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A dynamic scheduling mechanism of part feeding for mixed-model assembly lines based on the modified neural network and knowledge base. Soft comput 2020. [DOI: 10.1007/s00500-020-05141-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems. SUSTAINABILITY 2019. [DOI: 10.3390/su11051447] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With the development of intelligent manufacturing technology, the material handling system (MHS) faces larger resilience challenges that threaten the sustainability of the system. To evaluate system resilience, the disturbance that the system may experience and the system response need to be identified in advance. This paper proposes a systematic and innovative approach to performing resilience-related disturbance analysis, i.e., disturbance mode and effects analysis (DMEA). Using this method, the possible disturbance modes, their occurrence probabilities, and the quantitative effects on system performance can be collected in a bottom-up process, and the information can be applied to further resilience quantification. Moreover, a quantitative system resilience evaluation framework for the MHS based on DMEA and the Monte Carlo method is presented. Production is defined as the key performance index of the system and is monitored to reflect the resilience behavior of the system after the disturbance occurs. The resilience of a tire tread handing system is quantified in our case study, and the results show the effectiveness of our DMEA-based resilience evaluation method. We also find that a reasonable system configuration and maintenance strategy can effectively improve system resilience, and a trade-off can be made between resilience and cost.
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Wang J, Zheng P, Qin W, Li T, Zhang J. A novel resilient scheduling paradigm integrating operation and design for manufacturing systems with uncertainties. ENTERP INF SYST-UK 2018. [DOI: 10.1080/17517575.2018.1526322] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Junliang Wang
- College of Mechanical Engineering, Donghua University, Shanghai, China
| | - Peng Zheng
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Qin
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Tengda Li
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Zhang
- College of Mechanical Engineering, Donghua University, Shanghai, China
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Qin W, Zhang J, Sun Y. Multiple-objective scheduling for interbay AMHS by using genetic-programming-based composite dispatching rules generator. COMPUT IND 2013. [DOI: 10.1016/j.compind.2013.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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