The Inverse Optimal Allocation Model of Manufacturing Resource for Small and Medium-Sized Manufacturing Enterprises in Grid Environment

Abstract:

Almost each product is produced by more than one enterprise, so resource sharing and cooperation become more and more important. Small and medium-size manufacturing enterprises sometimes have not enough resources to complete the orders and they have to look for cooperators to meet the market demands. Inverse optimization model of linear programming is suitable for solving resource optimal allocation for small and medium-sized manufacturing enterprises in dynamic grid. This paper establishes an inverse optimization model of a linear programming for grid resources management. To meet market demand, conditional on optimal production and product portfolios, this model adjusts the parameters in resources optimal allocation, and reverses the process of optimization. This inverse optimal model helps the enterprise node to adjust its structure and the number of resources allocation to better meet market demand and maximize the interests according to its budget.

• Periodical: Applied Mechanics and Materials (Volume 273)
• Pages: 22-27
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.273.22
• Online since: January 2013
• Authors: Ping Lin, Xiang Bin Zhang
• Keywords: Grid, Inverse Optimization Model, Manufacturing Resource, Optimal Allocation

References

[1] Rajkumar Buyya, David Abramson, Jonathan Giddy, Heinz Stockinger(2002)Economic models for resource management and scheduling in Grid computing[J]. Journal of Concurrency: Practice and Experience, Grid computing special issue 14/13-15: 1507 - 1542.

DOI: 10.1002/cpe.690

[2] Rajkumar Buyya, Manzur Murshed, GridSim. 2002. A toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing[J], Concurrency and Computation: Practice and Experience, 14: 1175-1220.

DOI: 10.1002/cpe.710

[3] Rajkumar Buyya, Manzur Murshed, David Abramson, Srikumar Venugopal(2005).

[4] Liu Lilan , Yu Tao , SHI Zhan bei, FAN Ming lun(2004) Self-organization Manufacturing Grid and Its Task Scheduling Algorithm [J]. Computer Integrated Manufacturing Systems. 6: 449-455.

[5] Liu Lilan, Yu Tao, He Yu'an(2005) A QoS-based GFAM Scheduling Approach for Manufacturing Grid Proceedings of the 2005 The Fifth International Conference on Computer and Information Technology.

DOI: 10.1109/cit.2005.40

[6] MA Xue - f en , DA I Xu - dong , SUN S hu - dong, (2004. ) Optimization Deployment of Networked Manufacturing Resources [J] Computer Integrated Manufacturing Systems, 5: 523-527.

[7] Raafat Aburukba, Hamada Ghenniwa, Weiming Shen(2007)Agent-Based Dynamic Scheduling Approach for Collaborative Manufacturing, Proceedings of the 2007 11th International Conference on Computer Supported Cooperative Work in Design.

DOI: 10.1109/cscwd.2007.4281477

[8] LIU Hai- xia, LI Ren- wang, LI Xue, et al. (2008) Resource scheduling strategy in manufacture grid based on genetic algorithms and simulated annealing[J]. Computer Engineering and Applications, 44( 6) : 234- 236.

[9] ZHOU Changchun, YIN Guofu, WU Yingchun, ZHANG Li, OU Yanjiang(2009) Study on the Tactics of Manufacturing Resource Optima l Allocation Oriented to Manufacturing Grid [J]. Journal of Sichuan University (Engineering science edition) , 3: 237-244.

[10] D. Burton, Ph.L. Toint(1992)On an instance of the inverse shortest paths problem[J]. Mathematical Programming , Volume 53, Numbers 1-3, 45-61, DOI: 10. 1007/BF01585693.

DOI: 10.1007/bf01585693

[11] J. Zhang, Z. Ma, (1996) A network flow method for solving some inverse combinatorial optimization problems[J]. Optimization, 37: 59—72.

DOI: 10.1080/02331939608844197

[12] J. Zhang, Z. Liu. (1996) Calculating some inverse linear programming problem[J]. Comput. Appl. Math, 72: 261—273.

[13] Michael Ringor, Lech Papiez(1998) Inverse planning and optimization: a comparison of solutions[J]. Radiation Physics and Chemistry, Volume 53, Issue 3 : 263-274.

DOI: 10.1016/s0969-806x(98)00108-x