| dc.description.abstract | It is widely accepted that the Two-echelon vehicle routing problem (2E-VRP) addresses
the intricacies of distribution networks by incorporating larger vehicles for the first
echelon and smaller vehicles for the second echelon. Goods supplied from the
distribution centers to serve customers are transferred at intermediate points between two
echelons called “satellites”. Recently there has been a spate of interest since it helps
reduce overall operating costs, improve last-mile delivery efficiency, and maximize
resource usage. This study dedicates the concept of synchronizing mobile satellites that
consider the first echelon vehicles at the customer location to be satellites and allow the
second echelon to not return to the departure satellites. The problem reflects real-world
logistics challenges for the perishable goods of a company in Vietnam. A mixed-integer
linear programming model was used to find exact solutions with the objective of
minimizing transportation costs and the variants of capacity, time windows, and
synchronizing mobile satellites. A Hybrid Genetic Algorithm and Tabu Search
Algorithm were proposed to approach a larger-scale problem. This study investigated the
use of benchmark instances from the University of Vienna and real data from the
company in Vietnam using the combination of trucks and motorbikes. The computational
experiment was presented to validate the model and figure out that Hybrid Genetic
Algorithm have better performance in terms of time and the optimal solution. From that,
the new approachs is implemented to generate the optimal routes that assit the studied
company in costs- and time-saving. Taking the concept of synchronizing mobile satellites
in 2E-VRP into account for the first time is the solution for many complex logistics
distribution networks, especially for perishable goods. Although this study has generated
a big contribution to the literature review, there remains a need for an efficient approach
that can solve more constraints that reflect real-world challenges. | en_US |
