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A genetic tool for optimal design sequencing in complex engineering systems

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Abstract

Methods in multidisciplinary design optimization rely on computer tools to manage the large amounts of information involved. One such tool is DeMAID (DEsign Manager's Aide for Intelligent Decomposition), which incorporates planning and scheduling functions to analyse the effect of the information coupling between design tasks in complex systems on the efficiency of the design process. Scheduling involves the formation of circuits of interdependent design tasks, and the minimization of feedbacks within these circuits. Recently there has been interest in the incorporation of other considerations in the sequencing of tasks within circuits. This study presents the program Gendes (GENetic DEsign Sequencer), a sequencing tool based on a genetic algorithm. The program currently has the capability to minimize feedbacks as well as crossovers (intersections in the flow of design information which obscure straightforward evaluation), and allows the potential for other considerations in the sequencing function.

This paper presents the development of this tool and the methods used. The results of computational studies to determine the most effective settings of the genetic algorithm for the task sequencing problem are presented, including population size, objective function weighting for the tradeoff between feedbacks and crossovers, mutation rate, and choice of selection operator and fitness function form. The incorporation of Gendes into the DeMAID scheduling function is explored, and the method is applied to two test systems to show its feasibility.

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References

  • Bloebaum, C.L. 1992: An intelligent decomposition approach for coupled engineering systems.Proc. 4th AIAA/USAF/NASA/OAI Symp. on Multidisciplinary Analysis and Optimization (held in Cleveland, OH)

  • Bloebaum, C.L.; Hajela, P. 1991: Heuristic decomposition for non-hierarchic systems.Proc. 32nd Structures, Structural Dynamics and Materials (SDM) Conf. (held in Baltimore, MD)

  • Bloebaum, C.L.; Hajela, P.; Sobieszczanski-Sobieski, J. 1992: Non-hierarchic system decomposition in structural optimization,Eng. Optim. 19, 171–186

    Google Scholar 

  • Borland, C.J.; Benton, J.R.; Frank, P.D.et al.??? 1994: Multidisciplinary design optimization of a commercial aircraft wing—an exploratory study.Proc. 5th AIAA/USAF/NASA/OAI Symp. on Multidisciplinary Analysis and Optimization (held in Panama City, FL)

  • Bruns, R. 1993: Direct chromosome representation and advanced genetic operators for production scheduling.Proc. 5th Int. Conf. on Genetic Algorithms (held at the University of Illinois at Urbana-Champaign)

  • DeJong, K. 1975:The analysis and behaviour of a class of genetic adaptive systems. Ph.D. Dissertation, University of Michigan

  • Goldberg, D. 1989:Genetic algorithms in search, optimization, and machine learning. New York: Addison-Wesley

    Google Scholar 

  • Hajela, P.; Bloebaum, C.L.; Sobieski, J. 1989: Application of global sensitivity equations in multidisciplinary aircraft synthesis.Proc. AIAA/AHS/ASEE Aircraft Design, Systems and Operations Conf. (held in Seattle, WA)

  • Holland, J.H. 1975:Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press

    Google Scholar 

  • Kroo, I.; Altus, S.; Braun, R.; Gage, P.; Sobieski, J. 1994: Multidisciplinary optimization methods for aircraft preliminary design.Proc. 5th AIAA/USAF/NASA/OAS Symp. on Multidisciplinary Analysis and Optimization (held in Panama City, FL)

  • Lempel, A.; Cederbaum, I. 1966: Minimum feedback arc and vertex sets of a directed graph.IEEE Transactions on Circuit Theory CT-13, 399–403

    Google Scholar 

  • Maffioli, F. 1979: The complexity of combinatorial optimization algorithms and the challenge of heuristics. In: Christofides N. et al. (eds.).Combinatorial Optimization. New York: John Wiley & Sons

    Google Scholar 

  • McCulley, C.; Bloebaum, C.L. 1994: Optimal sequencing for complex engineering systems using genetic algorithms.Proc. 5th AIAA/USAF/NASA/OAS Symp. on Multidisciplinary Analysis and Optimization (held in Panama City, FL)

  • Nemhauser, G.L.; Wolsey, L.A. 1988:Integer and combinatorial optimization. New York: John Wiley & Sons

    Google Scholar 

  • Padula, S.L.; Sobieski, J. 1987: A computer simulator for development of engineering system design methodologies.NASA TM 89109

  • Potts, C.N. 1980: An algorithm for the single machine sequencing problem with precedence constraints.Math. Prog. Study 13, 78–87

    Google Scholar 

  • Rogers, J.L. 1989: DeMAID—a design manager's aide for intelligent decomposition user's guide.NASA TM 101575

  • Rogers, J.L. 1989: A knowledge-based tool for multilevel decomposition of a complex design problem.NASA TP 2903

  • Rogers, J.L. 1989: The potential application of the blackboard model of problem solving to multidisciplinary design.NASA TM 101633

  • Rogers, J.L.; Bloebaum, C.L. 1994: Ordering design tasks based on coupling strengths.Proc. 5th AIAA/USAF/NASA/OAI Symp. on Multidisciplinary Analysis and Optimization (held in Panama City, FL)

  • Skorin-Kapov, J. 1990: Tabu search applied to the quadratic assignment problem.ORSA J. Computing 2, 33–45

    Google Scholar 

  • Sobieski, J. 1990: Sensitivity of complex, internally coupled systems.AIAA J. 28, 153–160

    Google Scholar 

  • Sobieski, J. 1993: Multidisciplinary design optimization: an emerging new engineering discipline.NASA TM 107761.

  • Sobieski, J.; James, B.B.; Riley, M.F. 1987: Structural sizing by generalized, multilevel optimization.AIAA J. 25, 139–145

    Google Scholar 

  • Starkweather, T.; McDaniel, S.; Mathias, K.; Whitley, D.; Whitley, C. 1991: A comparison of genetic sequencing operators.Proc. 4th Int. Conf. on Genetic Algorithms. Morgan Kaufmann.

  • Steward, D.V. 1981:System analysis and management. New York: Petrocelli Books

    Google Scholar 

  • Syswerda, G. 1989: Uniform crossover in genetic algorithms.Proc. 3rd Int. Conf. on Genetic Algorithms. Morgan Kaufmann.

  • Syswerda, G. 1990: Schedule optimization using genetic algorithms. In:Handbook of genetic algorithms. New York: Van Nostrand Reinhold

    Google Scholar 

  • Thompson, S.E.:A genetic algorithm program package for global design optimization. Master of Science project, State University of New York at Buffalo

  • Tolson, R.H.; Sobieski, J. 1985: Multidisciplinary analysis and synthesis: needs and opportunities.Proc. 26th AIAA/ASME/ASCE/AHS Structures, Structural Dynamics and Materials Conf. (held in Orlando, FL)

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, State University of New York at Buffalo, 14260, Buffalo, NY, USA

    C. McCulley & C. L. Bloebaum

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  1. C. McCulley
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  2. C. L. Bloebaum
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McCulley, C., Bloebaum, C.L. A genetic tool for optimal design sequencing in complex engineering systems. Structural Optimization 12, 186–201 (1996). https://doi.org/10.1007/BF01196956

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