Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction Kapitel lesen Erstes Kapitel lesen

2011 | OriginalPaper | Buchkapitel

9. Preference Vector Ant Colony System for Minimising Make-span and Energy Consumption in a Hybrid Flow Shop

verfasst von : Bing Du, Huaping Chen, George Q. Huang, H. D. Yang

Erschienen in: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Verlag: Springer London

Einloggen

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

search-config
loading …

Abstract

Traditionally, scheduling problems usually deal with the objectives related to production efficiency (e.g., the make-span, the total completion time, the maximum lateness and the number of tardy jobs). However, sustainable manufacturing should minimise the energy consumption during production process. Energy consumption not only constitutes a major portion of total production cost but also results in significant environmental effects. In this chapter, we discuss a multi-objective scheduling problem in a hybrid flow shop. Two objectives considered in the proposed model are to minimise make-span and energy consumption. These two objectives are often in conflict with each other. A Preference Vector Ant Colony System (PVACS) is developed to search for a set of Pareto-optimal solutions using meta-heuristics for multi-objective optimisation. PVACS allows the search in the solution space to focus on the specific areas which are of particular interest to decision-makers, instead of searching for the entire Pareto frontier. This is achieved by maintaining a separate pheromone matrix for each objective, respectively and assigning each ant a preference vector that represents the preference between the two objectives of the decision-makers. The performance of PVACS was compared to two well-known multi-objective genetic algorithms: SPEA2 and NSGA-II. The experimental results show that PVACS outperforms the other two algorithms.

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

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Setup Planning Approach Considering Tolerance Cost Factors
Nächstes Kapitel Intelligent Optimisation for Integrated Process Planning and Scheduling
Literatur
1.
U.N. Environment Programme. (2010). Green economy report: a preview.
2.
Institute, T. M. (2009). The facts about modern manufacturing (8th ed.).
3.
US Department of Commerce, E.a.S.A. (2010). Measuring the green economy.
4.
5.
Tacconi, L. (2000). Biodiversity and ecological economics: Participation, values, and resource management. London: Earthscan/James & James.
6.
US Energy Information Administration. (2009). International Energy Outlook 2009
7.
Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45, 4247–4271.MATHCrossRef
8.
Lei, D. M. (2009). Multi-objective production scheduling: a survey. International Journal of Advanced Manufacturing Technology, 43(9–10), 926–938.CrossRef
9.
Ross, M. (1992). Efficient energy use in manufacturing. Proceedings of the National Academy of Sciences of the United States of America, 89(3), 827–831.CrossRef
10.
Park, S. H., Dissmann, B., & Nam, K. Y. (1993). A cross-country decomposition analysis of manufacturing energy-consumption. Energy, 18(8), 843–858.CrossRef
11.
Fromme, J. W. (1996). Energy conservation in the Russian manufacturing industry—Potentials and obstacles. Energy Policy, 24(3), 245–252.CrossRef
12.
Golove, W. H., & Schipper, L. J. (1996). Long-term trends in US manufacturing energy consumption and carbon dioxide emissions. Energy, 21(7–8), 683–692.CrossRef
13.
Adenikinju, A. F. (1998). Productivity growth and energy consumption in the Nigerian manufacturing sector: A panel data analysis. Energy Policy, 26(3), 199–205.CrossRef
14.
Bentzen, J. (2004). Estimating the rebound effect in US manufacturing energy consumption. Energy Economics, 26(1), 123–134.CrossRef
15.
Draganescu, F., Gheorghe, M., & Doicin, C. V. (2003). Models of machine tool efficiency and specific consumed energy. Journal of Materials Processing Technology, 141(1), 9–15.CrossRef
16.
Dietmair, A., & Verl, A. (2009). Energy consumption forecasting and optimization for tool machines. Modern Machinery Science Journal, 62–67.
17.
Herrmann, C., & Thiede, S. (2009). Process chain simulation to foster energy efficiency in manufacturing. CIRP Journal of Manufacturing Science and Technology, 1(4), 221–229.CrossRef
18.
Wolters, W. T. M., Lambert, A. J. D., & Claus, J. (1995). Sequencing problems in designing energy efficient production systems. International Journal of Production Economics, 41(1–3), 405–410.CrossRef
19.
Park, C. W., et al. (2009). Energy consumption reduction technology in manufacturing—A selective review of policies, standards, and research. International Journal of Precision Engineering and Manufacturing, 10(5), 151–173.CrossRef
20.
Fonseca, C., & Fleming, P. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1), 1–16.CrossRef
21.
Coello, C. A. C. (2006). Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28–36.CrossRef
22.
Schaffer, J. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100
23.
Goldberg, D. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-wesley.MATH
24.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.CrossRef
25.
Horn, J., Nafpliotis, N., & Goldberg, D. (1994). A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence (pp. 418)
26.
Fonseca, C., Fleming, P. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423
27.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.CrossRef
28.
Rudolph, G., & Agapie, A. (2000). Convergence properties of some multi-objective evolutionary algorithms. In Proceedings of the 2000 Congress on Evolutionary Computation (Vols 1 and 2, pp. 1010–1016)
29.
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. In Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100
30.
Knowles, J., & Corne, D. (2000). Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2), 149–172.CrossRef
31.
Deb, K., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.MathSciNetCrossRef
32.
Chaudhuri, S., & Deb, K. (2010). An interactive evolutionary multi-objective optimization and decision making procedure. Applied Soft Computing, 10(2), 496–511.CrossRef
33.
Doerner, K., et al. (2004). Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection. Annals of Operations Research, 131(1–4), 79–99.MathSciNetMATHCrossRef
34.
Doerner, K. F., et al. (2006). Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection. European Journal of Operational Research, 171(3), 830–841.MathSciNetMATHCrossRef
35.
Xia, W. J., & Wu, Z. M. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48(2), 409–425.MathSciNetCrossRef
36.
Sedenka, V., & Raida, Z. (2010). Critical comparison of multi-objective optimization methods: Genetic algorithms versus swarm intelligence. Radioengineering, 19(3), 369–377.
37.
Berrichi, A., et al. (2010). Bi-objective ant colony optimization approach to optimize production and maintenance scheduling. Computers & Operations Research, 37(9), 1584–1596.MathSciNetMATHCrossRef
38.
Jones, D. F., Mirrazavi, S. K., & Tamiz, M. (2002). Multi-objective meta-heuristics: An overview of the current state-of-the-art. European Journal of Operational Research, 137(1), 1–9.MATHCrossRef
39.
Tan, K. C., Lee, T. H., & Khor, E. F. (2002). Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artificial Intelligence Review, 17(4), 253–290.CrossRef
40.
Zitzler, E., et al. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation, 7(2), 117–132.CrossRef
41.
Pinedo, M. (2002). Scheduling: theory, algorithms and systems. Upper Saddle River, NJ: Prentice-Hall.MATH
42.
Gupta, J. (1988). Two-stage, hybrid flowshop scheduling problem. The Journal of the Operational Research Society, 39(4), 359–364.MATH
43.
Linn, R., & Zhang, W. (1999). Hybrid flow shop scheduling: A survey. Computers & Industrial Engineering, 37(1–2), 57–61.CrossRef
44.
Ribas, I., Leisten, R., & Framinan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers & Operations Research, 37(8), 1439–1454.MathSciNetMATHCrossRef
45.
Ruiz, R., & Vazquez-Rodriguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18.MathSciNetMATHCrossRef
46.
Huang, W., & Li, S. (1998). A two-stage hybrid flowshop with uniform machines and setup times. Mathematical and Computer Modelling, 27(2), 27–45.MathSciNetMATHCrossRef
47.
Dessouky, M. M., Dessouky, M. I., & Verma, S. K. (1998). Flowshop scheduling with identical jobs and uniform parallel machines. European Journal of Operational Research, 109(3), 620–631.MATHCrossRef
48.
Soewandi, H., & Elmaghraby, S. E. (2003). Sequencing on two-stage hybrid flowshops with uniform machines to minimize makespan. IIE Transactions, 35(5), 467–477.CrossRef
49.
Kyparisis, G. J., & Koulamas, C. (2006). A note on makespan minimization in two-stage flexible flow shops with uniform machines. European Journal of Operational Research, 175(2), 1321–1327.MATHCrossRef
50.
Bertel, S., & Billaut, J. C. (2004). A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation. European Journal of Operational Research, 159(3), 651–662.MathSciNetMATHCrossRef
51.
Sevastianov, S. V. (2002). Geometrical heuristics for multiprocessor flowshop scheduling with uniform machines at each stage. Journal of Scheduling, 5(3), 205–225.MathSciNetMATHCrossRef
52.
Kyparisis, G. J., & Koulamas, C. (2001). A note on weighted completion time minimization in a flexible flow shop. Operations Research Letters, 29(1), 5–11.MathSciNetMATHCrossRef
53.
Kyparisis, G. J., & Koulamas, C. (2006). Flexible flow shop scheduling with uniform parallel machines. European Journal of Operational Research, 168(3), 985–997.MathSciNetMATHCrossRef
54.
Verma, S., & Dessouky, M. (1999). Multistage hybrid flowshop scheduling with identical jobs and uniform parallel machines. Journal of Scheduling, 2(3), 135–150.MathSciNetMATHCrossRef
55.
Voss, S., & Witt, A. (2007). Hybrid flow shop scheduling as a multi-mode multi-project scheduling problem with batching requirements: A real-world application. International Journal of Production Economics, 105(2), 445–458.CrossRef
56.
Graham, R., et al. (1979). Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics, 5(2), 287–326.MathSciNetMATHCrossRef
57.
Colorni, A., Dorigo, M., Maniezzo, V. (1991). Distributed optimization by ant colonies. In Proceedings of the First European Conference on Artificial Life (pp. 134–142)
58.
Colorni, A., et al. (1994). Ant system for job-shop scheduling. Journal of Operations Research and Statistic Computing Science, 34(1), 39–53.MATH
59.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 26(1), 29–41.CrossRef
60.
Rajendran, C., & Ziegler, H. (2004). Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs. European Journal of Operational Research, 155(2), 426–438.MathSciNetMATHCrossRef
61.
Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6(4), 333–346.CrossRef
62.
Blum, C. (2005). Beam-ACO—hybridizing ant colony optimization with beam search: an application to open shop scheduling. Computers & Operations Research, 32(6), 1565–1591.CrossRef
63.
T’Kindt, V., et al. (2002). An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. European Journal of Operational Research, 142(2), 250–257.MathSciNetMATHCrossRef
64.
Jayaraman, V. K., et al. (2000). Ant colony framework for optimal design and scheduling of batch plants. Computers & Chemical Engineering, 24(8), 1901–1912.MathSciNetCrossRef
65.
Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2–3), 243–278.MathSciNetMATHCrossRef
66.
Mariano, C. E., Morales, E. (1999). MOAQ an Ant-Q algorithm for multiple objective optimization problems. In Gecco-99: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 894–901.
67.
Yagmahan, B., & Yenisey, M. M. (2008). Ant colony optimization for multi-objective flow shop scheduling problem. Computers & Industrial Engineering, 54(3), 411–420.CrossRef
68.
Palmer, D. (1965). Sequencing jobs through a multi-stage process in the minimum total time—a quick method of obtaining a near optimum. Operations Research Quarterly, 16(1), 101–107.MathSciNetCrossRef
69.
Damodaran, P., Manjeshwar, P. K., & Srihari, K. (2006). Minimizing makespan on a batch-processing machine with non-identical job sizes using genetic algorithms. International Journal of Production Economics, 103(2), 882–891.CrossRef
70.
Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 8(2), 173–195.CrossRef
Metadaten
Titel
Preference Vector Ant Colony System for Minimising Make-span and Energy Consumption in a Hybrid Flow Shop
verfasst von
Bing Du
Huaping Chen
George Q. Huang
H. D. Yang
Copyright-Jahr
2011
Verlag
Springer London
Buch
Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Print ISBN: 978-0-85729-617-7

Electronic ISBN: 978-0-85729-652-8

Copyright-Jahr: 2011

DOI
https://doi.org/10.1007/978-0-85729-652-8_9

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
    Bildnachweise