Q
Quan-Ke Pan
Researcher at Shanghai University
Publications - 304
Citations - 15638
Quan-Ke Pan is an academic researcher from Shanghai University. The author has contributed to research in topics: Job shop scheduling & Local search (optimization). The author has an hindex of 62, co-authored 281 publications receiving 12128 citations. Previous affiliations of Quan-Ke Pan include Liaocheng University & Huazhong University of Science and Technology.
Papers
More filters
Journal ArticleDOI
Clustering and multi-hop routing with power control in wireless sensor networks
TL;DR: In this paper, a clustering and multihop routing protocol (CMRP) is proposed, where each node independently makes its decision to compete for becoming a cluster head or join a cluster according to its residual energy and average broadcast power of all its neighbors.
Journal ArticleDOI
Protocol for the application of cooperative MIMO based on clustering in sparse wireless sensor networks
TL;DR: A novel clustering scheme is proposed for the application of cooperative MIMO in sparse WSN by extending the traditional low-energy adaptive clustering hierarchy (LEACH) protocol, which solves the problem that the cluster heads cannot find enough secondary cluster heads (SCH).
Journal ArticleDOI
A light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times
TL;DR: A novel light-robust-optimization model is proposed by integrating the goal programming formulations with set-based descriptions of the problem data, which can enable as many customers as possible to meet their demands within a group of predetermined time windows.
Journal ArticleDOI
A Variable Iterated Local Search Algorithm for Energy-Efficient No-idle Flowshop Scheduling Problem
TL;DR: A novel mixed-integer linear programming model is proposed for the No-idle permutation flowshop scheduling problem and the results show that the proposed algorithms are very effective for the EE-NIPFSP in terms of solution quality.
Proceedings ArticleDOI
A novel simplification method of point cloud with directed Hausdorff distance
TL;DR: Experimental results demonstrate that the proposed method is effective for point cloud simplification, and it exhibited superior performance compared to existing techniques.