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Fuzzy and Evolutionary Algorithms for Transport Logistics Under Uncertainty

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Intelligent and Fuzzy Techniques: Smart and Innovative Solutions (INFUS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1197))

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Abstract

This paper deals with the analysis of fuzzy and evolutionary approaches for efficiently solving vehicle routing problems (VRP) with constraints on vehicle’s capacity (CVRP) and time-windows (VRPTW). Authors focused their research on CVRP for marine bunkering tankers, in particular, on the planning of tanker’s routes under uncertainty of fuel demands at nodes. Triangular fuzzy numbers (TFNs) are proposed for modeling uncertain demands. In this case, the maximum possible number of customers is calculated, which can be served based on the subtraction operation with TFNs. In the paper, the authors also analyzed the planning of transport routes with time-windows. Currently, there are several methods and algorithms for planning of transport routes with time-windows, in particular: saving and sweeping algorithms, ant colony optimization (ACO) algorithm, artificial bee colony (ABC) algorithm, etc. In this paper, the authors discussed the features of using the ACO algorithm and the ABC algorithm to solve the vehicle routing problems with time-windows and the influence of their application on the results. The modeling results confirm the efficiency of the proposed fuzzy and evolutionary algorithms.

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Author information

Authors and Affiliations

  1. Intelligent Information Systems Department, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

    Yuriy Kondratenko, Galyna Kondratenko, Ievgen Sidenko & Mykyta Taranov

Authors
  1. Yuriy Kondratenko
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  2. Galyna Kondratenko
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  3. Ievgen Sidenko
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  4. Mykyta Taranov
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Corresponding author

Correspondence to Mykyta Taranov .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  3. Department of Industrial Engineering, Istanbul Technical University, İstanbul, Turkey

    Basar Oztaysi

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

  5. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  6. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

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Kondratenko, Y., Kondratenko, G., Sidenko, I., Taranov, M. (2021). Fuzzy and Evolutionary Algorithms for Transport Logistics Under Uncertainty. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_169

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