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.
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Proceedings ArticleDOI
A discrete differential evolution algorithm for single machine total weighted tardiness problem with sequence dependent setup times
TL;DR: A discrete differential evolution algorithm with the reference local search is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times and newly designed speed-up methods are presented for the greedy job insertion into a partial solution.
Journal ArticleDOI
A Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem
TL;DR: A hybrid of particle swarm optimization (PSO) and tabu search (TS) algorithm are presented to solve the FJSP with the criterion to minimize the maximum completion time (makespan).
Journal ArticleDOI
An effective Lagrangian relaxation approach for rescheduling a steelmaking-continuous casting process
TL;DR: Wang et al. as mentioned in this paper developed a time index formulation and an effective Lagrangian relaxation (LR) approach with machine capacity relaxation to address the rescheduling problem, which decomposes the relaxed problem into batch-level subproblems with variable processing times.
Journal ArticleDOI
A hybrid local-search algorithm for robust job-shop scheduling under scenarios
TL;DR: An uncertain job-shop scheduling problem with the makespan as the performance criterion is discussed and the efficiency of the defined neighborhood structure and the competitiveness of the developed hybrid local-search algorithm for the established model are shown.
Journal ArticleDOI
A Variable Block Insertion Heuristic for the Blocking Flowshop Scheduling Problem with Total Flowtime Criterion
TL;DR: Extensive computational results on the Taillard’s well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm, which is one of the most efficient algorithms recently in the literature.