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Dai Z, Zhang Z, Chen M. Collaborative task scheduling with new task arrival in cloud manufacturing using improved multi-population biogeography-based optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms.
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Affiliation(s)
- Ziwei Dai
- Department of Electronic Business, South China University of Technology, Guangzhou, China
| | - Zhiyong Zhang
- Department of Electronic Business, South China University of Technology, Guangzhou, China
| | - Mingzhou Chen
- School of Economics and Management, Tongji University, Shanghai, China
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2
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King GS, Rameshwar JR, Syan CS. Industry 4.0 in a Small Commodity-Based Economy: A Vehicle for Stimulating Innovation. JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT 2020. [DOI: 10.1142/s242486222050013x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Innovation is fundamental for companies to exploit changes in their environment and maintain a competitive advantage. Access to information and the ability to quickly utilise available resources are key enablers for in-company innovation. Both of these are elements associated with Industry 4.0, which can now be identified as a facilitator of innovation. The Industry 4.0 framework integrates evolutionary advances in manufacturing, production technologies, information technology, Internet of Things (IoT), distributed processing power, and visualization tools, which facilitate organizations to more effectively create enhanced value propositions for their customers, encouraging innovation in processes and products. Three local firms are analyzed, and opportunities for innovation are clearly identified in the process of transitioning to Industry 4.0 through reconfigured value chains; the creation of a cluster to facilitate the development of innovative cloud-based solutions; and product configurator and an augmented reality interactive display.
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Affiliation(s)
- Graham S. King
- University of West Indies, St. Augustine, Trinidad and Tobago, USA
| | | | - Chanan S. Syan
- University of West Indies, St. Augustine, Trinidad and Tobago, USA
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3
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Xia T, Zhang W, Chiu W, Jing C. Using cloud computing integrated architecture to improve delivery committed rate in smart manufacturing. ENTERP INF SYST-UK 2020. [DOI: 10.1080/17517575.2019.1701715] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ting Xia
- College of Opto-electronic Engineering, Zaozhuang University, Zaozhuang, China
| | - Wei Zhang
- Academy of Broadcasting Science, National Radio and Television Administration, Beijing, China
| | - W.S. Chiu
- Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan
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5
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Ding J, Wang Y, Zhang S, Zhang W, Xiong Z. Robust and stable multi-task manufacturing scheduling with uncertainties using a two-stage extended genetic algorithm. ENTERP INF SYST-UK 2019. [DOI: 10.1080/17517575.2019.1656290] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Jiepin Ding
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Yan Wang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Shuai Zhang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Wenyu Zhang
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Zhiying Xiong
- School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
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Jafarnejad Ghomi E, Masoud Rahmani A, Nasih Qader N. Service load balancing, task scheduling and transportation optimisation in cloud manufacturing by applying queuing system. ENTERP INF SYST-UK 2019. [DOI: 10.1080/17517575.2019.1599448] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
| | | | - Nooruldeen Nasih Qader
- Department of Computer Science, University of Human Development, Sulaymaniyah, Iraq
- Computer Science Department, University of Sulaimaniyah
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Leng J, Jiang P, Liu C, Wang C. Contextual self-organizing of manufacturing process for mass individualization: a cyber-physical-social system approach. ENTERP INF SYST-UK 2018. [DOI: 10.1080/17517575.2018.1470259] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Jiewu Leng
- Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System, State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, Guangdong, China
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Pingyu Jiang
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Chao Liu
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Chuang Wang
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
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He Z, Guo Z, Wang J. Integrated scheduling of production and distribution operations in a global MTO supply chain. ENTERP INF SYST-UK 2018. [DOI: 10.1080/17517575.2018.1428770] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Zhenggang He
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, PR China
| | - Zhaoxia Guo
- Business School, Sichuan University, Chengdu, PR China
| | - Junwei Wang
- Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, PR China
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9
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Liu Y, Wang L, Vincent Wang X. Cloud manufacturing: latest advancements and future trends. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.promfg.2018.06.058] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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10
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Zhou L, Zhang L, Ren L. Modelling and simulation of logistics service selection in cloud manufacturing. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.procir.2018.03.197] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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