Multimodal Processes Optimization Subject to Fuzzy Operation Time Constraints

  • R. WójcikEmail author
  • I. Nielsen
  • G. Bocewicz
  • Z. Banaszak
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)


Different material handling transport modes provide movement of work pieces between workstations along their manufacturing routes in the Multimodal Transportation Network (MTN). In this context the multimodal processes standing behind multiproduct production flow while executed in MTN can be seen as processes realized with synergic utilization of various local periodically acting processes. Such processes play a determining role in the evaluation of functioning efficiency inter alia in public transport systems, goods transport, energy and data transmission etc. The optimization of Automated Guided Vehicles (AGVs) fleet schedule subject to fuzzy operation times constraints is our main contribution. In the considered case both production rate (takt) and operations execution time are described by imprecise (fuzzy) data.


AGVs multimodal process fuzzy constraints optimization 


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  1. 1.
    Abara, J.: Applying integer linear programming to the fleet assignment problem. Interfaces 19, 4–20 (1989)CrossRefGoogle Scholar
  2. 2.
    Bocewicz, G., Nielsen, I., Banaszak, Z.: Automated Guided Vehicles Fleet Match-up Scheduling with Production Flow Constraints. Engineering Applications of Artificial Intelligence 30, 49–62 (2014)CrossRefGoogle Scholar
  3. 3.
    Hall, N.G., Sriskandarajah, C., Ganesharajah, T.: Operational Decisions in AGV-Served Flowshop Loops: Fleet Sizing and Decomposition. Annals of Operations Research 107, 189–209 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Krenczyk, D., Kalinowski, K., Grabowik, C.: Integration Production Planning and Scheduling Systems for Determination of Transitional Phases in Repetitive Production. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS, vol. 7209, pp. 274–283. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Lu, S.P., Kong, X.T.R., Luo, H., Wong, W.R., Luo, K.B.: Dynamic scheduling of AGVS for tobacco automatic warehouse: A case study. In: Proc. of International Conference on Computers and Industrial Engineering, CIE, pp. 725–733 (2013)Google Scholar
  6. 6.
    Moudani, W., Mora-Camino, F.: A dynamic approach for aircraft assignment and maintenance scheduling by airlines. Journal of Air Transport Management 6, 233–237 (2000)CrossRefGoogle Scholar
  7. 7.
    Pawlewski, P.: Multimodal approach to modeling of manufacturing processes. Procedia CIRP 17, 716–720 (2014)CrossRefGoogle Scholar
  8. 8.
    Polak, M., Majdzik, P., Banaszak, Z., Wójcik, R.: The performance evaluation tool for automated prototyping of concurrent cyclic processes. Fundamenta Informaticae 60(1-4), 269–289 (2004)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Relich, M., Jakabova, M.: A Decision Support Tool for Project Portfolio Management with Imprecise Data. In: 10th International Conference on Strategic Management and its Support by Information Systems, pp. 164–172 (2013)Google Scholar
  10. 10.
    Sitek, P., Wikarek, J.: A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management. International Journal of Production Research, 1–18 (2015), doi:10.1080/00207543.2015.1005762Google Scholar
  11. 11.
    Von Kampmeyer, T.: Cyclic scheduling problems. Ph.D. Dissertation, Mathematik/Informatik, Universität Osnabrück (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • R. Wójcik
    • 1
    Email author
  • I. Nielsen
    • 2
  • G. Bocewicz
    • 3
  • Z. Banaszak
    • 4
  1. 1.Institute of Computer Engineering, Control and RoboticsWrocław University of TechnologyWroclawPoland
  2. 2.Dept. of Mechanical and Manufacturing EngineeringAalborg UniversityAalborgDenmark
  3. 3.Dept. of Computer Science and ManagementKoszalin University of TechnologyKoszalinPoland
  4. 4.Dept. of Business InformaticsWarsaw University of TechnologyWroclawPoland

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