Artificial neural networks integrated mixed integer mathematical model for multi-fleet heterogeneous time-dependent cash in transit problem with time windows - Ayyildiz, Ertugrul; Taskin, Alev; Yildiz, Aslihan; Ozkan, Coskun (SPRINGER LONDON LTD, 2022)
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 For this purpose, key factors are defined, and a formulation is proposed to determine the money deposited to and withdrawn into ATMs.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.
Keywords: Artificial neural networks; Cash in transit; Mixed integer linear programming; Time-dependent vehicle routing problem; VEHICLE-ROUTING PROBLEM; GREEN VRP; ALGORITHM; TRANSPORTATION; OPTIMIZATION; PREDICTION; DISCRETE; SYSTEM; Computer Science