TY - JOUR
T1 - A two-stage algorithm for bi-objective logistics model of cash-in-transit vehicle routing problems with economic and environmental optimization based on real-time traffic data
AU - Jin, Yuanzhi
AU - Ge, Xianlong
AU - Zhang, Long
AU - Ren, Jingzheng
N1 - Funding Information:
This research was funded by the Chongqing graduate Scientific research innovation Project [grant number CYB20178, 2020], and National Social Science Foundation of China [grant number 19CGL041, 2019]. This study was also supported by The Start-up Grant of The Hong Kong Polytechnic University for New Employees (Project title: Multi-criteria Decision Making for More Sustainable Transportation, project account code: 1-ZE8W).
Publisher Copyright:
© 2021
PY - 2022/3
Y1 - 2022/3
N2 - Traffic congestion problems are very common in large municipalities, especially in areas with karst features. Traffic jams happen in many key traffic nodes (such as the bridges across the river and the tunnels through the mountains) frequently, which may lead to severe challenges for the vehicle routing optimization. To effectively solve the routing problem of Cash-in-Transit (CIT) sectors, this study aims to establish a novel bi-objective Cash-in-Transit Vehicle Routing Problem (CTVRP) model, including both the economic and environmental objectives, and designs a Nearest Neighbor-first Iterated Local Search-second algorithm (NN-ILS) with the consideration of the special terrain. Then, a case study of a CIT company is performed to demonstrate the model and algorithm and a vivid solution is presented in real road network after the optimization by using the route fitting procedure. Meanwhile, the accuracy and effectiveness of the algorithm is verified by comparing it with several classical algorithms and OR-Tools solver. The experimental results show that the developed algorithm can help the decision-makers to obtain the solutions with high quality compared with the classical algorithms. Finally, the uncertainty of the developed algorithm is analyzed empirically and the Multi-Attribute Decision Making (MADM) combined with Principal Component Analysis (PCA) is utilized to support decision-makers to select the best satisfying solution instead of choosing the solution with minimum objective value(s).
AB - Traffic congestion problems are very common in large municipalities, especially in areas with karst features. Traffic jams happen in many key traffic nodes (such as the bridges across the river and the tunnels through the mountains) frequently, which may lead to severe challenges for the vehicle routing optimization. To effectively solve the routing problem of Cash-in-Transit (CIT) sectors, this study aims to establish a novel bi-objective Cash-in-Transit Vehicle Routing Problem (CTVRP) model, including both the economic and environmental objectives, and designs a Nearest Neighbor-first Iterated Local Search-second algorithm (NN-ILS) with the consideration of the special terrain. Then, a case study of a CIT company is performed to demonstrate the model and algorithm and a vivid solution is presented in real road network after the optimization by using the route fitting procedure. Meanwhile, the accuracy and effectiveness of the algorithm is verified by comparing it with several classical algorithms and OR-Tools solver. The experimental results show that the developed algorithm can help the decision-makers to obtain the solutions with high quality compared with the classical algorithms. Finally, the uncertainty of the developed algorithm is analyzed empirically and the Multi-Attribute Decision Making (MADM) combined with Principal Component Analysis (PCA) is utilized to support decision-makers to select the best satisfying solution instead of choosing the solution with minimum objective value(s).
KW - Cash-in-transit
KW - Iterated local search algorithm
KW - Multi-attribute decision making
KW - Nearest neighbor algorithm
KW - Vehicle Routing Problem (VRP)
UR - https://www.sciencedirect.com/science/article/pii/S2452414X21000716
UR - http://www.scopus.com/inward/record.url?scp=85122667313&partnerID=8YFLogxK
U2 - 10.1016/j.jii.2021.100273
DO - 10.1016/j.jii.2021.100273
M3 - Journal article
SN - 2452-414X
VL - 26
JO - Journal of Industrial Information Integration
JF - Journal of Industrial Information Integration
M1 - 100273
ER -