分享经济下的个性化产品生产资源配置优化
Optimization of Production Resource Allocation of Personalized Products under Sharing Economy
摘要: 分享经济作为新业态,为制造业高质量转型升级提供了新途径。分享制造中,生产资源配置优化是其核心问题。本文研究了分享经济下个性化产品生产资源配置优化问题。建立了生产资源配置总成本和产品交付期最小化的多目标优化模型。提出采用ε-约束法的多目标求解算法,求解分享经济下生产资源调度问题的帕累托边界(Pareto Front)。采用C++ Cplex编程实现了该多目标优化算法。最后,通过案例验证了该算法的有效性。数值实验表明,所提出的多目标优化算法能够在合理的时间内求解实际的分享制造中的生产资源调度问题。
Abstract: As a new form of business, sharing economy provides a new way for high-quality transformation and upgrading of manufacturing industry. In shared manufacturing, the allocation optimization of manufacturing resource sharing is the core problem. This paper studies the optimization of production resource allocation for personalized products in sharing economy. A multi-objective optimization model is established to minimize the total cost of production resource allocation and product delivery time; An ε-constrained multi-objective algorithm is proposed to solve the Pareto front of the production resource scheduling problem under the sharing economy. The multi-objec- tive optimization algorithm is implemented by C++ CPLEX programming. Finally, the effectiveness of the algorithm is verified by a case. Numerical experiments show that the proposed multi-objective optimization algorithm can solve the actual production resource scheduling problem in shared manufacturing in a reasonable time.
文章引用:黄真玉, 杨东. 分享经济下的个性化产品生产资源配置优化[J]. 管理科学与工程, 2021, 10(2): 135-145. https://doi.org/10.12677/MSE.2021.102018

参考文献

[1] Fang, B., Ye, Q. and Law, R. (2016) Effect of Sharing Economy on Tourism Industry Employment. Annals of Tourism Research, 57, 264-267.
[Google Scholar] [CrossRef
[2] 国家信息中心分享经济研究中心. 中国共享经济发展报告(2021) [R]. 2021.
[3] 丁明磊. “分享制造”将成为下一片“蓝海”[J]. 高科技与产业化, 2017(10): 16-19.
[4] 葛春艳. 共享经济下制造业发展存在的问题与对策[J]. 商场现代化, 2019(16): 114-115.
[5] 工业和信息部. 关于加快培育共享制造新模式促进制造业高质量发展的指导意见[Z]. 2019.
[6] Jiang, P. and Li, P. (2019) Shared Factory: A New Production Node for Social Manufacturing in the Context of Sharing Economy. Proceedings of the Institution of Mechanical Engineers, 234, 285-294.
[Google Scholar] [CrossRef
[7] Laili, Y., Lin, S. and Tang, D. (2020) Multi-Phase Integrated Scheduling of Hybrid Tasks in Cloud Manufacturing Environment. Robotics and Computer-Integrated Manufacturing, 61, Article ID: 101850.
[Google Scholar] [CrossRef
[8] 高琦. 共享经济对我国制造业发展的影响研究[J]. 现代经济信息, 2019(1): 382.
[9] Simeone, A., Caggiano, A. and Boun, L. (2019) Intelligent Cloud Manufacturing Platform for Efficient Resource Sharing in Smart Manufacturing Networks. Procedia CIRP, 79, 233-238.
[Google Scholar] [CrossRef
[10] 董沁茹, 芦莉莎, 严江武. 我国共享制造现有实践案例研究[J]. 商场现代化, 2019(8): 178-179.
[11] Li, P. and Jiang, P. (2021) Enhanced Agents in Shared Factory: Enabling High-Efficiency Self-Organization and Sustainability of the Shared Manufacturing Resources. Journal of Cleaner Production, 292, Article ID: 126020.
[Google Scholar] [CrossRef
[12] Ye, F., Ni, D. and Li, K.W. (2020) Competition between Manufacturers and Sharing Economy Platforms: An Owner Base and Sharing Utility Perspective. International Journal of Production Economics, 234, Article ID: 108022.
[Google Scholar] [CrossRef
[13] Zaidi, L., Sahnoun, M. and Bettayeb, B. (2019) Task Allocation Based on Shared Resource Constraint for Multi-Robot Systems in Manufacturing Industry. IFAC-PapersOnLine, 52, 2020-2025.
[Google Scholar] [CrossRef
[14] Kays, E., Karim, A., Varela, L., et al. (2018) Ranked Sequence Positional Weight Heuristic for Simultaneous Balancing and Scheduling Jobs in a Distributed Manufacturing Environment. Procedia CIRP, 67, 3-7.
[Google Scholar] [CrossRef
[15] Li, F.F., Xu, Z. and Li, H.T. (2021) A Multi-Agent Based Cooperative Approach to Decentralized Multi-Project Scheduling and Resource Allocation. Computers & Industrial Engineering, 151, Article ID: 106961.
[16] Liu, S.C., Zhang, L., Zhang, W.L., et al. (2021) Game Theory Based Multi-Task Scheduling of Decentralized 3D Printing Services in Cloud Manufacturing. Neurocomputing, 446, 74-85.
[Google Scholar] [CrossRef
[17] Liu, J.-L., Wang, L.-C. and Chu, P.-C. (2019) Development of a Cloud-Based Advanced Planning and Scheduling System for Automotive Parts Manufacturing Industry. Procedia Manufacturing, 38, 1532-1539.
[Google Scholar] [CrossRef
[18] Bo, Y.A., Sw, A., Qc, B., et al. (2021) Scheduling of Field Service Resources in Cloud Manufacturing Based on Multi-Population Competitive-Cooperative GWO. Computers & Industrial Engineering, 154, Article ID: 107104.
[19] Mavrotas, G. (2009) Effective Implementation of the ε-Constraint Method in Multi-Objective Mathematical Programming Problems. Applied Mathematics and Computation, 213, 455-465.
[Google Scholar] [CrossRef