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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A multi-objective hot-rolling scheduling problem in the compact strip production
TL;DR: A multi-objective evolutionary algorithm is developed to find the Pareto optimal or near-optimal solutions for the sheet-strip sequencing problem and the results demonstrate the effectiveness of the proposed algorithms for solving the hot-rolling scheduling problem under consideration.
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An energy-efficient permutation flowshop scheduling problem
Hande Öztop,M. Fatih Tasgetiren,Deniz Türsel Eliiyi,Quan-Ke Pan,Quan-Ke Pan,Levent Kandiller +5 more
TL;DR: In this paper, a bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework to address the conflicting objectives of minimizing total flowtime and total energy consumption.
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A Greedy Cooperative Co-evolution ary Algorithm with Problem-specific Knowledge for Multi-objective Flowshop Group Scheduling Problems
TL;DR: A large number of experimental results show that the proposed algorithm significantly outperforms the existing classic multi-objective optimization algorithms, which is due to the usage of problem-related knowledge.
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Effective hybrid discrete artificial bee colony algorithms for the total flowtime minimization in the blocking flowshop problem
TL;DR: In this article, three effective hybrid discrete artificial bee colony (hDABC1, hDABC2, HDABC3) algorithms are presented to solve the blocking flow shop scheduling problem with the objective of minimizing the total flowtime.
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A hybrid artificial bee colony for optimizing a reverse logistics network system
TL;DR: A hybrid discrete artificial bee colony (HDABC) algorithm for solving the location allocation problem in reverse logistics network system and an enhanced local search procedure is developed to further improve the search capability.