Business & Information Systems Engineering

, Volume 6, Issue 1, pp 17–24 | Cite as

Model-Based Decision Support in Manufacturing and Service Networks

  • Andreas Fink
  • Natalia Kliewer
  • Dirk Mattfeld
  • Lars MönchEmail author
  • Franz Rothlauf
  • Guido Schryen
  • Leena Suhl
  • Stefan Voß
Research Notes


In this paper, we sketch some of the challenges that should be addressed in future research efforts for model-based decision support in manufacturing and service networks. This includes integration issues, taking into account the autonomy of the decision-making entities in face of information asymmetry, the modeling of preferences of the decision makers, efficiently determining robust solutions, i.e. solutions that are insensitive with respect to changes in the problem data, and a reduction of the time needed for model building and usage. The problem solution cycle includes problem analysis, the design of appropriate algorithms and their performance assessment. We are interested in a prototypical integration of the proposed methods within application systems, which can be followed up with field tests of the extended application systems. We argue that the described research agenda requires the interdisciplinary collaboration of business and information systems engineering researchers with colleagues from management science, computer science, and operations research. In addition, we present some exemplifying, illustrative examples of relevant research results.


Model-based decision support Manufacturing and service networks Research areas in business and information systems engineering 



The authors would like to thank Reha Uzsoy, North Carolina State University, for useful comments on this paper.


  1. Barlatt AY, Cohn A, Gusikhin O, Fradkin Y, Davidson R, Batey J (2012) Ford motor company implements integrated planning and scheduling in a complex automotive manufacturing environment. Interfaces 42(5):478–491 CrossRefGoogle Scholar
  2. Belz R, Mertens P (1994) SIMULEX – a multiattribute DSS to solve rescheduling problems. Annals of Operations Research 52(3):107–129 CrossRefGoogle Scholar
  3. Bilyk A, Mönch L (2012) Variable neighborhood search-based subproblem solution procedures for a parallel shifting bottleneck heuristic for complex job shops. In: Proc of IEEE conference on automation science and engineering, Seoul, pp 415–420 Google Scholar
  4. Crainic TG, Gendreau M, Potvin JY (2009) Intelligent freight-transportation systems: assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies 16(6):541–557 CrossRefGoogle Scholar
  5. D’Andrea R (2012) A revolution in the warehouse: a retrospective on Kiva systems and the grand challenges ahead. IEEE Transaction on Automation Science and Engineering 9(4):638–639 CrossRefGoogle Scholar
  6. Degbotse A, Denton BT, Fordyce K, Milne RJ, Orzell R, Wang C-T (2013) IBM blends heuristics and optimization to plan its semiconductor supply chain. Interfaces 43(2):130–142 CrossRefGoogle Scholar
  7. Dudek G (2009) Collaborative planning in supply chains, 2nd edn. Springer, Heidelberg CrossRefGoogle Scholar
  8. Driessel R, Mönch L (2012) An integrated scheduling and material handling approach for complex job shops: a computational study. International Journal of Production Research 50(20):5966–5985 CrossRefGoogle Scholar
  9. Dück V, Ionescu L, Kliewer N, Suhl L (2012) Increasing stability of crew and aircraft schedules. Transportation Research Part C: Emerging Technologies 20(1):47–61 CrossRefGoogle Scholar
  10. Ehm H, Wenke H, Mönch L, Ponsignon T, Forstner L (2011) Towards a supply chain simulation reference model for the semiconductor industry. In: Proc of the 2011 winter simulation conference, Phoenix, USA, pp 2124–2135 Google Scholar
  11. Ehmke J, Meisel S, Mattfeld DC (2012) Floating car based travel times for city logistics. Transportation Research Part C: Emerging Technologies 21(1):338–352 CrossRefGoogle Scholar
  12. Fink A, VoßS (2003) Anwendung von Metaheuristiken zur Lösung betrieblicher Planungsprobleme – Potenziale und Grenzen einer softwaretechnischen Unterstützung. WIRTSCHAFTSINFORMATIK 45(4):395–407 CrossRefGoogle Scholar
  13. Fink A, Homberger J (2013) An ant-based coordination mechanism for resource-constrained project scheduling with multiple agents and cash flow objectives. Flexible Services and Manufacturing Journal 25(1/2):94–121 CrossRefGoogle Scholar
  14. Fischbein SA, Yellig E (2011) Why it is so hard to build and validate discrete event simulation models of manufacturing facilities. In: Kempf K, Keskinocak P, Uzsoy R (eds) Planning production and inventories in the extended enterprise: a state of the art handbook, vol 2. Springer, Heidelberg, pp 271–288 CrossRefGoogle Scholar
  15. Graves SC (2010) Uncertainty and production planning. In: Kempf K, Keskinocak P, Uzsoy R (eds) Planning production and inventories in the extended enterprise: a state of the art handbook, vol 1. Springer, Heidelberg, pp 83–102 Google Scholar
  16. Greenberg HJ (1996) The ANALYZE rulebase for supporting LP analysis. Annals of Operations Research 65(1):91–126 CrossRefGoogle Scholar
  17. Kant G, Jacks M, Aantjes C (2008) Coca-Cola enterprises optimizes vehicle routes for efficient product delivery. Interfaces 38(1):40–50 CrossRefGoogle Scholar
  18. Lang F, Fink A, Schryen G (2012) Elicitating, modeling, and processing uncertain human preferences for software agents in electronic negotiations: an empirical study. In: Proc of the international conference on information systems (ICIS) 2012, Association for Information Systems Google Scholar
  19. Maniezzo V, Stützle T, VoßS (eds) (2009) Matheuristics: hybridizing metaheuristics and mathematical programming. Annals of information systems, vol 10. Springer, Heidelberg Google Scholar
  20. Mönch L (2006) Autonome und kooperative Steuerung komplexer Produktionsprozesse mit Multi-Agenten-Systemen. WIRTSCHAFTSINFORMATIK 48(2):107–119 CrossRefGoogle Scholar
  21. Nisan N, Rougarden T, Tardos E, Vazirani VV (eds) (2007) Algorithmic game theory. Cambridge University Press, New York Google Scholar
  22. Orcun S, Asmundsson JM, Uzsoy R, Clement JP, Pekny JF, Rardin RL (2007) Supply chain optimization and protocol environment (SCOPE) for rapid prototyping and analysis of complex supply chains. Production Planning & Control 18(5):388–406 CrossRefGoogle Scholar
  23. Pfeiffer J, Golle U, Rothlauf F (2008) Reference point based multi-objective algorithms for group decisions. In: Proc of the genetic and evolutionary computation conference, pp 697–704 Google Scholar
  24. Plattner H, Zeier A (2011) In-memory data management – an inflection point for enterprise applications. Springer, Heidelberg Google Scholar
  25. Rose O (2007) Improved simple simulation models for semiconductor wafer fabrication facilities. In: Proc of the 2007 winter simulation conference, pp 1708–1712 CrossRefGoogle Scholar
  26. Rothlauf F (2011) Design of modern heuristics: principles and application. Springer, Heidelberg CrossRefGoogle Scholar
  27. Schneeweiss C (2003) Distributed decision making, 2nd edn. Springer, Heidelberg CrossRefGoogle Scholar
  28. Schulz C, Hasle G, Brodtkorb AR, Hagen TR (2013) GPU computing in discrete optimization. Part II. Survey focused on routing problems. EURO Journal on Transportation and Logistics 2:159–186 CrossRefGoogle Scholar
  29. Schütte R (2012) In-Memory-Technologien: Überlegungen zur Begründbarkeit und zum Einsatz beim Betrieb von großen Systemen, WI-Meinung/Dialog. WIRTSCHAFTSINFORMATIK 54(4):211–213 Google Scholar
  30. Steinzen I, Gintner V, Suhl L, Kliewer N (2010) A time-space network approach for the integrated vehicle and crew scheduling problem with multiple depots. Transportation Science 44(3):367–382 CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2014

Authors and Affiliations

  • Andreas Fink
    • 1
  • Natalia Kliewer
    • 2
  • Dirk Mattfeld
    • 3
  • Lars Mönch
    • 4
    Email author
  • Franz Rothlauf
    • 5
  • Guido Schryen
    • 6
  • Leena Suhl
    • 7
  • Stefan Voß
    • 8
  1. 1.Helmut-Schmidt-Universität HamburgHamburgGermany
  2. 2.Freie Universität BerlinBerlinGermany
  3. 3.Technische Universität BraunschweigBraunschweigGermany
  4. 4.FernUniversität in HagenHagenGermany
  5. 5.Johannes Gutenberg-Universität MainzMainzGermany
  6. 6.Universität RegensburgRegensburgGermany
  7. 7.Universität PaderbornPaderbornGermany
  8. 8.Universität HamburgHamburgGermany

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