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Continuous Genetic Algorithms as Intelligent Assistance for Resource Distribution in Logistic Systems

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  • Łukasz Wieczorek

    (Institute of Information Technology, Lodz University of Technology, 90-924 Łódź, Poland)

  • Przemysław Ignaciuk

    (Institute of Information Technology, Lodz University of Technology, 90-924 Łódź, Poland)

Abstract

This paper addresses the problem of resource distribution control in logistic systems influenced by uncertain demand. The considered class of logistic topologies comprises two types of actors—controlled nodes and external sources—interconnected without any structural restrictions. In this paper, the application of continuous-domain genetic algorithms (GAs) is proposed in order to support the optimization process of resource reflow in the network channels. GAs allow one to perform simulation-based optimization and provide desirable operating conditions in the face of a priori unknown, time-varying demand. The effectiveness of inventory management process governed under an order-up-to policy involves two different objectives—holding costs and service level. Using the network analytical model with the inventory management policy implemented in a centralized way, GAs search a space of candidate solutions to find optimal policy parameters for a given topology. Numerical experiments confirm the analytical assumptions.

Suggested Citation

  • Łukasz Wieczorek & Przemysław Ignaciuk, 2018. "Continuous Genetic Algorithms as Intelligent Assistance for Resource Distribution in Logistic Systems," Data, MDPI, vol. 3(4), pages 1-14, December.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:68-:d:190978
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Poppe, Joeri & Basten, Rob J.I. & Boute, Robert N. & Lambrecht, Marc R., 2017. "Numerical study of inventory management under various maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 262-273.
    3. Gereffi, Gary & Frederick, Stacey, 2010. "The global apparel value chain, trade and the crisis : challenges and opportunities for developing countries," Policy Research Working Paper Series 5281, The World Bank.
    4. Grazia Speranza, M., 2018. "Trends in transportation and logistics," European Journal of Operational Research, Elsevier, vol. 264(3), pages 830-836.
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