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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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
J. Bongard, H. Lipson: Integrated Design, Deployment and Inference for Robot Ecologies, Proc. Robosphere 2004 (NASA Ames Research Center 2004)
I. Rechenberg: Evolution Strategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution (Frommann-Holzboog, Stuttgart 1973)
L.A. Fogel, M. Walsh: Artificial Intelligence Through Simulated Evolution (Wiley, New York 1966)
J. Holland: Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor 1975), (MIT Press, Cambridge 1992)
D. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading 1989)
J.R. Koza: Genetic Programming (MIT Press, Cambridge 1992)
H. Schwefel: Evolution and Optimum Seeking, 2nd edn. (Wiley, New York 1995)
Z. Michalewicz: Genetic Algorithm + Data Structures = Evolution Programs, 3rd edn. (Springer, New York 1996)
M. Gen, R. Cheng: Genetic Algorithms and Engineering Design (Wiley, New York 1997)
K. Deb: Multi-Objective Optimization Using Evolutionary Algorithms (Wiley, New York 2001)
M. Gen, R. Cheng, L. Lin: Network Models and Optimization: Multiobjective Genetic Algorithm Approach (Springer, London 2008)
Wikipedia: Evolutionary Computation, http://en.wikipedia.org/wiki/Evolutionary_computation
J.D. Schaffer: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, Proc. 1st Int. Conf. on Genet. Algorithms (1985) pp. 93–100
C. Fonseca, P. Fleming: An overview of evolutionary algorithms in multiobjective optimization, Evolut. Comput. 3(1), 1–16 (1995)
N. Srinivas, K. Deb: Multiobjective function optimization using nondominated sorting genetic algorithms, Evolut. Comput. 3, 221–248 (1995)
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)
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)
E. Zitzler, L. Thiele: SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Technical Report 103, (Computer Engineering and Communication Networks Lab, Zurich 2001)
D. Tate, A. Smith: Unequal-area facility layout by genetic search, IIE Trans. 27, 465–472 (1995)
J. Cohoon, S. Hegde, N. Martin: Distributed genetic algorithms for the floor-plan design problem, IEEE Trans. Comput.-Aided Des. 10, 483–491 (1991)
K. Tam: Genetic algorithms, function optimization, facility layout design, Eur. J. Oper. Res. 63, 322–346 (1992)
A. Kusiak, S. Heragu: The facility layout problem, Eur. J. Oper. Res. 29, 229–251 (1987)
M. Pinedo: Scheduling Theory, Algorithms and Systems (Prentice-Hall, Upper Saddle River 2002)
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)
H. Zhang, M. Gen: Multistage-based genetic algorithm for flexible job-shop scheduling problem, J. Complex. Int. 11, 223–232 (2005)
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)
D. Turbide: Advanced planning and scheduling (APS) systems, Midrange ERP Mag. (1998)
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)
C. Moon, Y. Seo: Evolutionary algorithm for advanced process planning and scheduling in a multi-plant, Comput. Ind. Eng. 48(2), 311–325 (2005)
Y. Tsujimura, M. Gen, E. Kubota: Solving fuzzy assembly-line balancing problem with genetic algorithms, Comput. Ind. Eng. 29(1/4), 543–547 (1995)
M. Gen, Y. Tsujimura, Y. Li: Fuzzy assembly line balancing using genetic algorithms, Comput. Ind. Eng. 31(3/4), 631–634 (1996)
J. Rubinovitz, G. Levitin: Genetic algorithm for line balancing, Int. J. Prod. Econ. 41, 343–354 (1995)
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)
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)
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)
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)
J.K. Lin: Study on guide path design and path planning in automated guided vehicle system. Ph.D. Thesis (Waseda University, Japan 2004)
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)
Y.K. Lau, Y. Zhao: Integrated scheduling of handling equipment at automated container terminals, Ann. Operat. Res. 159(1), 373–394 (2008)
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)
P. Preston, E. Kozan: An approach to determine storage locations of containers at seaport terminals, Comput. Oper. Res. 28(10), 983–995 (2001)
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)
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)
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)
A. Scholl, N. Boysen, M. Fliedner, R. Klein: Homepage for assembly line optimization research, http://www.assembly-line-balancing.de/
A. Scholl: Data of Assembly Line Balancing Problems. Schriften zur Quantitativen Betriebswirtschaftslehre 16/93, (TH Darmstadt, Darmstadt 1993)
G. Levitin, J. Rubinovitz, B. Shnits: A genetic algorithm for robotic assembly balancing, Eur. J. Oper. Res. 168, 811–825 (2006)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-78831-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78830-0
Online ISBN: 978-3-540-78831-7
eBook Packages: EngineeringEngineering (R0)