TY - JOUR
T1 - Green logistics location-routing problem with eco-packages
AU - Wang, Yong
AU - Peng, Shouguo
AU - Zhou, Xuesong
AU - Mahmoudi, Monirehalsadat
AU - Zhen, Lu
N1 - Funding Information:
The authors would like to express our sincere appreciation for the valuable comments made by three anonymous reviewers, which helped us to improve the quality of this paper. The first two authors and the fifth author of this paper are supported by National Natural Science Foundation of China (Project No. 71871035 , 71831008 , 71671107 ), Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 18YJC630189 ), Key Science and Technology Research Project of Chongqing Municipal Education Commission (No. KJZD-K202000702 ), Social Science Planning Foundation of Chongqing of China (No. 2019YBGL054 ), and Key Project of Human Social Science of Chongqing Municipal Education Commission (No. 20SKGH079 ), This research is supported by 2018 Chongqing Liuchuang Plan Innovation Project (No. cx2018111 ).
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/11
Y1 - 2020/11
N2 - Optimization of the green logistics location-routing problem with eco-packages involves solving a two-echelon location-routing problem and the pickup and delivery problem with time windows. The first echelon consists of large eco-package transport, which is modeled by a time-discretized transport-concentrated network flow programming in the resource sharing state–space–time (SST) network. The second echelon focuses on small eco-package pickups and deliveries, established by the cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities. In addition, the Gaussian mixture clustering algorithm is utilized to assign customers to their respective service providers in the pickup and delivery process, and a Clarke–Wright saving method-based non-dominated sorting genetic algorithm II is designed to optimize pickup and delivery routes, and improve their cost-effectiveness and degree of synchronization. Different strategy testing results are used in the service phase as input data to calculate the cost of the transport phase, which is solved through a Lagrangian relaxation approach. The 3D SST network representation innovatively captures the eco-package route sequence and state transition constraints over the shortest path in the pickup and delivery at any given moment of the transport phase. A large-scale logistics network in Chengdu, China, is used to demonstrate the proposed model and algorithm, and undertake sensitivity analysis considering the life cycle of green eco-packages.
AB - Optimization of the green logistics location-routing problem with eco-packages involves solving a two-echelon location-routing problem and the pickup and delivery problem with time windows. The first echelon consists of large eco-package transport, which is modeled by a time-discretized transport-concentrated network flow programming in the resource sharing state–space–time (SST) network. The second echelon focuses on small eco-package pickups and deliveries, established by the cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities. In addition, the Gaussian mixture clustering algorithm is utilized to assign customers to their respective service providers in the pickup and delivery process, and a Clarke–Wright saving method-based non-dominated sorting genetic algorithm II is designed to optimize pickup and delivery routes, and improve their cost-effectiveness and degree of synchronization. Different strategy testing results are used in the service phase as input data to calculate the cost of the transport phase, which is solved through a Lagrangian relaxation approach. The 3D SST network representation innovatively captures the eco-package route sequence and state transition constraints over the shortest path in the pickup and delivery at any given moment of the transport phase. A large-scale logistics network in Chengdu, China, is used to demonstrate the proposed model and algorithm, and undertake sensitivity analysis considering the life cycle of green eco-packages.
KW - Clarke–Wright
KW - Lagrangian relaxation model
KW - Location-routing problem
KW - State–space–time network
KW - Synchronization degree
UR - http://www.scopus.com/inward/record.url?scp=85092736898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092736898&partnerID=8YFLogxK
U2 - 10.1016/j.tre.2020.102118
DO - 10.1016/j.tre.2020.102118
M3 - Article
AN - SCOPUS:85092736898
SN - 1366-5545
VL - 143
JO - Transportation Research, Part E: Logistics and Transportation Review
JF - Transportation Research, Part E: Logistics and Transportation Review
M1 - 102118
ER -