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Evolutionary Techniques for Automation

  • Chapter
Springer Handbook of Automation

Part of the book series: Springer Handbooks ((SHB))

Abstract

In this chapter, evolutionary techniques (ETs) will be introduced for treating automation problems in factory, manufacturing, planning and scheduling, and logistics and transportation systems. ET is the most popular metaheuristic method for solving NP-hard optimization problems. In the past few years, ETs have been exploited to solve design automation problems. Concurrently, the field of ET reveals a significant interest in evolvable hardware and problems such as routing, placement or test pattern generation.

The rest of this chapter is organized as follows. First the background developments of evolutionary techniques are described. Then basic schemes and working mechanism of genetic algorithms (GAs) will be given, and multiobjective evolutionary algorithms for treating optimization problems with multiple and conflicting objectives are presented. Lastly, automation and the challenges for applying evolutionary techniques are specified.

Next, the various applications based on ETs for solving factory automation (FA) problems will be surveyed, covering planning and scheduling problems, nonlinear optimization problems in manufacturing systems, and optimal design problems in logistics and transportation systems.

Finally, among those applications based on ETs, detailed case studies will be introduced. The first case study covers dispatching of automated guided vehicles (AGV) and machine scheduling in a flexible manufacturing system (FMS). The second ET case study for treating automation problems is the robot-based assembly line balancing (ALB) problem. Numerical experiments for various scales of AGV dispatching problems and robot-based ALB problems will be described to show the effectiveness of the proposed approaches with greater search capability that improves the quality of solutions and enhances the rate of convergence over existing approaches.

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Abbreviations

AGV:

autonomous guided vehicle

AI:

artificial intelligence

ALB:

assembly line balancing

ANTS:

Workshop on Ant Colony optimization and Swarm Intelligence

APS:

advanced planning and scheduling

AS:

ancillary service

BAP:

Berth allocation planning

CEC:

Congress on Evolutionary Computation

EA:

evolutionary algorithm

EMO:

evolutionary multiobjective optimization

EP:

evolutionary programming

ES:

enterprise system

ES:

evolution strategy

ET:

evolutionary technique

FA:

factory automation

FA:

false alarm

FL:

fuzzy-logic

FMS:

field message specification

FMS:

flexible manufacturing system

FMS:

flight management system

FOGA:

Foundations of Genetic Algorithms

GA:

genetic algorithms

GECCO:

Genetic and Evolutionary Computation Conference

GP:

genetic programming

HES:

handling equipment scheduling

ID:

identification

ID:

instructional design

IV:

intravenous

MIT:

Massachusetts Institute of Technology

MIT:

miles in-trail

NP:

nominal performance

NP:

nondeterministic polynomial-time

P/D:

pickup/delivery

RS:

robust stability

SLP:

storage locations planning

WMX:

weight mapping crossover

awGA:

adaptive-weight genetic algorithm

fJSP:

flexible jobshop problem

moGA:

multiobjective genetic algorithm

nsGA:

nondominated sorting genetic algorithm

rALB:

robot-based assembly line balancing

rcPSP:

resource-constrained project scheduling problem

rwGA:

random-weight genetic algorithm

sALB:

simple assembly line balancing

spEA:

strength Pareto evolutionary algorithm

veGA:

vector evaluated genetic algorithm

References

  1. J. Bongard, H. Lipson: Integrated Design, Deployment and Inference for Robot Ecologies, Proc. Robosphere 2004 (NASA Ames Research Center 2004)

    Google Scholar 

  2. I. Rechenberg: Evolution Strategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution (Frommann-Holzboog, Stuttgart 1973)

    Google Scholar 

  3. L.A. Fogel, M. Walsh: Artificial Intelligence Through Simulated Evolution (Wiley, New York 1966)

    MATH  Google Scholar 

  4. J. Holland: Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor 1975), (MIT Press, Cambridge 1992)

    Google Scholar 

  5. D. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading 1989)

    MATH  Google Scholar 

  6. J.R. Koza: Genetic Programming (MIT Press, Cambridge 1992)

    MATH  Google Scholar 

  7. H. Schwefel: Evolution and Optimum Seeking, 2nd edn. (Wiley, New York 1995)

    Google Scholar 

  8. Z. Michalewicz: Genetic Algorithm + Data Structures = Evolution Programs, 3rd edn. (Springer, New York 1996)

    Google Scholar 

  9. M. Gen, R. Cheng: Genetic Algorithms and Engineering Design (Wiley, New York 1997)

    Google Scholar 

  10. K. Deb: Multi-Objective Optimization Using Evolutionary Algorithms (Wiley, New York 2001)

    MATH  Google Scholar 

  11. M. Gen, R. Cheng, L. Lin: Network Models and Optimization: Multiobjective Genetic Algorithm Approach (Springer, London 2008)

    MATH  Google Scholar 

  12. Wikipedia: Evolutionary Computation, http://en.wikipedia.org/wiki/Evolutionary_computation

  13. J.D. Schaffer: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Proc. 1st Int. Conf. on Genet. Algorithms (1985) pp. 93–100

    Google Scholar 

  14. C. Fonseca, P. Fleming: An overview of evolutionary algorithms in multiobjective optimization, Evolut. Comput. 3(1), 1–16 (1995)

    Article  Google Scholar 

  15. N. Srinivas, K. Deb: Multiobjective function optimization using nondominated sorting genetic algorithms, Evolut. Comput. 3, 221–248 (1995)

    Google Scholar 

  16. H. Ishibuchi, T. Murata: A multiobjective genetic local search algorithm and its application to flowshop scheduling, IEEE Trans. Syst. Man Cybern. 28(3), 392–403 (1998)

    Article  Google Scholar 

  17. E. Zitzler, L. Thiele: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Trans. Evolut. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  18. E. Zitzler, L. Thiele: SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report 103, (Computer Engineering and Communication Networks Lab, Zurich 2001)

    Google Scholar 

  19. D. Tate, A. Smith: Unequal-area facility layout by genetic search, IIE Trans. 27, 465–472 (1995)

    Article  Google Scholar 

  20. J. Cohoon, S. Hegde, N. Martin: Distributed genetic algorithms for the floor-plan design problem, IEEE Trans. Comput.-Aided Des. 10, 483–491 (1991)

    Article  Google Scholar 

  21. K. Tam: Genetic algorithms, function optimization, facility layout design, Eur. J. Oper. Res. 63, 322–346 (1992)

    Article  MATH  Google Scholar 

  22. A. Kusiak, S. Heragu: The facility layout problem, Eur. J. Oper. Res. 29, 229–251 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  23. M. Pinedo: Scheduling Theory, Algorithms and Systems (Prentice-Hall, Upper Saddle River 2002)

    MATH  Google Scholar 

  24. I. Kacem, S. Hammadi, P. Borne: Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems, IEEE Trans. Syst. Man Cybern. Part C 32(1), 408–419 (2002)

    Article  Google Scholar 

  25. H. Zhang, M. Gen: Multistage-based genetic algorithm for flexible job-shop scheduling problem, J. Complex. Int. 11, 223–232 (2005)

    Google Scholar 

  26. K.W. Kim, Y.S. Yun, J.M. Yoon, M. Gen, G. Yamazaki: Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling, Comput. Ind. 56(2), 143–160 (2005)

    Article  Google Scholar 

  27. D. Turbide: Advanced planning and scheduling (APS) systems, Midrange ERP Mag. (1998)

    Google Scholar 

  28. C. Moon, J.S. Kim, M. Gen: Advanced planning and scheduling based on precedence and resource constraints for e-Plant chains, Int. J. Prod. Res. 42(15), 2941–2955 (2004)

    Article  MATH  Google Scholar 

  29. C. Moon, Y. Seo: Evolutionary algorithm for advanced process planning and scheduling in a multi-plant, Comput. Ind. Eng. 48(2), 311–325 (2005)

    Article  Google Scholar 

  30. Y. Tsujimura, M. Gen, E. Kubota: Solving fuzzy assembly-line balancing problem with genetic algorithms, Comput. Ind. Eng. 29(1/4), 543–547 (1995)

    Article  Google Scholar 

  31. M. Gen, Y. Tsujimura, Y. Li: Fuzzy assembly line balancing using genetic algorithms, Comput. Ind. Eng. 31(3/4), 631–634 (1996)

    Article  Google Scholar 

  32. J. Rubinovitz, G. Levitin: Genetic algorithm for line balancing, Int. J. Prod. Econ. 41, 343–354 (1995)

    Article  Google Scholar 

  33. J. Gao, G. Chen, L. Sun, M. Gen, An efficient approach for type II robotic assembly line balancing problems, Comput. Ind. Eng., in press (2007)

    Google Scholar 

  34. L. Qiu, W. Hsu, S. Huang, H. Wang: Scheduling and routing algorithms for AGVs: a survey, Int. J. Prod. Res. 40(3), 745–760 (2002)

    Article  MATH  Google Scholar 

  35. I.F.A. Vis: Survey of research in the design and control of automated guided vehicle systems, Eur. J. Oper. Res. 170(3), 677–709 (2006)

    Article  Google Scholar 

  36. T. Le-Anh, D. Koster: A review of design and control of automated guided vehicle systems, Eur. J. Oper. Res. 171(1), 1–23 (2006)

    Article  MATH  Google Scholar 

  37. J.K. Lin: Study on guide path design and path planning in automated guided vehicle system. Ph.D. Thesis (Waseda University, Japan 2004)

    Google Scholar 

  38. L. Lin, S.W. Shinn, M. Gen, H. Hwang: Network model and effective evolutionary approach for AGV dispatching in manufacturing system, J. Intell. Manuf. 17(4), 465–477 (2006)

    Article  Google Scholar 

  39. Y.K. Lau, Y. Zhao: Integrated scheduling of handling equipment at automated container terminals, Ann. Operat. Res. 159(1), 373–394 (2008)

    Google Scholar 

  40. A. Imai, H.C. Chen, E. Nishimura, S. Papadimitriou: The simultaneous berth and quay crane allocation problem, Transp. Res. Part E: Logist. Transp. Rev. 44(5), 900–920 (2008)

    Google Scholar 

  41. P. Preston, E. Kozan: An approach to determine storage locations of containers at seaport terminals, Comput. Oper. Res. 28(10), 983–995 (2001)

    Article  MATH  Google Scholar 

  42. J.B. Yang: GA-based discrete dynamic programming approach for scheduling in FMS environment, IEEE Trans. Syst. Man Cybern. B 31(5), 824–835 (2001)

    Article  Google Scholar 

  43. K. Kim, G. Yamazaki, L. Lin, M. Gen: Network-based hybrid genetic algorithm to the scheduling in FMS environments, J. Artif. Life Robot. 8(1), 67–76 (2004)

    Article  Google Scholar 

  44. S.H. Kim, H. Hwang: An adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process, Int. J. Prod. Econ. 60/61, 465–472 (1999)

    Article  Google Scholar 

  45. A. Scholl, N. Boysen, M. Fliedner, R. Klein: Homepage for assembly line optimization research, http://www.assembly-line-balancing.de/

  46. A. Scholl: Data of Assembly Line Balancing Problems. Schriften zur Quantitativen Betriebswirtschaftslehre 16/93, (TH Darmstadt, Darmstadt 1993)

    Google Scholar 

  47. G. Levitin, J. Rubinovitz, B. Shnits: A genetic algorithm for robotic assembly balancing, Eur. J. Oper. Res. 168, 811–825 (2006)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, 808-0135, Kitakyushu, Japan

    Mitsuo Gen PhD

  2. Information, Production & Systems Research Center, Waseda University, 2-7 Hibikino, Wakamatsu-ku, 808-0135, Kitakyushu, Japan

    Lin Lin PhD

Authors
  1. Mitsuo Gen PhD
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Correspondence to Mitsuo Gen PhD or Lin Lin PhD .

Editor information

Editors and Affiliations

  1. PRISM Center, and School of Industrial Engineering, Purdue University, 315 N. Grant Street, 47907, West Lafayette, IN, USA

    Shimon Y. Nof

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© 2009 Springer-Verlag Berlin Heidelberg

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Gen, M., Lin, L. (2009). Evolutionary Techniques for Automation. In: Nof, S. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78831-7_29

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  • DOI: https://doi.org/10.1007/978-3-540-78831-7_29

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