@inproceedings{07d5e7721cc54efa82d13768c93ac219,
title = "A parallel simulated annealing enhancement of the optimal-matching heuristic for ridesharing",
abstract = "In this paper, we develop an efficient parallelheuristic method for solving the global optimization problemassociated with the ridesharing system. Based on the carefullyformalized problem and objective function, we fully utilize theheuristic characteristics of the algorithm for handling the real-lifeconstraints in ridesharing. Following the principles of simulatedannealing, our method is adaptive in handling the matchingand route optimization tasks. We develop an efficient parallelscheme with simulated annealing, named PCSA, for solving theglobal optimization problem for ridesharing. Our algorithm iscapable to efficiently address the potential of ridesharing byexploiting the mobility information of the ride requests. Basedon extensive experiments on large real-world data, we validatethe performance of our parallel heuristic algorithm. Our resultsconfirm the effectiveness and efficiency of the proposed methodand its superiority over all other benchmarks.",
keywords = "Global-optimization, Heuristic-method, Parallel-Computing, Ridesharing, Simulated-Annealing",
author = "Lilhao Zhang and Zeyang Ye and Keli Xiao and Bo Jin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 19th IEEE International Conference on Data Mining, ICDM 2019 ; Conference date: 08-11-2019 Through 11-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICDM.2019.00101",
language = "English",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "906--915",
editor = "Jianyong Wang and Kyuseok Shim and Xindong Wu",
booktitle = "Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019",
}