Cost Optimization Problem of Hybrid Flow-Shop Based on Differential Evolution Algorithm

Abstract:

A differential evolution algorithm based job scheduling method is presented, whose optimization target is production cost. The cost optimization model of hybrid flow-shop is thereby constructed through considering production cost as a factor in scheduling problem of hybrid flow-shop. In the implementation process of the method, DE is used to take global optimization and find which machine the jobs should be assigned on at each stage, which is also called the process route of the job; then the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. With converting time-based scheduling results to fitness function which is comprehensively considering the processing cost, waiting costs, and the products storage costs, the processing cost is taken as the optimization objective. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 1692-1700
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.1692
• Online since: January 2012
• Authors: Zhong Hua Han, Xiang Bin Meng, Bin Ma, Chang Tao Wang
• Keywords: Cost Optimization, Differential Evolution (DE), Hybrid Flow-Shop Scheduling Problem (HFSP)

References

[1] Z. Jia, L. Gong, The Study on Rate of Nonlinear Direct Cost Based on Neural Network, [C], 2009 International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, vol. 3, 2009, pp.342-345.

DOI: 10.1109/icmtma.2009.101

[2] Y. Shi, H. Li, N. Lu , Quality Reliability-Cost Optimization of Construction Project based on Genetic Algorithm, [C], International Conference on Engineering Computation, Hongkong, 2009, pp.159-162, doi: 10. 1109/ICEC. 2009. 32.

DOI: 10.1109/icec.2009.32

[3] J. Liu, D. Niu, Y. Zhang, Multi-Objective Optimization of Cost - Quality -Time- Security in Construction Project management Based on Particle Swarm Method, [C], 2009 Second International Workshop on Computer Science and Engineering, Qingdao, China, vol. 1, 2009, pp.425-429.

DOI: 10.1109/wcse.2009.702

[4] Y. Wu, M. Liu, Ch. Wu. A Genetic Algorithm for Solving Flow Shop Scheduling Problems with Parallel Machine and Special Procedure Constraints"[C], 2003 International Conference on Machine Learning and Cybernetics, Xi, an, China, Vol. 3, 2003, pp.1774-1779.

DOI: 10.1109/icmlc.2003.1259784

[5] W. Xiao, P. Hao, S. Zhang and X. Xu. Hybrid Flow Shop Scheduling Using Genetic Algorithms, [C], Proc. World Congr. Intelligent Control Autom. WCICA, Hefei, China, vol. 1, 2000, pp.537-541, doi: 10. 1109/WCICA. 2000. 860026.

DOI: 10.1109/wcica.2000.860026

[6] R. Storn and K. Price. Differential Evolution - A Simple and Efficient Heuristic Strategy for Global Optimization over Continuous Spaces, [J], Journal of Global Optimization, 1997, vol. 11, p.341–359.

DOI: 10.1023/a:1008202821328

[7] M. F. Tasgetiren, Q. Pan, P. N. Suganthan, and Y. Liang. A Discrete Differential Evolution Algorithm for the No-Wait, [C], Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling (CI-Sched 2007), Honolulu, United states, 2007, pp.251-258.

DOI: 10.1109/scis.2007.367698

[8] A. Ghasemi, et al, Unit commitment scheduling using binary differential evolution algorithm[J], OPSEARCH, 2009, 46(1): 108–122.

DOI: 10.1007/s12597-009-0007-0

[9] Z. Han, H. Shi, C. Liu, Differential Evolution Algorithm for the Earliness Tardiness Hybrid Flow-shop Scheduling Problem, [C], The 3rd International Conference on Intelligent Information Technology Application, Nanchang, China, 2009, pp: 188-193, doi: 10. 1109/IITA. 2009. 147 Figure 2. Gantt chart of Scheme1Scheduling Result Figure 3. Gantt chart of Scheme2 Scheduling Result Figure 4. Gantt chart of Scheme3 Scheduling Result.

DOI: 10.1109/iita.2009.147