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The Vehicle Routing Problem with Time Window and Randomness in Demands, Travel, and Unloading Times

Author

Listed:
  • Pérez-Lechuga, Gilberto
  • Venegas-Martínez, Francisco

Abstract

Background: The vehicle routing problem (VRP) is of great importance in the Industry 4.0 era because enabling technologies such as the internet of things (IoT), artificial intelligence (AI), big data, and geographic information systems (GISs) allows for real-time solutions to versions of the problem, adapting to changing conditions such as traffic or fluctuating demand. Methods: In this paper, we model and optimize a classic multi-link distribution network topology, including randomness in travel times, vehicle availability times, and product demands, using a hybrid approach of nested linear stochastic programming and Monte Carlo simulation under a time-window scheme. The proposed solution is compared with cutting-edge metaheuristics such as Ant Colony Optimization (ACO), Tabu Search (TS), and Simulated Annealing (SA). Results: The results suggest that the proposed method is computationally efficient and scalable to large models, although convergence and accuracy are strongly influenced by the probability distributions used. Conclusions: The developed proposal constitutes a viable alternative for solving real-world, large-scale modeling cases for transportation management in the supply chain.

Suggested Citation

  • Pérez-Lechuga, Gilberto & Venegas-Martínez, Francisco, 2026. "The Vehicle Routing Problem with Time Window and Randomness in Demands, Travel, and Unloading Times," MPRA Paper 128859, University Library of Munich, Germany, revised 05 Jan 2026.
    • Handle: RePEc:pra:mprapa:128859
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    File URL: https://mpra.ub.uni-muenchen.de/128859/1/MPRA_paper_128859.pdf
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    JEL classification:

    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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