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Artificial neural networks integrated mixed integer mathematical model for multi-fleet heterogeneous time-dependent cash in transit problem with time windows

Abstract

The cash in transit (CIT) problem is a version of the vehicle routing problem (VRP), which deals with the planning of money distribution from the depot(s) to the automated teller machines (ATMs) safely and quickly. This study investigates a novel CIT problem, which is a variant of time-dependent VRP with time windows. To establish a more realistic approach to the time-dependent CIT problem, vehicle speed varying according to traffic density is considered. The problem is formulated as a mixed-integer mathematical model. Artificial neural networks (ANNs) are used to forecast the money demand for each ATM. For this purpose, key factors are defined, and a formulation is proposed to determine the money deposited to and withdrawn into ATMs. The mathematical model is run for different scenarios, and optimum routes are obtained.

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Ayyıldız, E., Taşkın, A., Yıldız, A. et al. Artificial neural networks integrated mixed integer mathematical model for multi-fleet heterogeneous time-dependent cash in transit problem with time windows. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-022-07659-7

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  • DOI: https://doi.org/10.1007/s00521-022-07659-7

Keywords

  • Artificial neural networks
  • Cash in transit
  • Mixed integer linear programming
  • Time-dependent vehicle routing problem