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


AYYILDIZ E., TAŞKIN A., YILDIZ A., ÖZKAN C.

NEURAL COMPUTING & APPLICATIONS, vol.34, no.24, pp.21891-21909, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 24
  • Publication Date: 2022
  • Doi Number: 10.1007/s00521-022-07659-7
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.21891-21909
  • 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
  • Yıldız Technical University Affiliated: Yes

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 For this purpose, key factors are defined, and a formulation is proposed to determine the money deposited to and withdrawn into ATMs.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.