L
Lining Xing
Researcher at National University of Defense Technology
Publications - 120
Citations - 2097
Lining Xing is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Computer science & Job shop scheduling. The author has an hindex of 18, co-authored 73 publications receiving 1358 citations. Previous affiliations of Lining Xing include Shanghai Second Polytechnic University & Foshan University.
Papers
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Journal ArticleDOI
A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems
TL;DR: A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP) and results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.
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Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns
TL;DR: A multi-objective evolutionary algorithm to address robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns and results indicate that the first suggested surrogate measure performs better for small cases, while the second surrogate measure performing better for both small and relatively large cases.
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Behavior of crossover operators in NSGA-III for large-scale optimization problems
Jiao-Hong Yi,Jiao-Hong Yi,Lining Xing,Gai-Ge Wang,Junyu Dong,Athanasios V. Vasilakos,Amir H. Alavi,Ling Wang +7 more
TL;DR: Enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI, and experimental results indicate that the NS GA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
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Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling
TL;DR: A simulation model is presented to solve the multi-objective flexible job shop scheduling problem using Matlab, a special mathematical computation language, and the results obtained have shown that the proposed approach is a feasible and effective approach.
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An efficient search method for multi-objective flexible job shop scheduling problems
TL;DR: The final experimental results have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.