W
Wenming Cheng
Researcher at Southwest Jiaotong University
Publications - 86
Citations - 737
Wenming Cheng is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Gantry crane & Heuristic (computer science). The author has an hindex of 14, co-authored 81 publications receiving 551 citations.
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Proceedings ArticleDOI
Global artificial bee colony search algorithm for numerical function optimization
TL;DR: In this paper, a novel search strategy of main three procedures of the ABC algorithm is presented, and the results demonstrate that the proposed algorithm is more effective than other population based optimization algorithms.
Journal ArticleDOI
Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems
Peng Guo,Wenming Cheng,Yi Wang +2 more
TL;DR: By employing the proposed algorithm, facilities can be positioned more efficiently, which means the fixed cost and the transportation cost can be decreased significantly, and organizations can enhance competitiveness by using the optimized facility location scheme.
Journal ArticleDOI
Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm
Peng Guo,Wenming Cheng,Yi Wang +2 more
TL;DR: In this article, a hybrid discrete cuckoo search algorithm is proposed to solve the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times.
Journal ArticleDOI
Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs
TL;DR: A mixed integer programming model and a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule for scheduling with deteriorating jobs to many industrial applications.
Posted Content
Parallel machine scheduling with step deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm
Peng Guo,Wenming Cheng,Yi Wang +2 more
TL;DR: The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.