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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Journal ArticleDOI
A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation
TL;DR: An effective discrete artificial bee colony (DABC) algorithm that has a hybrid representation and a combination of forward decoding and backward decoding methods for solving the HFS problem with the makespan criterion is presented.
Proceedings ArticleDOI
A discrete differential evolution algorithm for the permutation flowshop scheduling problem
TL;DR: In this paper, a novel discrete differential evolution (DDE) algorithm is presented to solve the permutation flowhop scheduling problem with the makespan criterion, which is simple in nature such that it first mutates a target population to produce the mutant population.
Journal ArticleDOI
An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling
TL;DR: This paper addresses a new steelmaking-continuous casting (SCC) scheduling problem from iron and steel production processing with a novel cooperative co-evolutionary artificial bee colony (CCABC) algorithm that has two sub-swarms, with each addressing a sub-problem.
Journal ArticleDOI
Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns
TL;DR: An evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions.
Journal ArticleDOI
An estimation of distribution algorithm for lot-streaming flow shop problems with setup times
Quan-Ke Pan,Rubén Ruiz +1 more
TL;DR: A novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation to solve an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases.