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 collaborative variable neighborhood descent algorithm for the hybrid flowshop scheduling problem with consistent sublots
TL;DR: This paper introduces this issue into the hybrid flowshop scheduling problem with consistent sublots (HFSP_CS) and develops a collaborative variable neighborhood descent algorithm (CVND), which shows the most suitable performance in terms of the objective values and algorithm efficiency.
Minimizing Total Earliness and Tardiness Penalties with a Common Due Date on a Single-Machine Using a Discrete Particle Swarm Optimization Algorithm
TL;DR: In this article, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date.
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An Effective Subgradient Method for Scheduling a Steelmaking-Continuous Casting Process
TL;DR: An effective subgradient method for SCC scheduling based on the machine capacity relaxation with guarantees of convergence using the Brannlund's level control strategy and enhances the efficiency by introducing a deflected conditional subgradient to weaken the zigzagging phenomena that slows the convergence of conventional subgradient algorithms.
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A variable block insertion heuristic for solving permutation flow shop scheduling problem with makespan criterion
TL;DR: Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm.
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Local search-based metaheuristics for the robust distributed permutation flowshop problem
TL;DR: Two local-search based metaheuristics are presented to solve the Distributed Permutation Flowshop Scheduling Problem with uncertain processing times with makespan criterion, and they perform significantly better than the five competing algorithms adapted in the literature.