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:
Buchtitelbild

2019 | OriginalPaper | Buchkapitel

1. Possibilistic Framework for Multi-objective Optimization Under Uncertainty

verfasst von : Oumayma Bahri, Nahla Benamor, El-Ghazali Talbi

Erschienen in: Bioinspired Heuristics for Optimization

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.

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
Nächstes Kapitel Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU
Literatur
1.
Babbar, M., Lakshmikantha, A., & Goldberg, D. E. (2003). A modified NSGA-II to solve noisy multiobjective problems. Genetic and Evolutionary Computation, 2723, 2127.
2.
Basseur, M., Liefooghe, A. L. J., & El-Ghazali, T. (2007). ParadisEO-MOEO: A framework for evolutionary multi-objective optimization. In Evolutionary multi-criterion optimization (pp. 386–400).
3.
Brito, J., Morino, J. A., & Verdegay, J. L. (2009). Fuzzy optimization in vehicle routing problems. In IFSA-EUSFLAT.
4.
Coello, C. A. C., & Lamont, G. B. (2004). Applications of multi-objective evolutionary algorithms. World Scientific.
5.
Coello, C. A. C., Lamont, G. B., & Van Veldhuisen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems. Springer, 5, 79–104.
6.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Wiley.
7.
Deb, K., Agrawal, S., et al. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Evolutionary Computation, 6.
8.
Deb, K., & Srinivas, N. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 3, 221248.
9.
Dubois, D., & Prade, H. (1998). An introductory survey of possibility theory and its recent developments. Fuzzy Theory and Systems, 10, 21–42.CrossRef
10.
El-Ghazali, T. (2009). Metaheuristics: From design to implementation. Wiley.
11.
Fonseca, C. M., Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms (pp. 416–423).
12.
Goh, C. K., & Tan, K. C. (2009). Evolutionary multi-objective optimization in uncertain environments: Issues and algorithms. Springer.
13.
Goncalves, G., Hsu, T., & Xu, J. (2009). Vehicle routing problem with time windows and fuzzy demands: An approach based on the possibility theory. Inderscience, 4, 312–330.
14.
Horn, J., Nafpliotis, N., & Goldberg, D. (1994). A Niched Pareto genetic algorithm for multiobjective optimization. Evolutionary Computation, 1, 82–87.
15.
Hughes, E. (2001). Evolutionary multi-objective ranking with uncertainty and noise. In Evolutionary Multi-criterion optimization (p. 329343).
16.
Hughes, E. J. (2001). Constraint handling with uncertain and noisy multi-objective evolution. Seoul, Korea.
17.
Knowles, J. D., & Corne, D. W. (2000). Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 2, 149–172.CrossRef
18.
Kolmogorov, A. N. (1960). Foundations of the theory of probability (2nd ed.). Chelsea Pub Co.
19.
Laarabi, M., Sacile, R., et al. (2013). Ranking triangular fuzzy numbers using fuzzy set. In Fuzzy logic and applications (pp. 100–108).
20.
Limbourg, P. (2005). Multi-objective optimization of problems with epistemic uncertainty. In Evolutionary multi-criterion optimization (Vol. 35).
21.
Limbourg, P., & Daniel, E. (2005). An optimization algorithm for imprecise multi-objective problem functions. Evolutionary Computation, 1, 459–466.
22.
Paquete, L., & Sttzle, T. (2007). Stochastic local search algorithms for multiobjective combinatorial optimization: Methods and analysis. Handbook of approximation algorithms and metaheuristics, 13, 79–104.
23.
Shafer, G. (1976). A mathematical theory of evidence. University Press: Princeton.MATH
24.
Smets, P. (1989). Constructing the Pignistic Probability Function in a Context of Uncertainty. In Proceeding 5th Conference on Uncertainty in Artificial intelligence.
25.
Solomon, M. (1987). Algorithms for the vehicle routing and scheduling problem with time window constraints. Operations Research, 35.
26.
Sulieman, D., Jourdan, L., & El-Ghazali, T. (2010). Using multiobjective metaheuristics to solve VRP with uncertain demands. Evolutionary Computation, 1–8.
27.
Teich, J. (2001). Pareto-front exploration with uncertain objectives. In Evolutionary multi-criterion optimization (p. 314328).
28.
Toth, P., & Vigo, D. (2002). The vehicle routing problem. Siam.
29.
Wang, Z., & Tian, F. (2010). A Note of the expected value and variance of Fuzzy variables. International Journal of Nonlinear Science, 9.
30.
Zadeh, L. (1965). Fuzzy sets. Information and Control.
31.
Zadeh, L. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100, 9–34.CrossRef
32.
Zitzler, E., Laumans, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK).
33.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. Evolutionary Computation, 4, 257271.
34.
Zitzler, E., Thiele, L., & Knowles, J. (2005). A tutorial on the performance assessment of stochastic multiobjective optimizers. In Evolutionary multi-criterion optimization.
35.
Zitzler, E., Thiele, L., et al.: Performance assessment of multiobjective optimizers: An analysis and review. Evolutionary Computation, 117–132.
Metadaten
Titel
Possibilistic Framework for Multi-objective Optimization Under Uncertainty
verfasst von
Oumayma Bahri
Nahla Benamor
El-Ghazali Talbi
Copyright-Jahr
2019
Buch
Bioinspired Heuristics for Optimization

Print ISBN: 978-3-319-95103-4

Electronic ISBN: 978-3-319-95104-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-95104-1_1