nach oben

Erschienen in:

05.01.2017

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

verfasst von: Choo Jun Tan, Siew Chin Neoh, Chee Peng Lim, Samer Hanoun, Wai Peng Wong, Chu Kong Loo, Li Zhang, Saeid Nahavandi

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.

Vorheriger Artikel Integrating part modeling and assembly modeling from the perspective of process

Nächster Artikel RETRACTED ARTICLE: Estimation of contact forces of underactuated robotic finger using soft computing methods

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abu-Lebdeh, G., & Benekohal, R. F. (1999). Convergence variability and population sizing in micro-genetic algorithms. Computer-Aided Civil and Infrastructure Engineering, 14(5), 321–334.CrossRef

Amirian, H., & Sahraeian, R. (2015). Augmented \(\epsilon \)-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect. International Journal of Production Research, 53(19), 5962–5976.CrossRef

Archimede, B., Letouzey, A., Memon, M., & Xu, J. (2014). Towards a distributed multi-agent framework for shared resources scheduling. Journal of Intelligent Manufacturing, 25(5), 1077–1087.CrossRef

Bäck, T., Hammel, U., & Schwefel, H. P. (1997). Evolutionary computation: Comments on the history and current state. IEEE Transactions on Evolutionary Computation, 1(1), 3–17.CrossRef

Bäck, T., & Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), 1–23.CrossRef

Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X. P., et al. (2012). Automated design of ligands to polypharmacological profiles. Nature, 492(7428), 215–220.CrossRef

Capón-García, E., Bojarski, A. D., Espuña, A., & Puigjaner, L. (2013). Multiobjective evolutionary optimization of batch process scheduling under environmental and economic concerns. AIChE Journal, 59(2), 429–444.CrossRef

Çaliş, B., & Bulkan, S. (2013). A research survey: Review of AI solution strategies of job shop scheduling problem. Journal of Intelligent Manufacturing, 26(5), 961–973.CrossRef

Chakaravarthy, G., Marimuthu, S., Ponnambalam, S., & Kanagaraj, G. (2014). Improved sheep flock heredity algorithm and artificial bee colony algorithm for scheduling m-machine flow shops lot streaming with equal size sub-lot problems. International Journal of Production Research, 52(5), 1509–1527.CrossRef

Chen, S. H., Chang, P. C., Cheng, T., & Zhang, Q. (2012). A self-guided genetic algorithm for permutation flowshop scheduling problems. Computers & Operations Research, 39(7), 1450–1457.CrossRef

Chen, S. H., & Chen, M. C. (2013). Addressing the advantages of using ensemble probabilistic models in estimation of distribution algorithms for scheduling problems. International Journal of Production Economics, 141(1), 24–33.CrossRef

Chen, Y. (2011). Fuzzy skyhook surface control using micro-genetic algorithm for vehicle suspension ride comfort. In M. Kppen, G. Schaefer, & A. Abraham (Eds.), Intelligent computational optimization in engineering, studies in computational intelligence (Vol. 366, pp. 357–394). Berlin/Heidelberg: Springer.CrossRef

Chiang, T. C., & Lin, H. J. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics, 141(1), 87–98.CrossRef

Chou, Y. C., Cao, H., & Cheng, H. H. (2013). A bio-inspired mobile agent-based integrated system for flexible autonomic job shop scheduling. Journal of Manufacturing Systems, 32(4), 752–763.CrossRef

Coello, C. A. C. (2006). Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28–36.CrossRef

Coello, C. A. C., Lamont, G. B., & Van Veldhuisen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems. Genetic and evolutionary computation series (2nd ed.). London: Springer.

Coello, C. A. C., & Pulido, G. (2005). Multiobjective structural optimization using a microgenetic algorithm. Structural and Multidisciplinary Optimization, 30, 388–403.CrossRef

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRef

Deng, W., Li, W., Cai, X., Bulou, A., & Wang, Q. A. (2012). Universal scaling in sports ranking. New Journal of Physics, 14(9), 093038.CrossRef

Durillo, J. J., & Nebro, A. J. (2011). jMetal: A java framework for multi-objective optimization. Advances in Engineering Software, 42(10), 760–771.CrossRef

Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Cambridge: Cambridge University Press.CrossRef

Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Cai, T. X., & Chong, C. S. (2014). Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives. Journal of Intelligent Manufacturing, 27(2), 363–374.CrossRef

Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866.CrossRef

Geyik, F., & Cedimoglu, I. (2004). The strategies and parameters of tabu search for job-shop scheduling. Journal of Intelligent Manufacturing, 15(4), 439–448.CrossRef

Gneiting, T., & Raftery, A. E. (2005). Weather forecasting with ensemble methods. Science, 310(5746), 248–249.CrossRef

Gökdağ, H., & Yıldız, A. R. (2012). Structural damage detection using modal parameters and particle swarm optimization. Materials Testing, 54(6), 416–420.CrossRef

Goldberg, D. E. (1989). Sizing populations for serial and parallel genetic algorithms. In Proceedings of the third international conference on genetic algorithms (pp. 70–79). San Francisco, CA: Morgan Kaufmann Publishers Inc.

Hanoun, S., & Nahavandi, S. (2012). A greedy heuristic and simulated annealing approach for a bicriteria flowshop scheduling problem with precedence constraints—A practical manufacturing case. The International Journal of Advanced Manufacturing Technology, 60(9–12), 1087–1098.CrossRef

Hanoun, S., Nahavandi, S., Creighton, D., & Kull, H. (2012). Solving a multiobjective job shop scheduling problem using pareto archived cuckoo search. In IEEE 17th conference on emerging technologies factory automation, 2012 (ETFA 2012) (pp. 1–8).

Hanoun, S., Nahavandi, S., & Kull, H. (2011). Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives. In The sixth international multi-conference on computing in the global information technology (ICCGI 2011) (pp. 99–104). Luxembourg

Inker, L. A., Schmid, C. H., Tighiouart, H., Eckfeldt, J. H., Feldman, H. I., Greene, T., et al. (2012). Estimating glomerular filtration rate from serum creatinine and cystatin C. New England Journal of Medicine, 367(1), 20–29.CrossRef

Karthikeyan, S., Asokan, P., & Nickolas, S. (2014). A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints. The International Journal of Advanced Manufacturing Technology, 72(9–12), 1567–1579.CrossRef

Khalili, M., & Naderi, B. (2015). A bi-objective imperialist competitive algorithm for no-wait flexible flow lines with sequence dependent setup times. The International Journal of Advanced Manufacturing Technology, 76(1–4), 461–469.CrossRef

Kiani, M., & Yıldız, A. R. (2015). A comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization. Archives of Computational Methods in Engineering. doi:10.1007/s11831-015-9155-y.

Li, D., Das, S., Pahwa, A., & Deb, K. (2013). A multi-objective evolutionary approach for generator scheduling. Expert Systems with Applications, 40(18), 7647–7655.CrossRef

Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., et al. (2012). Large-scale prediction and testing of drug activity on side-effect targets. Nature, 486(7403), 361–367.CrossRef

McWilliams, A., Tammemagi, M. C., Mayo, J. R., Roberts, H., Liu, G., Soghrati, K., et al. (2013). Probability of cancer in pulmonary nodules detected on first screening CT. The New England Journal of Medicine, 369(10), 910–919.CrossRef

Mendoza, J., Lopez, M., Coello, C. A. C., & Lopez, E. (2009). Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Generation, Transmission & Distribution, 3(9), 825–840.CrossRef

Mendoza, J., Morales, D., Lopez, R., Lopez, E., Vannier, J. C., & Coello, C. A. C. (2007). Multiobjective location of automatic voltage regulators in a radial distribution network using a micro genetic algorithm. IEEE Transactions on Power Systems, 22(1), 404–412.CrossRef

Nagar, A., Haddock, J., & Heragu, S. (1995). Multiple and bicriteria scheduling: A literature survey. European Journal of Operational Research, 81(1), 88–104.CrossRef

Nebro, A., Alba, E., & Luna, F. (2007). Multi-objective optimization using grid computing. Soft Computing, 11(6), 531–540.CrossRef

Nebro, A. J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J. J., & Beham, A. (2008). AbYSS: Adapting scatter search to multiobjective optimization. IEEE Transactions on Evolutionary Computation, 12(4), 439–457.CrossRef

Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2013). A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Transactions on Evolutionary Computation, 17(5), 621–639.CrossRef

Öztürk, N., Yıldız, A. R., Kaya, N., & Öztürk, F. (2006). Neuro-genetic design optimization framework to support the integrated robust design optimization process in CE. Concurrent Engineering, 14(1), 5–17.CrossRef

Panneerselvam, R. (2006). Simple heuristic to minimize total tardiness in a single machine scheduling problem. The International Journal of Advanced Manufacturing Technology, 30(7), 722–726.CrossRef

Pareto, V. (1971). Manual of political economy (A. S. Schwier, Trans.). New York: Augustus M. Kelley Publishers.

Prot, D., Bellenguez-Morineau, O., & Lahlou, C. (2013). New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria. European Journal of Operational Research, 231(2), 282–287.CrossRef

Qiu, X., & Lau, H. Y. (2014). An ais-based hybrid algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing, 25(3), 489–503.CrossRef

Rohaninejad, M., Kheirkhah, A., Fattahi, P., & Vahedi-Nouri, B. (2015). A hybrid multi-objective genetic algorithm based on the electre method for a capacitated flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 77(1–4), 51–66.CrossRef

Sapp, B., Weiss, D., & Taskar, B. (2011). Parsing human motion with stretchable models. In IEEE conference on computer vision and pattern recognition, 2011 (CVPR 2011) (pp. 1281–1288). Providence.

Schuetz, R., Zamboni, N., Zampieri, M., Heinemann, M., & Sauer, U. (2012). Multidimensional optimality of microbial metabolism. Science, 336(6081), 601–604.CrossRef

Sen, T., Raiszadeh, F. M. E., & Dileepan, P. (1988). A branch-and-bound approach to the bicriterion scheduling problem involving total flowtime and range of lateness. Management Science, 34(2), 254–260.CrossRef

Shahsavari-Pour, N., & Ghasemishabankareh, B. (2013). A novel hybrid meta-heuristic algorithm for solving multi objective flexible job shop scheduling. Journal of Manufacturing Systems, 32(4), 771–780.CrossRef

Shoval, O., Sheftel, H., Shinar, G., Hart, Y., Ramote, O., Mayo, A., et al. (2012). Evolutionary trade-offs, pareto optimality, and the geometry of phenotype space. Science, 336(6085), 1157–1160.CrossRef

Sridhar, J., & Rajendran, C. (1996). Scheduling in flowshop and cellular manufacturing systems with multiple objectives—A genetic algorithmic approach. Production Planning & Control, 7(4), 374–382.CrossRef

Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2, 221–248.CrossRef

Tan, C. J., Lim, C. P., & Cheah, Y. N. (2013). A modified micro genetic algorithm for undertaking multi-objective optimization problems. Journal of Intelligent and Fuzzy Systems, 24(3), 483–495.CrossRef

Tan, C. J., Lim, C. P., & Cheah, Y. N. (2014). A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models. Neurocomputing, 125, 217–228.CrossRef

Tan, C. J., Lim, C. P., Cheah, Y. N., & Tan, S. C. (2013). Classification and optimization of product review information using soft computing models. In International symposium on affective engineering, 2013 (ISAE 2013) (pp. 115–120). Kitakyushu: Japan Society of Kansei Engineering.

Tan, C. J., Samer, H., Lim, C. P., Creighton, D., & Nahavandi, S. (2015). A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing. In: 2015 9th Annual IEEE international systems conference (SysCon) (pp. 170–174).

Tian, J., Li, M., & Chen, F. (2010). Dual-population based coevolutionary algorithm for designing RBFNN with feature selection. Expert Systems with Applications, 37(10), 6904–6918.CrossRef

Tiwari, A., Chang, P. C., Tiwari, M., & Kollanoor, N. J. (2015). A pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem. International Journal of Production Research, 53(3), 793–834.CrossRef

Tsai, M. Y. (2015). Variable selection in bayesian generalized linear-mixed models: An illustration using candidate gene case-control association studies. Biometrical Journal, 57(2), 234–253.CrossRef

Wassenhove, L. N. V., & Gelders, L. F. (1980). Solving a bicriterion scheduling problem. European Journal of Operational Research, 4(1), 42–48.CrossRef

Wassenhove, L. V., & Gelders, L. (1978). Four solution techniques for a general one machine scheduling problem: A comparative study. European Journal of Operational Research, 2(4), 281–290.CrossRef

Wilson, D. T., Hawe, G. I., Coates, G., & Crouch, R. S. (2013). A multi-objective combinatorial model of casualty processing in major incident response. European Journal of Operational Research, 230(3), 643–655.CrossRef

Wu, W. H., Wu, W. H., Chen, J. C., Lin, W. C., Wu, J., & Wu, C. C. (2015). A heuristic-based genetic algorithm for the two-machine flowshop scheduling with learning consideration. Journal of Manufacturing Systems, 35, 223–233.CrossRef

Xiong, J., Xing, L., & Chen, Y. (2013). Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns. International Journal of Production Economics, 141(1), 112–126.CrossRef

Yıldız, A. R. (2008). Optimal structural design of vehicle components using topology design and optimization. Materials Testing, 50(4), 224–228.CrossRef

Yıldız, A. R. (2013). Comparison of evolutionary-based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333. doi:10.1016/j.engappai.2012.05.014.

Yıldız, A. R., Kaya, N., Öztürk, F., & Alankus, O. (2004). Optimal design of vehicle components using topology design and optimisation. International Journal of Vehicle Design, 34(4), 387–398.CrossRef

Yıldız, A. R., Kurtuluş, E., Demirci, E., Yıldız, B. S., & Karagöz, S. (2016). Optimization of thin-wall structures using hybrid gravitational search and Nelder–Mead algorithm. Materials Testing, 58(1), 75–78.CrossRef

Yıldız, A. R., Öztürk, N., Kaya, N., & Öztürk, F. (2003). Integrated optimal topology design and shape optimization using neural networks. Structural and Multidisciplinary Optimization, 25(4), 251–260. doi:10.1007/s00158-003-0300-0.CrossRef

Yıldız, B. S., Lekesiz, H., & Yıldız, A. R. (2016). Structural design of vehicle components using gravitational search and charged system search algorithms. Materials Testing, 58(1), 79–81.CrossRef

Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers and Industrial Engineering, 56(4), 1309–1318.CrossRef

Zhang, J., Yang, J., & Zhou, Y. (2016). Robust scheduling for multi-objective flexible job-shop problems with flexible workdays. Engineering Optimization. doi:10.1080/0305215X.2016.1145216.

Zhang, Q., Liu, W., Tsang, E., & Virginas, B. (2010). Expensive multiobjective optimization by MOEA/D with gaussian process model. IEEE Transactions on Evolutionary Computation, 14(3), 456–474.CrossRef

Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., & da Fonseca, V. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation, 7(2), 117–132.CrossRef

Zoubir, A., & Boashash, B. (1998). The bootstrap and its application in signal processing. IEEE Signal Processing Magazine, 15(1), 56–76.CrossRef

Titel: Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
verfasst von: Choo Jun Tan
Siew Chin Neoh
Chee Peng Lim
Samer Hanoun
Wai Peng Wong
Chu Kong Loo
Li Zhang
Saeid Nahavandi
Publikationsdatum: 05.01.2017
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 2/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-016-1291-1

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2019

A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem

Assessing the cost structure of component reuse in a product family for remanufacturing

Defect identification in friction stir welding using continuous wavelet transform

Damage forecasting based on multi-factor fuzzy time series and cloud model

A geometric error budget method to improve machining accuracy reliability of multi-axis machine tools

An adaptive approach for determining batch sizes using the hidden Markov model

Premium Partner

Marktübersichten