Cheng Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Cheng Zhang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Duc Truong Pham
University of Birmingham
1 shared publication - Gang Yuan
School of Mechanical Engineering, Southeast University, Nanjing 211102, China
1 shared publication - Guangdong Tian
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
1 shared publication - Tao Peng
State Key Laboratory of Fluid Power Components and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
1 shared publication Xuesong Zhang
Northeast Forestry University Transportation College Harbin 150000 , China
1 shared publication- Yixiong Feng
Guizhou University
1 shared publication
Latest Publications
- 19 January 2023
- Published by ASME International in Journal of Manufacturing Science and Engineering
- Vol. 145 (5) , 1-36
Multi-Objective Evolutionary Algorithm With Machine Learning and Local Search for an Energy-Efficient Disassembly Line Balancing Problem in Remanufacturing
Product disassembly is a vital element of recycling and remanufacturing processes. The disassembly line balancing problem (DLBP), i.e., how to assign a set of tasks to a disassembly workstation, is crucial for a product disassembly process. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this study proposes an optimization model for a multi-objective disassembly line balancing problem that aims to minimize the idle rate, smoothness, cost, and energy consumption during the disassembly operation. Due to the complex nature of the optimization problem, a discrete whale optimization algorithm is proposed in this study, which is developed as an extension of the whale optimization algorithm. To enable the algorithm to solve discrete optimization problems, we propose coding and decoding methods that combine the features of DLBP. First of all, the initial disassembly solution is obtained by using K-means clustering to speed up the exchange of individual information. After that, new methods for updating disassembly sequences are developed, in which a local search strategy is introduced to increase the accuracy of the algorithm. Finally, the algorithm is used to solve the disassembly problem of a worm reducer and the first 12 feasible task allocation options in the Pareto frontier are shown. A comparison with typically existing algorithms confirms the high performance of the proposed whale optimization algorithm, which has a good balance of solution quality and efficiency.
- 19 January 2023
- Published by ASME International in Journal of Manufacturing Science and Engineering
- Vol. 145 (5) , 1-36
Multi-Objective Evolutionary Algorithm With Machine Learning and Local Search for an Energy-Efficient Disassembly Line Balancing Problem in Remanufacturing
Product disassembly is a vital element of recycling and remanufacturing processes. The disassembly line balancing problem (DLBP), i.e., how to assign a set of tasks to a disassembly workstation, is crucial for a product disassembly process. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this study proposes an optimization model for a multi-objective disassembly line balancing problem that aims to minimize the idle rate, smoothness, cost, and energy consumption during the disassembly operation. Due to the complex nature of the optimization problem, a discrete whale optimization algorithm is proposed in this study, which is developed as an extension of the whale optimization algorithm. To enable the algorithm to solve discrete optimization problems, we propose coding and decoding methods that combine the features of DLBP. First of all, the initial disassembly solution is obtained by using K-means clustering to speed up the exchange of individual information. After that, new methods for updating disassembly sequences are developed, in which a local search strategy is introduced to increase the accuracy of the algorithm. Finally, the algorithm is used to solve the disassembly problem of a worm reducer and the first 12 feasible task allocation options in the Pareto frontier are shown. A comparison with typically existing algorithms confirms the high performance of the proposed whale optimization algorithm, which has a good balance of solution quality and efficiency.