A Parallel Simulated Annealing Enhancement Of The Optimal-Matching Heuristic For Ridesharing

In this paper, we develop an efficient parallel heuristic method to solve the global optimization problem associated with the ridesharing system. Based on the carefully formalized problem and objective function, we fully utilize the heuristic characteristics of the algorithm for handling the real-life constraints in ridesharing. Following the principles of simulated annealing, our method is adaptive in handling the matching and route optimization tasks. We develop an efficient parallel scheme with simulated annealing, named PCSA, for solving the global optimization problem for ridesharing. Our algorithm is capable of efficiently addressing the potential of ridesharing by exploiting the mobility information of the ride requests. Based on extensive experiments on large real-world data, we validate the performance of our parallel heuristic algorithm. Our results confirm the effectiveness and efficiency of the proposed method and its superiority over all other benchmarks.