Abstract
In this paper, we introduce a new city logistics problem arising in the last mile distribution of e-commerce. The problem involves two levels of routing problems. The first requires a design of the routes for a vehicle fleet located at the depots to transport the customer demands to a subset of the satellites. The second level concerns the routing of a vehicle fleet from the satellites to serve all of the customers. A feature of the problem is that customers may provide different delivery options, allowing them to pick up their packages at intermediate pickup facilities. The objective is to minimize the total distribution cost. To solve the problem, a hybrid multi-population genetic algorithm is proposed. An effective heuristic algorithm is designed to generate initial solutions, and several procedures are designed to better manage the population as well as exploit and explore the solution space. The proposed method is tested on a large family of instances, including a real-world instance; the computational results obtained show the effectiveness of the different components of the algorithm.
| Language | English |
|---|---|
| Pages | 765-778 |
| Journal | European Journal of Operational Research |
| Volume | 265 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2018 |
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Keywords
- City logistics
- Hybrid genetic algorithm
- Routing
- Two-echelon
Cite this
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A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution. / Zhou, Lin; Baldacci, Roberto; Vigo, Daniele; Wang, Xu.
In: European Journal of Operational Research, Vol. 265, No. 2, 2018, p. 765-778.Research output: Contribution to journal › Article
TY - JOUR
T1 - A Multi-Depot Two-Echelon Vehicle Routing Problem with Delivery Options Arising in the Last Mile Distribution
AU - Zhou,Lin
AU - Baldacci,Roberto
AU - Vigo,Daniele
AU - Wang,Xu
PY - 2018
Y1 - 2018
N2 - In this paper, we introduce a new city logistics problem arising in the last mile distribution of e-commerce. The problem involves two levels of routing problems. The first requires a design of the routes for a vehicle fleet located at the depots to transport the customer demands to a subset of the satellites. The second level concerns the routing of a vehicle fleet from the satellites to serve all of the customers. A feature of the problem is that customers may provide different delivery options, allowing them to pick up their packages at intermediate pickup facilities. The objective is to minimize the total distribution cost. To solve the problem, a hybrid multi-population genetic algorithm is proposed. An effective heuristic algorithm is designed to generate initial solutions, and several procedures are designed to better manage the population as well as exploit and explore the solution space. The proposed method is tested on a large family of instances, including a real-world instance; the computational results obtained show the effectiveness of the different components of the algorithm.
AB - In this paper, we introduce a new city logistics problem arising in the last mile distribution of e-commerce. The problem involves two levels of routing problems. The first requires a design of the routes for a vehicle fleet located at the depots to transport the customer demands to a subset of the satellites. The second level concerns the routing of a vehicle fleet from the satellites to serve all of the customers. A feature of the problem is that customers may provide different delivery options, allowing them to pick up their packages at intermediate pickup facilities. The objective is to minimize the total distribution cost. To solve the problem, a hybrid multi-population genetic algorithm is proposed. An effective heuristic algorithm is designed to generate initial solutions, and several procedures are designed to better manage the population as well as exploit and explore the solution space. The proposed method is tested on a large family of instances, including a real-world instance; the computational results obtained show the effectiveness of the different components of the algorithm.
KW - City logistics
KW - Hybrid genetic algorithm
KW - Routing
KW - Two-echelon
UR - http://www.scopus.com/inward/record.url?scp=85027703592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027703592&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2017.08.011
DO - 10.1016/j.ejor.2017.08.011
M3 - Article
VL - 265
SP - 765
EP - 778
JO - European Journal of Operational Research
T2 - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 2
ER -