardest to optimize. This article begins with a review of the job-shop and flexible job-shop scheduling problem, and follow by the literature on artificial immune systems (AIS) and suggests ways them in solving job-shop and flexible job-shop scheduling problems. For the purposes of this study, AIS is defined as a computational system based on metaphors borrowed from the biological immune system. This article also, summarizes the direction of current research and suggests areas that might most profitably be given further scholarly attention.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Adams J, Balas E, Zawack D (1986) The shifting bottleneck procedure for job shop scheduling. Carnegie-Mellon University Management Science Research Group, Pittsburgh

    Google Scholar 

  • Aickelin U, Burke E, Mohamed Din A (2004) Investigating artificial immune systems for job shop rescheduling in changing environments. In: 6th International conference in adaptive computing in design and manufacture, Bristol, UK

  • Bagheri A, Zandieh M, Mahdavi I, Yazdani M (2010) An artificial immune algorithm for the flexible job-shop scheduling problem. Future Generation Comput Syst 26: 533–541

    Article  Google Scholar 

  • Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New York

    Google Scholar 

  • Baptiste P, Le Pape C (1996) A constraint-based branch and bound algorithm for preemptive job-shop scheduling. In: 5th IEE, international symposium on assembly and task planning, Besançon

  • Bersini H (1991) Immune network and adaptive control in towards a practice of autonomous systems. In: Proceedings of the first European conference on artificial life. MIT Press, Cambridge, pp 217–226

  • Bersini H, Varela FJ (1990) Hints for adaptive problem solving gleaned from immune networks. Parallel Prob Solv Nat pp 343–354

  • Bersini H, Varela FJ (1991) The immune recruitment mechanism: a selective evolutionary strategy. In: Proceedings of the international conference on genetic algorithm, pp 520–526

  • Bruker P, Schlie R (1990) Job-shop scheduling with multi-purpose machine. Computing 45: 369–375

    Article  MathSciNet  Google Scholar 

  • Burnet FM (1978) Clonal selection and after. In: Bell GI, Perelson AS, Pimbley GH (eds) Theoretical immunology. Marcel Dekker Inc, New York, pp 63–85

    Google Scholar 

  • Chueh CH (2004) An immune algorithm for engineering optimization. Dissertation Tatung University, Taipei

    Google Scholar 

  • Costa AM, Vargas PA, Von Zuben FJ, Frabca PM (2002) Makespan minimization on parallel processors: an immune-based approach. In: Proceedings of the 2002 congress on evolutionary computation. IEEE Press, pp 920–925

  • Dausset J (1980) The major histocompatibality complex in man—past, present and future concepts. Nobel Lecturer University of Paris, Paris

    Google Scholar 

  • Dreher H (1995) The immune power personality. Penguin Books, Baltimore

    Google Scholar 

  • Fabio F, Maurizio R (2006) Comparison of artificial immune systems and genetic algorithms in electrical engineering. Comput Math Electr Electron Eng 25(4): 792–811

    Article  MATH  Google Scholar 

  • Farmer JD, Packard NH, Perelson AS (1986) The immune systems, adaptation and machine learning. Phys D 22: 87–204

    MathSciNet  Google Scholar 

  • French S (1982) Sequencing and scheduling, mathematics and its applications. Ellis Horwood Limited, Chichester

    Google Scholar 

  • Gen M, Tsujimura Y, Kubota E (1994) Solving job-shop scheduling problem using genetic algorithms. In: Proceedings of the 16th international conferences on computer and industrial engineering, Ashikaga, Japan, pp 576–579

  • Giffler B, Thomson G (1960) Algorithms for solving production scheduling problems. Oper Res VIII: 487–503

    Article  Google Scholar 

  • Hart EJT (2008) Application areas of AIS: the past, the present and the future. Appl Soft Comput 8: 191–201

    Article  Google Scholar 

  • Hart E, Ross P (1999a) An immune system approach to scheduling in changing environments. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceeding of the GECCO 1999. Morgan Kaufmann, Los Altos, pp 1559–1565

  • Hart E, Ross P (1999b) The evolution and analysis of a potential antibody library for job-shop scheduling. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimisation. McGraw-Hill, London, pp 185–202

    Google Scholar 

  • Hart E, Ross P, Nelson J (1998) Producing robust schedules via, an artificial immune system. In: Proceeding of the ICEC ’98. IEEE Press, Cambridge, pp 464–469

  • Hermann JW (2006) Improving production scheduling: integrating organizational, decision-making, and problem-solving perspectives. In: Industrial Engineering Research Conference, Orlando, Florida

  • Hoffman GW (1986) A neural network model based on the nalogy with the immune system. J Theor Biol 122: 33–67

    Article  Google Scholar 

  • Ishida Y (1990) Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model. In: Proceedings of the international joint conference on neural networks, pp 777–782

  • Ishida Y (1993) An immune network model and its applications to process diagnosis. Syst Comput Jpn 24(6): 646–651

    Article  Google Scholar 

  • Janeway CA Jr (1992) The immune system evolved to discriminate infectious nonself from noninfectious self. Immunol Today 13(1): 11–16

    Article  Google Scholar 

  • Jensen MT (2003) Generating robust and flexible jobshop schedules using genetic algorithms. IEEE Trans Evol Comput 7(3): 275–288

    Article  Google Scholar 

  • Jerne NK (1973) The immune system. Scientif Am 229(1): 52–60

    Article  Google Scholar 

  • Kacem I, Hammadi S, Borne P (2002) Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans Syst Man Cybern 32(1): 1–13

    Article  Google Scholar 

  • Land AH, Doig AG (1960) An automatic method of solving discrete programming problems. Econometricca 28(3): 497–520

    Article  MathSciNet  MATH  Google Scholar 

  • Luh GC, Chueh CH (2007) Job shop optimization using multi-modal immune algorithm. In: Okuno HG, Ali M (eds) IEA/AIE 2007, LNAI 4570. Springer, Berlin, pp 1127–1137

  • Ong ZX, Tay JC, Kwoh CK (2005) Applying the clonal selection principle to find flexible job-shop schedules. LNCS 3627: 442–455

    Google Scholar 

  • Pinedo M (2002) Scheduling: theory, algorithms and systems. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Pezzella F, Morganti G, Ciaschetti G (2008) A genetic algorithm for the flexible job-shop scheduling problem. Comput Oper Res 35(10): 3202–3212

    Article  MATH  Google Scholar 

  • Roshanaei V, Naderi B, Jolai F, Khalili M (2009) A variable neighborhood search for job shop scheduling with set-up times to minimize Makespan. Future Generation Comput Syst 25: 654–661

    Article  Google Scholar 

  • Tarlinton D (1998) Germinal centers: form and function. Curr Oper Immune 10: 245–251

    Article  Google Scholar 

  • Tay JC, Ho NB (2004) GENACE: an efficient cultural algorithm for solving the flexible job-shop problem. In: Proceedings of the IEEE congress of evolutionary computation, pp 1759–1766

  • Tomoyuki M (2003) An application of immune algorithms for job-shop scheduling problems. In: Proceedings of the 5th international symposium on assembly and task planning, pp 146–150

  • Weissman IL, Cooper MD (1993) How the immune system develops. Scientif Am 269(3): 33–40

    Google Scholar 

  • Wiers V (1997) Human-computer interaction in production scheduling-Analysis and design of decision support systems for production scheduling tasks. Dissertation Eindhoven University of Technology, Eindhoven

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence and Bioinformatics Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Ahmad Shahrizal Muhamad & Safaai Deris

Authors
  1. Ahmad Shahrizal Muhamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Safaai Deris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Shahrizal Muhamad.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Muhamad, A.S., Deris, S. An artificial immune system for solving production scheduling problems: a review. Artif Intell Rev 39, 97–108 (2013). https://doi.org/10.1007/s10462-011-9259-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-011-9259-1

Keywords

Search

Navigation