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
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Journal ArticleDOI
A hybrid variable neighborhood search algorithm for solving multi-objective flexible job shop problems
TL;DR: A novel hybrid variable neighborhood search algorithm combining with the genetic algorithm (VNS+GA) for solving the multi-objective flexible job shop scheduling problems (FJSPs) to minimize the makespan, the total workload of all machines, and the workload of the busiest machine is proposed.
Journal ArticleDOI
Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm
TL;DR: Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder in the distributed environment.
Book ChapterDOI
A Discrete Particle Swarm Optimization Algorithm for the Permutation Flowshop Sequencing Problem with Makespan Criterion
TL;DR: In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the permutation flowshop sequencing problem with the makespan criterion and a new crossover operator, here the authors call it the PTL crossover operator.
Proceedings ArticleDOI
A genetic algorithm for the generalized traveling salesman problem
TL;DR: The proposed genetic algorithm is hybridized with an iterated local search to further improve the solution quality and is shown to be the best performing algorithm so far in the literature in terms of solution quality.
Proceedings ArticleDOI
A Discrete Particle Swarm Optimization Algorithm for Single Machine Total Earliness and Tardiness Problem with a Common Due Date
TL;DR: A modified version of HRM heuristic presented by Hino et al. in [7], here the authors call it M_HRM, is presented to solve the single machine total earliness and tardiness penalties with a common due date and it has better results than the existing approaches in the literature.