nach oben

Journal of Intelligent Manufacturing

Erschienen in:

10.06.2016

Balancing stochastic U-lines using particle swarm optimization

verfasst von: Emel Kızılkaya Aydoğan, Yılmaz Delice, Uğur Özcan, Cevriye Gencer, Özkan Bali

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2019

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

U-lines are important parts of the Just-In-Time production system in order to improve productivity and quality. In real life applications of assembly lines, the tasks may have varying execution times defined as a probability distribution. In this study, a novel particle swarm optimization algorithm is proposed to solve the U-line balancing problem with stochastic task times. A computational study is conducted to compare the performance of the proposed approach to the existing methods in the literature. The results of the computational study show that the proposed approach performs quite effectively. It also yields good solutions for all test problems within a short computational time.

Vorheriger Artikel A review of diagnostic and prognostic capabilities and best practices for manufacturing

Nächster Artikel A new tool wear monitoring method based on multi-scale PCA

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

Bautista, J., Suárez, R., Mateo, M., & Companys, R. (2000). Local search heuristics for the assembly line balancing problem with incompatibilities between tasks. In Proceedings of the 2000 IEEE international conference on robotics and automation (pp. 2404–2409), San Francisco.

Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32, 909–932.CrossRef

Baykasoğlu, A., & Dereli, T. (2009). Simple and U-type assembly line balancing by using an ant colony based algorithm. Mathematical and Computational Applications, 14(1), 1–12.CrossRef

Baykasoğlu, A., & Özbakir, L. (2007). Stochastic U-line balancing using genetic algorithms. The International Journal of Advanced Manufacturing Technology, 32, 139–147.CrossRef

Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694–715.CrossRef

Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2013). Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of Intelligent Manufacturing, 24(4), 775–789.CrossRef

Bowman, E. H. (1960). Assembly line balancing by linear programming. Operations Research, 8(3), 385–389.CrossRef

Chakaravarthy, G. V., Marimuthu, S., & Sait, A. N. (2013). Performance evaluation of proposed differential evolution and particle swarm optimization algorithms for scheduling m-machine flow shops with lot streaming. Journal of Intelligent Manufacturing, 24(1), 175–191.CrossRef

Chand, S., & Zeng, T. (2001). A comparison of U-line and straight-line performances under stochastic task times. Manufacturing and Service Operations Management, 3(2), 138–150.CrossRef

Chen, K.-H., & Su, C.-T. (2010). Activity assigning of fourth party logistics by particle swarm optimization-based preemptive fuzzy integer goal programming. Expert Systems with Applications, 37(5), 3630–3637.CrossRef

Chiang, W.-C., & Urban, T. L. (2006). The stochastic U-line balancing problem: A heuristic procedure. European Journal of Operational Research, 175, 1767–1781.CrossRef

Chutima, P., & Chimklai, P. (2012). Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Computers and Industrial Engineering, 62(1), 39–55.CrossRef

Delice, Y., Aydoğan, E. K., Özcan, U., & İlkay, M. S. (2014). A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-014-0959-7.CrossRef

Erel, E., Sabuncuoglu, I., & Aksu, B. A. (2001). Balancing of U-type assembly systems using simulated annealing. International Journal of Production Research, 39(13), 3003–3015.CrossRef

Erel, E., & Sarin, S. C. (1998). A survey of the assembly line balancing procedures. Production Planning and Control, 9(5), 414–434.

Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization. NewYork: Wiley-Interscience.

Ghosh, S., & Gagnon, J. A. (1989). Comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems. International Journal of Production Research, 27(4), 637–670.CrossRef

Gökçen, H., & Ağpak, K. (2006). A goal programming approach to simple U-line balancing problem. European Journal of Operational Research, 171, 577–585.CrossRef

Gökçen, H., Ağpak, K., Gencer, C., & Kizilkaya, E. (2005). A shortest route formulation of simple U-type assembly line balancing problem. Applied Mathematical Modeling, 29, 373–380.CrossRef

Guerriero, F., & Miltenburg, J. (2003). The stochastic U-line balancing problem. Naval Research Logistics, 50(1), 31–57.CrossRef

Gutjahr, A. L., & Nemhauser, G. L. (1964). An algorithm for the line balancing problem. Management Science, 11, 308–315.CrossRef

Hamta, N., FatemiGhomi, S. M. T., Jolai, F., & Akbarpour, S. M. (2013). A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.CrossRef

HelgesonW, Birnie D. (1961). Assembly line balancing using the ranked positional weight technique. Journal of Industrial Engineering, 12, 394–398.

Hwang, R. K., Katayama, H., & Gen, M. (2008). U-shaped assembly line balancing problem with genetic algorithm. International Journal of Production Research, 46(16), 4637–4649.CrossRef

Kennedy, J., & Eberhard, R.C. (1997). A discrete binary version of the particle swarm optimization. In Proceedings of the conference on systems, man, and cybernetics SMC97 (pp. 4104–4108).

Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization. In Proceedings of the IEEE international joint conference on neural networks (pp. 1942–1948), IEEE Press.

Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco, CA: Morgan Kaufmann.

Miltenburg, J. (2001). U-shaped production lines: A review of theory and practice. International Journal of Production Economics, 70(3), 201–214.CrossRef

Miltenburg, J., & Wijngaard, J. (1994). The U-line balancing problem. Management Science, 40(10), 1378–1388.CrossRef

Mohemmed, A. W., Sahoo, N. C., & Geok, T. K. (2008). Solving shortest path problem using particle swarm optimization. Applied Soft Computing, 8(4), 1643–1653.CrossRef

Moodie, C. L., & Young, H. H. (1965). A heuristic method of assembly line balancing for assumptions of constant or variable work element times. Journal of Industrial Engineering, 16(1), 23–29.

Nakade, K., & Ohno, K. (1999). An optimal worker allocation for a U-shaped production line. International Journal of Production Economics, 60–61, 353–358.CrossRef

Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. (2011). A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing, 11, 3658–3670.CrossRef

Ohno, K., & Nakade, K. (1997). Analysis and optimization of U-shaped production line. Journal of the Operations Research Society of Japan, 40(1), 90–104.CrossRef

Ohno, K., Nakade, K., & Shantikumar, J. G. (1997). Bounds and approximations for cycle times of a U-shaped production line. Operations Research Letters, 21, 191–200.CrossRef

Özcan, U., & Toklu, B. (2009). A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems. Journal of Intelligent Manufacturing, 20, 123–136.CrossRef

Petropoulos, D. I., & Nearchou, A. C. (2011). A particle swarm optimization algorithm for balancing assembly lines. Assembly Automation, 31(2), 118–129.CrossRef

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

Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions On Evolutionary Computation, 8(3), 240–255.CrossRef

Salveson, M. E. (1955). The assembly line balancing problem. Journal of Industrial Engineering, 6(6), 18–25.

Scholl, A. (1999). Balancing and sequencing of assembly lines (2nd ed.). Heidelberg: Physica-Verlag.CrossRef

Scholl, A., & Klein, R. (1999). ULINO: optimally balancing U-shaped JIT assembly lines. International Journal of Production Research, 37(4), 721–736.CrossRef

Shi, Y., & Eberhart, R.C. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE congress on evolutionary computation USA (pp. 69–73).

Shi, Y., & Eberhart, R.C. (1999). Empirical study of particle swarm optimization. In Proceedings of the IEEE congress on evolutionary computation (pp. 1945–1950), IEEE Press.

Urban, T. L. (1998). Note: optimal balancing of U-shaped assembly lines. Management Science, 44(5), 738–741.CrossRef

Urban, T. L., & Chiang, W.-C. (2006). An optimal piecewise-linear program for the U-line balancing problem with stochastic task times. European Journal of Operational Research, 168, 771–782.CrossRef

Titel: Balancing stochastic U-lines using particle swarm optimization
verfasst von: Emel Kızılkaya Aydoğan
Yılmaz Delice
Uğur Özcan
Cevriye Gencer
Özkan Bali
Publikationsdatum: 10.06.2016
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 1/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-016-1234-x

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 1/2019

A comprehensive approach to parameters optimization of energy-aware CNC milling

Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions

ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment

Correction to: A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning

A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning

Optimal preventive maintenance policy based on reinforcement learning of a fleet of military trucks

Premium Partner

Marktübersichten