qiuhua tang
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A comparative theoretical and computational study on robust counterpart optimization: II. Probabilistic guarantees on constraint satisfaction
Z Li, Q Tang, CA Floudas
Industrial & engineering chemistry research 51 (19), 6769-6788, 2012
Production scheduling of a large-scale steelmaking continuous casting process via unit-specific event-based continuous-time models: Short-term and medium-term scheduling
J Li, X Xiao, Q Tang, CA Floudas
Industrial & Engineering Chemistry Research 51 (21), 7300-7319, 2012
Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm
Z Li, Q Tang, LP Zhang
Journal of Cleaner Production 135, 508-522, 2016
An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II
Q Tang, Z Li, L Zhang
Computers & Industrial Engineering 97, 146-156, 2016
Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty
Y Ye, J Li, Z Li, Q Tang, X Xiao, CA Floudas
Computers & chemical engineering 66, 165-185, 2014
Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm
Q Tang, Z Li, LP Zhang, C Zhang
Computers & Operations Research 82, 102-113, 2017
Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem
Z Li, MN Janardhanan, Q Tang, P Nielsen
Advances in Mechanical Engineering 8 (9), 1687814016667907, 2016
Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem
Z Li, N Dey, AS Ashour, Q Tang
Neural Computing and Applications 30 (9), 2685-2696, 2018
Multi-objective co-operative co-evolutionary algorithm for minimizing carbon footprint and maximizing line efficiency in robotic assembly line systems
JM Nilakantan, Z Li, Q Tang, P Nielsen
Journal of Cleaner Production 156, 124-136, 2017
Two-sided assembly line balancing problem of type I: Improvements, a simple algorithm and a comprehensive study
Z Li, Q Tang, LP Zhang
Computers & Operations Research 79, 78-93, 2017
Station ant colony optimization for the type 2 assembly line balancing problem
Q Zheng, M Li, Y Li, Q Tang
The International Journal of Advanced Manufacturing Technology 66 (9-12 …, 2013
Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops
L Zhang, Q Tang, Z Wu, F Wang
Energy 138, 210-227, 2017
Rules-based heuristic approach for the U-shaped assembly line balancing problem
M Li, Q Tang, Q Zheng, X Xia, CA Floudas
Applied Mathematical Modelling 48, 423-439, 2017
Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line
Z Li, MN Janardhanan, Q Tang, P Nielsen
Engineering Optimization 50 (5), 877-893, 2018
Minimizing the cycle time in two-sided assembly lines with assignment restrictions: improvements and a simple algorithm
Z Li, Q Tang, L Zhang
Mathematical Problems in Engineering 2016, 2016
Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints
Q Tang, Z Li, L Zhang, CA Floudas, X Cao
Chinese Journal of Mechanical Engineering 28 (5), 1067-1079, 2015
Optimization framework for process scheduling of operation-dependent automobile assembly lines
Q Tang, J Li, CA Floudas, M Deng, Y Yan, Z Xi, P Chen, J Kong
Optimization Letters 6 (4), 797-824, 2012
Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment
Z Zhang, Q Tang, D Han, Z Li
Neural Computing and Applications 31 (11), 7501-7515, 2019
New MILP model and station-oriented ant colony optimization algorithm for balancing U-type assembly lines
Z Li, I Kucukkoc, Q Tang
Computers & Industrial Engineering 112, 107-121, 2017
More MILP models for integrated process planning and scheduling
L Jin, Q Tang, C Zhang, X Shao, G Tian
International Journal of Production Research 54 (14), 4387-4402, 2016
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