An Adaptive Large Neighborhood Search Solving Large People and Parcel Share-a-Ride Problem

We consider in this paper a variant of people and parcel share-a-ride problem which combines two objectives: minimizing the number of rejected requests and minimizing total travel distance of taxis. We propose an adaptive large neighborhood search algorithm for solving this problem. In addition, to handle large instances of the problem, we propose a bucket data structure for reducing the neighborhood exploration. We analyze the performance of the proposed algorithm on larges instances generated based on taxi traces from San Francisco.