Skip to main content

Simulation des Entscheidungsträgers unter Unsicherheit – Mehrkriterielle Optimierung für das integrierte Bestands- und Tourenplanungsproblem

  • Chapter
  • First Online:
Entscheidungstheorie und –praxis

Zusammenfassung

Dieser Beitrag behandelt die Problemstellung der Integration eines Entscheidungsträgers (ET) in einen interaktiven Ansatz und geht in diesem Kontext auf den offensichtlichen Bedarf von Simulationsmethoden ein. Das neuartige Simulationskonzept fokussiert hierbei die experimentelle Integration von Verhaltensmustern des ET. Solche Verhaltensweisen können beispielsweise die Ermüdung oder das Lernverhalten des ET während des Interaktionsprozesses beinhalten. In den bisherigen Ansätzen wurde dem Verhalten des Experten Rechnung getragen, indem sich viele Arbeiten mit der Anwendung von Nutzenfunktionen beschäftigen. Im Rahmen experimenteller Arbeit wird anhand von Testinstanzen für das integrierte Bestands- und Tourenplanungsproblem aufgezeigt, dass der Lösungsansatz in der Lage ist, Lösungen zu generieren, die gegen eine meistpräferierte Lösung konvergieren.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Literatur

  • Ajenstat J, Jones P (2004) Virtual decision maker for stock market trading as a network for cooperating autonomous intelligent agents. In: Sprague RH (Hrsg) Proceedings of the 37th annual Hawaii international conference on system sciences (HICSS 37). IEEE Computer Society Press

    Google Scholar 

  • Aksoy Y, Butler TW, Minor ED (1996) Comparative studies in interactive multiple objective mathematical programming. Eur J Oper Res 89(2):408–422

    Google Scholar 

  • Belton V, Branke J, Eskelinen P, Greco S, Molina J, Ruiz F, Słowiński RS (2008) Interactive multiobjective optimization from a learning perspective, pages 405–435. Volume 5252 of Branke et al. (J Branke et al. 2008).

    Google Scholar 

  • Bertazzi L, Speranza MG (2013) Inventory routing problems with multiple customers. Euro J Transp Logist 2(3):255–275

    Google Scholar 

  • Bertazzi L, Paletta G, Speranza M (2002) Deterministic order-up-to level policies in an inventory routing problem. Transp Sci 36(1):119–132

    Google Scholar 

  • Branke J, Deb K, Miettinen K, Słowiński R (Hrsg) (2008) Multiobjective Optimization – interactive and evolutionary approaches, volume 5252 of Lecture Notes in Computer Science. Springer, Berlin

    Google Scholar 

  • Caballero R, Luque M, Molina J, Ruiz F (2002) Promoin: an interactive system for multiobjective programming. Int J Inf Technol Decis Mak 1(4):635–656

    Google Scholar 

  • Clarke G, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12:568–581

    Google Scholar 

  • Coelho LC (2012) Flexibility and consistency in inventory-routing. Ph.d., HEC Montréal – Affiliée à l Université de Montréal

    Google Scholar 

  • Coelho LC, Laporte G (2013) Exact solutions for several classes of inventory-routing problems. Comput Oper Res 40(2):558–565

    Google Scholar 

  • Durbach IN, Stewart TJ (2009) Using expected values to simplify decision making under uncertainty. Omega 37(2):312–330

    Google Scholar 

  • Geiger MJ (2005) Multikriterielle Ablaufplanung. Deutscher Universitäts-Verlag, Wiesbaden

    Google Scholar 

  • Geiger MJ, Sevaux M (2011) The biobjective inventory routing problem—problem solution and decision support. In: Pahl J, Reiners T, Voß S (Hrsg) Network optimization, volume 6701 of Lecture Notes in Computer Science. Springer, Berlin, pp 365–378

    Google Scholar 

  • Gendreau M, Potvin J-Y, Bräysy O, Hasle G, Løkketangen A (2008) Metaheuristics for the vehicle routing problem and its extensions: a categorized bibliography. In: Golden B, Raghavan S, Wasil E (Hrsg) The vehicle routing problem: latest advances and new challenges, volume 43 of Operations Research/Computer Science Interfaces. Springer, Berlin, pp 143–169

    Google Scholar 

  • Hakanen J, Miettinen K, Mäkelä MM, Manninen J (2005) On interactive multiobjective optimization with NIMBUS\(^{\rm \textregistered}\) in chemical process design. J Multi-Criteria Decis Anal 13(2–3):125–134

    Google Scholar 

  • Huber S, Geiger MJ, Sevaux M (2014) Interactive approach to the inventory routing problem: computational speedup through focused search. In Lecture Notes in Logistics. Springer (accepted)

    Google Scholar 

  • Hwang CL, Paidy SR, Yoon K, Masud ASM (1980) Mathematical programming with multiple objectives: a tutorial. Comput Oper Res 7(1–2):5–31

    Google Scholar 

  • Klein G, Moskowitz H, Ravindran A (1990) Interactive multiobjective optimization under uncertainty. Manage Sci 36(1):58–75

    Google Scholar 

  • Köksalan MM, Karwan MH, Zionts S (1984) An improved method for solving multiple criteria problems involving discrete alternatives. IEEE Trans Syst Man Cybern SMC 14(1):24–34

    Google Scholar 

  • Köksalan M, Wallenius J, Zionts S (2011) Multiple criteria decision making—from early history to the 21st century. World Scientific, Singapore

    Google Scholar 

  • Köksalan MM, Sagala PNS (1995) Interactive approaches for discrete alternative multiple criteria decision making with monotone utility functions. Manage Sci 41(7):1158–1171

    Google Scholar 

  • Korhonen P (1988) A visual reference direction approach to solving discrete multiple criteria problems. Eur J Oper Res 34(2):152–159

    Google Scholar 

  • Korhonen PJ, Laakso J (1986) A visual interactive method for solving the multiple criteria problems. Eur J Oper Res 24(2):277–287

    Google Scholar 

  • Lahdelma R, Makkonen S, Salminen P (2009) Two ways to handle dependent uncertainties in multi-criteria decision problems. Omega 37(1):79–92

    Google Scholar 

  • Li F, Golden B, Wasil E (2007) A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. Comput Oper Res 34(9):2734–2742

    Google Scholar 

  • Luque M, Caballero R, Molina J, Ruiz F (2007) Eqivalent information for multiobjective interactive procedures. Manage Sci 53(1):125–134

    Google Scholar 

  • Luque M, Yang J-B, Wong BYH (2009a) PROJECT method for multiobjective optimization based on gradient projection and reference points. IEEE Trans Syst Man Cybern Part A Syst Hum 39(4):864–879

    Google Scholar 

  • Luque M, Miettinen K, Eskelinen P, Ruiz F (2009b) Incorporating preference information in interactive reference point methods for multiobjective optimization. Omega 37(2):450–462

    Google Scholar 

  • Luque M, Ruiz F, Miettinen K (2011) Global formulation for interactive multiobjective optimization. OR Spectr 33(1):27–48

    Google Scholar 

  • Malakooti B (1989) Theories and an exact interactive paired-comparison approach for discrete multiple-criteria problems. IEEE Trans Syst Man Cybern SMC 19(2):365–378

    Google Scholar 

  • Marsden JR, Pakath R, Wibowo K (2006) Decision making under time pressure with different information sources and performance-based financial incentives: part 3. Decis Support Syst 42(1):186–203

    Google Scholar 

  • Miettinen K (1999) Nonlinear multiobjective optimization. Kluwer, Boston

    Google Scholar 

  • Miettinen K (2008) Introduction to multiobjective optimization: noninteractive approaches, pages 1–26. Volume 5252 of Branke et al. (J Branke et al. 2008)

    Google Scholar 

  • Miettinen K, Ruiz F, Wierzbicki AP (2008) Introduction to multiobjective optimization: interactive approaches, pages 27–57. Volume 5252 of Branke et al. (J Branke et al. 2008)

    Google Scholar 

  • Miettinen K, Mustajoki J, Stewart TJ (2014) Interactive multiobjective optimization with NIMBUS for decision making under uncertainty. OR Spectr 36(1):39–56

    Google Scholar 

  • Nakayama H, Sawaragi Y (1984) Satisficing trade-off method for multiobjective programming. In Grauer M, Wierzbicki AP (Hrsg) Interactive decision analysis, volume 229 of Lecture Notes in Economics and Mathematical Systems, pages 113–122. Springer, Berlin

    Google Scholar 

  • Oliveira C, Antunes CH (2009) An interactive method of tackling uncertainty in interval multiple objective linear programming. J Math Sci 161(6):854–866

    Google Scholar 

  • Phelps SP, Köksalan M (2003) An interactive evolutionary metaheuristic for multiobjective combinatorial optimization. Manage Sci 49(12):1726–1738

    Google Scholar 

  • Roostaee R, Izadikhah M, Hosseinzadeh Lotfi F (2012) An interactive procedure to solve multi-objective decision-making problem: an improvement to STEM Method. J Appl Math 2012:1–18

    Google Scholar 

  • Ruiz F, Luque M, Cabello JM (2009) A classification of the weighting schemes in reference point procedures for multiobjective programming. J Oper Res Soc 60(4):544–553

    Google Scholar 

  • Sevaux M, Geiger MJ (2011) Inventory routing and on-line inventory routing file format. Technical report RR-11-01-01, Helmut-Schmidt-University, University of the Federal Armed Forces

    Google Scholar 

  • Shin WS, Ravindran A (1992) A comparative study of interactive tradeoff cutting plane methods for MOMP. Eur J Oper Res 56(3):380–393

    Google Scholar 

  • Steuer RE, Choo E-U (1983) An interactive weighted tchebycheff procedure for multiple objective programming. Math Progr 26(3):326–344

    Google Scholar 

  • Stewart TJ (2005) Dealing with uncertainties in MCDA, volume 78 of International Series in Operations Research & Management Science, pages 445–470. Springer, New York

    Google Scholar 

  • Stewart TJ, French S, Rios J (2013) Integrating multicriteria decision analysis and scenario planning – review and extension. Omega 41(4):679–688

    Google Scholar 

  • Tversky A, Sattath S, Slovic P (2000) Contingent weighting in judgement and choice. In Kahneman D, Tversky A (Hrsg) Choices, Values, and Frames, pages 503–517. Cambridge University Press

    Google Scholar 

  • Vanderpooten D (1989) The interactive approach in mcda: a technical framework and some basic conceptions. Math Comput Modell 12(10–11):1213–1220

    Google Scholar 

  • Wierzbicki AP (1980) The use of reference objectives in multiobjective optimization. In Fandel G, Gal T (Hrsg) Multiple criteria decision making theory and application, volume 177 of Lecture Notes in Economics and Mathematical Systems, pages 468–486. Springer, Berlin

    Google Scholar 

  • Zanakis SH, Solomon A, Wishart N, Dublish S (1998) Multi-attribute decision making: a simulation comparison of selected methods. Eur J Oper Res 107(3):507–529

    Google Scholar 

  • Zionts S, Wallenius J (1976) An interactive programming method for solving the multiple criteria problem. Manage Sci 22:652–663

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sandra Huber .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Huber, S., Geiger, M., Sevaux, M. (2015). Simulation des Entscheidungsträgers unter Unsicherheit – Mehrkriterielle Optimierung für das integrierte Bestands- und Tourenplanungsproblem. In: Schenk-Mathes, H., Köster, C. (eds) Entscheidungstheorie und –praxis. Springer Gabler, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46611-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46611-7_1

  • Published:

  • Publisher Name: Springer Gabler, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46610-0

  • Online ISBN: 978-3-662-46611-7

  • eBook Packages: Business and Economics (German Language)

Publish with us

Policies and ethics