Adaptive guided ejection search for pickup and delivery with time windows

The pickup and delivery problem with time windows is an NP-hard discrete optimization problem with two objectives-to minimize the fleet serving transportation requests, and to minimize the distance traveled during this service. Although there exist exact algorithms for tackling this problem, they are still difficult to apply in massively large practical scheduling scenarios due to their time complexities. Hence, the approximate methods became the main stream of research in this field. In this paper, we propose an adaptive guided ejection search algorithm for solving the pickup and delivery with time windows. The pivotal part of this technique is the pre-processing step, in which the instance characteristics concerning its underlying structure are extracted in the clustering and histogram-based analyses. Then, the k-nearest neighbor algorithm is applied to classify the instance to an appropriate class. Finally, the most suitable variant of our enhanced guided ejection search algorithm is adaptively chosen for solving this instance based on the classification outcome. An extensive experimental study performed on the full Li and Lim's benchmark (encompassing 354 problem instances belonging to 6 classes) revealed that our pre-processing allows for achieving very high classification accuracy, thus for selecting the best variant of the enhanced guided ejection search.