Abstract
The selection of machining scheme for a part is an important and strategic problem. It involves multiple and conflicting objectives such as cost, time, quality, service level, resource utilization, etc. The selection is always affected by subjective factors such as the knowledge and experiences of decision maker in conventional machining. This paper proposes a method based on genetic algorithms (GA) to find out the set of Pareto-optimal solutions for multi-objective digital machining scheme selection. To deal with multi-objective and enable the engineer to make decision on different demands, an analytic hierarchy process (AHP) is implemented in the proposed procedure to determine the weight value of evaluation indexes. Three conflicting objectives: cost, quality and operation time are simultaneously optimized. An application sample is developed and its results are analyzed. The optimization results show that the hybrid algorithm is reliable and robust.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Albayrak E., Erensal Y.C. (2004) Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem. Journal of Intelligent Manufacturing 15: 491–503 doi:10.1023/B:JIMS.0000034112.00652.4c
Chan F.T.S., Chung S.H., Wadhwa S. (2005) A hybrid genetic algorithm for production and distribution. Omega 33(4): 345–355 doi:10.1016/j.omega.2004.05.004
Denkena B., Shpitalm M., Kowalski P. (2007) Knowledge management in process planning. CIRP Annals. Manufacturing Technology 56(1): 175–180
Dinabandhu B., Murthy C.A. (1996) Genetic algorithm with elitist model and its convergence. International Journal of Pattern Recognition and Artificial Intelligence 10(6): 990–995
Eiben A.E., Aarts E.H., Van H.K.M. (1991) Global convergence of genetic algorithms: An infinite Markov chain analysis. Parallel problem solving from nature. Springer-Verlag, Berlin, pp 4–12
Holland J. (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Khoshnevis B., Sormaz D.N., Park J.Y. (1999) An integrated process planning system using feature reasoning and space search-based optimization. IIE Transactions 31: 597–616
Konakk A., Coit D.W., Smith A.E. (2006) Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety 91: 992–1007 doi:10.1016/j.ress.2005.11.018
Liu Y., Wu J. (2004) Problems about the application of AHP. Journal of Wenzhou University 4: 67–72
Mahapatra G.S., Roy T.K. (2006) Fuzzy multi-objective mathematical programming on reliability optimization model. Applied Mathematics and Computation 174(1): 643–659 doi:10.1016/j.amc.2005.04.105
Malakooti B., Raman V. (2000) An interactive multi-objective artificial neural network approach for machine setup optimization. Journal of Intelligent Manufacturing 11: 41–50 doi:10.1023/A:1008999907768
Marri H.B., Gunasekaran A., Grieve R.J. (1998) Computer—aided process planning: A state of art. International Journal of Advanced Manufacturing Technology 14(4): 261–268 doi:10.1007/BF01199881
Ngai E.W.T., Chan E.W.C. (2005) Evaluation of knowledge management tools using AHP. Expert Systems with Applications 29: 889–899 doi:10.1016/j.eswa.2005.06.025
Rudolph G. (1994) Convergence properties of canonical genetic algorithms. IEEE Transactions on Neural Networks 5(1): 96–101 doi:10.1109/72.265964
Saaty T.L. (1985) Decision making for leaders to make a decision. Life Time Leaning Publications, Belmont, California
Saaty T.L. (1990) How to make a decision: The analytic hierarchy process. European Journal of Operational Research, North-Holland 48: 9–26 doi:10.1016/0377-2217(90)90057-I
Shakeri M. (2004) Implementation of an automated operation planning and optimum operation sequencing and tool selection algorithms. Computers in Industry 54: 223–236 doi:10.1016/j.compind.2003.12.002
Soremekun G., Gürdal Z., Haftka R.T., Watson L.T. (2001) Composite laminate design optimization by genetic algorithm with generalized elitist selection. Computers & Structures, 79: 131–143 doi:10.1016/S0045-7949(00)00125-5
Tesfamariam S., Sadiq R. (2006) Risk-based environmental decision-making using fuzzy analytic hierarchy process (AHP). Stochastic Environmental Research and Risk Assessment 21: 35–50 doi:10.1007/s00477-006-0042-9
Topaloglu S. (2006) A multi-objective programming model for scheduling emergency medicine residents. Computers & Industrial Engineering 51(3): 375–388 doi:10.1016/j.cie.2006.08.003
Vigdergauz S. (2002) Genetic algorithm optimization of the effective Young moduli in a perforated plate. Structural and Multidisciplinary Optimization 24: 106–117 doi:10.1007/s00158-002-0221-3
Wu X.D., Chu C.H., Wang Y.F. (2007) A genetic algorithm for cellular manufacturing design and layout. European Journal of Operational Research 181: 156–167 doi:10.1016/j.ejor.2006.05.035
Yun W.M., Xi Y.P. (1996) The analysis of global convergence and computational efficiency for genetic algorithm. Control theory an Application 13(4): 455–460
Yurdakul M. (2004) Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model. Robotics and Computer-Integrated Manufacturing 20(4): 329–340 doi:10.1016/j.rcim.2003.11.002
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Guan, X., Wang, Y. & Tao, L. Machining scheme selection of digital manufacturing based on genetic algorithm and AHP. J Intell Manuf 20, 661–669 (2009). https://doi.org/10.1007/s10845-008-0155-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-008-0155-8