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Chunming Ye
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

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Journal article
Published: 11 March 2020 in Applied Sciences
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The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.

ACS Style

Hui Li; Po-Chou Shih; Xizhao Zhou; Chunming Ye; Li Huang. An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance. Applied Sciences 2020, 10, 1910 .

AMA Style

Hui Li, Po-Chou Shih, Xizhao Zhou, Chunming Ye, Li Huang. An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance. Applied Sciences. 2020; 10 (6):1910.

Chicago/Turabian Style

Hui Li; Po-Chou Shih; Xizhao Zhou; Chunming Ye; Li Huang. 2020. "An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance." Applied Sciences 10, no. 6: 1910.

Journal article
Published: 18 July 2019 in Applied Sciences
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Production scheduling of semiconductor wafer manufacturing is a challenging research topic in the field of industrial engineering. Based on this, the green manufacturing collaborative optimization problem of the semiconductor wafer distributed heterogeneous factory is first proposed, which is also a typical NP-hard problem with practical application value and significance. To solve this problem, it is very important to find an effective algorithm for rational allocation of jobs among various factories and the production scheduling of allocated jobs within each factory, so as to realize the collaborative optimization of the manufacturing process. In this paper, a scheduling model for green manufacturing collaborative optimization of the semiconductor wafer distributed heterogeneous factory is constructed. By designing a new learning strategy of initial population and leadership level, designing a new search strategy of the predatory behavior for the grey wolf algorithm, which is a new swarm intelligence optimization algorithm proposed in recent years, the diversity of the population is expanded and the local optimum of the algorithm is avoided. In the experimental stage, two factories’ and three factories’ test cases are generated, respectively. The effectiveness and feasibility of the algorithm proposed in this paper are verified through the comparative study with the improved Grey Wolf Algorithms—MODGWO, MOGWO, the fast and elitist multi-objective genetic algorithm—NSGA-II.

ACS Style

Jun Dong; Chunming Ye. Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory. Applied Sciences 2019, 9, 2879 .

AMA Style

Jun Dong, Chunming Ye. Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory. Applied Sciences. 2019; 9 (14):2879.

Chicago/Turabian Style

Jun Dong; Chunming Ye. 2019. "Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory." Applied Sciences 9, no. 14: 2879.

Journal article
Published: 21 October 2016 in Sustainability
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Green innovation has been deemed a key corporate capability to deal with environmental issues. The usage of advanced manufacturing technologies (AMT) provides important resources and knowledge for firms’ green innovation. Drawing on a resources-based approach, this study contributes to the existing literature by examining how the adoption of specific types of AMT (process, design, and planning) influences two dimensions of green innovation (green product innovation and green process innovation). In particular, we explore these relationships through internal environmental collaboration. Based on data collected from 198 Chinese manufacturing firms, we found that process, design, and planning AMT can contribute to both green products and process innovation. Moreover, the findings confirm the significant mediating role of internal environmental collaboration in this relationship. Specifically, internal environmental collaboration mediates the relationship between process AMT and green product innovation as well as the relationship between design AMT and two dimensions of green innovation; it also partially mediates the relationship between process AMT and green process innovation as well as the relationship between planning AMT and two dimensions of green innovation. These findings provide novel insights into how manufacturing firms can use various types of AMT to enhance their green innovation.

ACS Style

Ting Kong; Taiwen Feng; Chunming Ye. Advanced Manufacturing Technologies and Green Innovation: The Role of Internal Environmental Collaboration. Sustainability 2016, 8, 1056 .

AMA Style

Ting Kong, Taiwen Feng, Chunming Ye. Advanced Manufacturing Technologies and Green Innovation: The Role of Internal Environmental Collaboration. Sustainability. 2016; 8 (10):1056.

Chicago/Turabian Style

Ting Kong; Taiwen Feng; Chunming Ye. 2016. "Advanced Manufacturing Technologies and Green Innovation: The Role of Internal Environmental Collaboration." Sustainability 8, no. 10: 1056.