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

作     者：Ayyildiz, Ertugrul Taskin, Alev Yildiz, Aslihan Ozkan, Coskun 

作者机构：Karadeniz Tech Univ Dept Ind Engn Trabzon Turkey Yildiz Tech Univ Dept Ind Engn Istanbul Turkey 

出 版 物：《NEURAL COMPUTING & APPLICATIONS》 (神经网络计算与应用)

年 卷 期：2022年第34卷第24期

页      面：21891-21909页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

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 For this purpose, key factors are defined, and a formulation is proposed to determine the money deposited to and withdrawn into ATMs.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