TY - JOUR
T1 - Forward and reverse logistics vehicle routing problems with time horizons in B2C e-commerce logistics
AU - Zhang, Mengdi
AU - Pratap, Saurabh
AU - Zhao, Zhiheng
AU - Prajapati, D.
AU - Huang, George Q.
N1 - Funding Information:
This research was supported, in part, by Natural Science Foundation of the Jiangsu Higher Education Institutions of China (grant number 19KJB580016), Nanjing University of Post and Telecommunications research start-up fund (grant numbers NY218125, NY218126), NSFC Incubation fund (grant number NY219112) and Natural Science Foundation of Jiangsu Province (grant number BK20180749).
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021/10
Y1 - 2021/10
N2 - This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated.
AB - This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated.
KW - CPLEX
KW - differential evolutionary algorithm
KW - E-commerce logistics
KW - meta-heuristic
KW - reverse logistics
KW - vehicle routing problem
UR - http://www.scopus.com/inward/record.url?scp=85090304796&partnerID=8YFLogxK
U2 - 10.1080/00207543.2020.1812749
DO - 10.1080/00207543.2020.1812749
M3 - Journal article
AN - SCOPUS:85090304796
SN - 0020-7543
VL - 59
SP - 6291
EP - 6310
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 20
ER -