Cloud Based Evolution Algorithm with Feedback Control for Emergency Logistic

Abstract:

This paper develops a parallel and distributed evolutionary algorithm based on the cloud computing environment and feedback control algorithm to help planners solve the emergency logistic problems. The cloud environment is emulated and used as various virtual machines with different types of evolution procedures. To yield both exploration and exploitation, two crossover processes are deployed on different virtual machines. In the process of crossover, local optimal solutions can be competed and evaluated to form new populations so that the search space can be expanded and the advantaged crossover procedures can be further adopted. The proposed feedback control algorithm based on the evaluation of evolution algorithm can interact and emphasize the process with better performance. According to proposed feedback algorithm, virtual machines with different on demand formatted crossover algorithms can be dynamically established and adopted. Taking the advantage of cloud computing environment and the proposed feedback control algorithm of evolution algorithm, planners can take less effort on deploying both computation power and storage space. Also, it can further applied in various complicated applications more practically.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3370-3374
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3370
• Online since: January 2013
• Authors: Ming Shen Jian, Fu Jie Jhan, Han Chun Chang, Ta Yuan Chou, Jun Hong Shen
• Keywords: Cloud, Emergency Logistic, Evolution Algorithm, Feedback Control

References

[1] B. Eksioglu, A. V. Vural and A. Reisman, The vehicle routing problem: A taxonomic review, Comput. Ind. Eng. 57 (2009) 1472-1483.

DOI: 10.1016/j.cie.2009.05.009

[2] B. L. Golden and A. Assad, Vehicle Routing: Methods and Studies. Amsterdam; New York; New York, N.Y., U.S.A.: North-Holland; Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co., (1979).

[3] P. Toth and D. Vigo, The Vehicle Routing Problem. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, (2001).

[4] J. Sheu, An emergency logistics distribution approach for quick response to urgent relief demand in disasters, Transportation Research Part E: Logistics and Transportation Review 43 (2007) 687-709.

DOI: 10.1016/j.tre.2006.04.004

[5] L. Özdamar, E. Ekinci, and B. Küçükyazici, Emergency logistics planning in natural disasters, Annals of Operations Research 129 (2004) 217-245.

DOI: 10.1023/b:anor.0000030690.27939.39

[6] J. Castro, Solving difficult multicommodity problems with a specialized interior-point algorithm, Annals of Operations Research 124 (2003) 35-48.

DOI: 10.1023/b:anor.0000004761.99649.a5

[7] Y. Chiu and H. Zheng, Real-time mobilization decisions for multi-priority emergency response resources and evacuation groups: Model formulation and solution, Transportation Research Part E: Logistics and Transportation Review 43 (2007) 710-736.

DOI: 10.1016/j.tre.2006.11.006

[8] A. Haghani and S. Oh, Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations, Transportation Research Part A: Policy and Practice 30 (1996) 231-250.

DOI: 10.1016/0965-8564(95)00020-8

[9] S. Yan and Y. Shih, Optimal scheduling of emergency roadway repair and subsequent relief distribution, Comput. Oper. Res. 36 (2009) 2049-(2065).

DOI: 10.1016/j.cor.2008.07.002

[10] W. Yi and L. Özdamar, A dynamic logistics coordination model for evacuation and support in disaster response activities, Eur. J. Oper. Res. 79 (2007) 1177-1193.

DOI: 10.1016/j.ejor.2005.03.077

[11] Y. Yuan and D. Wang, Path selection model and algorithm for emergency logistics management, Comput. Ind. Eng. 56 (2009) 1081-1094.

[12] K. G. Zografos, K. N. Androutsopoulos and G. M. Vasilakis, A real-time decision support system for roadway network incident response logistics, Transportation Research Part C: Emerging Technologies 10 (2002) 1-18.

DOI: 10.1016/s0968-090x(00)00043-7

[13] H. Kanoh and K. Hara, Hybrid genetic algorithm for dynamic multi-objective route planning with predicted traffic in a real-world road network, Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (2008) 657-664.

DOI: 10.1145/1389095.1389226

[14] H. M. Fan, T. Zhao, X. Y. Zhao, M. B. Jang, and G. S. Dong, Research on emergency relief goods distribution after regional natural disaster occurring, Proceediongs of Information Management, Innovation Management and Industrial Engineering 3 (2008).

DOI: 10.1109/iciii.2008.291

[15] J. Z. Peng, W. S. Xu, and J. J. Yang, A hybrid heuristic algorithm for large scale emergency logistics, Proceedings of Intelligent Computation Technology and Automation 3 (2009) 899-902.

[16] L. Yang, B. F. Jones, and S. Yang, A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms, Eur. J. Oper. Res. 181 (2007) 903-915.

DOI: 10.1016/j.ejor.2006.07.003

[17] W. Yi and A. Kumar, Ant colony optimization for disaster relief operations, Transportation Research Part E: Logistics and Transportation Review 43 (2007) 660-672.

DOI: 10.1016/j.tre.2006.05.004

[18] Z. Hu, A Network for emergency logistics management inspired by immune multi-affinity model, Proceedings of International Coonference on Information Management, Innovation Management and Industrial Engineering 4 (2009) 22-25.

DOI: 10.1109/iciii.2009.467

[19] L. Ozdamar and W. Yi, Greedy neighborhood search for disaster relief and evacuation logistics, Intelligent Systems, IEEE 23 (2008) 14-23.

DOI: 10.1109/mis.2008.7

[20] P. M. Reyes, Logistics networks: a game theory application for solving the transshipment problem, Applied Mathematics and Computation 168 (2005) 1419-1431.

DOI: 10.1016/j.amc.2004.10.030

[21] G. Tzeng, H. Cheng, and T. D. Huang, Multi-objective optimal planning for designing relief delivery systems, Transportation Research Part E: Logistics and Transportation Review, (2007) vol. 43, pp.673-686.

DOI: 10.1016/j.tre.2006.10.012

[22] J. Sheu, Dynamic relief-demand management for emergency logistics operations under large-scale disasters, Transportation Research Part E: Logistics and Transportation Review 46 (2010) 1-17.

DOI: 10.1016/j.tre.2009.07.005

[23] J. H. Christensen, Using RESTful Web-Services and Cloud Computing to Create Next Generation Mobile Applications, OOPSLA (2009) 627-633.

DOI: 10.1145/1639950.1639958