| dc.description.abstract | The Covid-19 pandemic and the digital era's rapid growth have shifted
consumer behavior towards online shopping, increasing the demand for "same day delivery." Crowdshipping, involving collaboration between e-platform
retailers and occasional drivers with diverse capacities, adds complexity to route
planning, necessitating advanced last-mile delivery strategies integrating
dedicated and occasional drivers (DDs and ODs). One approach to enhance the
efficiency of crowdshipping is by consolidating orders into batches, enabling a
single courier to handle multiple orders within the same pickup location and drop off route. However, this assignment-batching problem becomes computationally
expensive in real-world scenarios with numerous customer orders and uncertain
demand and supply. To address this, heuristic solution methods are employed.
This study proposes an order clustering and assignment algorithm that combines
Mixed Integer Programming (MIP) with basic game theory principles to
iteratively improve solutions. Additionally, the Adaptive Large Neighborhood
Search (ALNS) algorithm is applied in the routing stage. The approach leverages
a graph-based method by decomposing the original problem into more
manageable sub-problems through clustering. ALNS then refines the solution by
disrupting the assigned sequences post-allocation, overcoming local minima
from the initial clustering phase. The performance of the proposed solution is
tested through experiments based on a real-world case study of GrubHub, as
referenced in key literature. Results indicate that the algorithm effectively
handles varying scales of instances, achieving relatively optimal solutions under
different demand and supply conditions (e.g., density levels, courier availability)
and problem sizes | en_US |
