Research on Production Scheduling Optimization with Utilization Ratio of Equipments


Article Preview

An optimization of multi-varieties and small-batch of production scheduling is proposed, which is embodied the utilization ratio of equipment. First, the production scheduling model with multi-varieties and small-batch is improved by adding a new constraint. Second, the feeding behavior, clustering and rear collision of artificial fish algorithm are improved in order to describe the multi-varieties and small-batch of production scheduling. Finally, the optimizing results influenced by iteration times and quantity of artificial fish are analyzed. The experiments show that the utilization ratio of equipments are nearly same and the Man Hour is decreased obviously while the optimization method is used, which testifies the validity of the new optimization method.



Edited by:

Jun Zhang, Zhijian Wang, Shuren Zhu and Xiaoming Meng






F. Li et al., "Research on Production Scheduling Optimization with Utilization Ratio of Equipments", Applied Mechanics and Materials, Vols. 263-266, pp. 3177-3183, 2013

Online since:

December 2012




* - Corresponding Author

[1] Pang Qinghua, Pan Yu. Study on Flexible Scheduling in more variety and minor batch's production [J]. Jounal of Nanjing University of Technology, 2003, 25(2): 65-70.

[2] Hu Yaoguang, Cui Xiangmin et al, Study on PVC Gloves Produc-tion Scheduling Based on Genetic Algorithm, 2011 6th IEEE Con-ference on Industrial Electronics and Applications, 2011, 579-83.

DOI: 10.1109/iciea.2011.5975652

[3] Han Tiantian, Lan Yu, Fang Mingming. Process optimization design of multi-varieties and small batch[J]. Discovring Value, 2011(4): 121-123.

[4] Liu Ying, Song Guanghui, Xie Yurong, et al. Part/machine grouping method study for multi-variety&small-batch discrete manufacturing enterprise production workshop. Application Research of Computers, 2012, 29(2): 546-549.

[5] Li Xiaolei, Lu Fei, Tian Guohui, et al. Applications of artificial fish school algorithm in combinatorial optimization problems[J]. Journal of Shangdong University(Engineering Science), 2004, 34(5): 64-67.

[6] Zhu Minghao, She Xiangyang. Improved artificial fish school algorithm to solve traveling salesman problem[J]. Application Research of Computers, 2010, 27(10): 3734-3736.

[7] Wangliang. Intelligent algorithm of production scheduling with its application[M]. Scientific Press, Beijing, 2007, 7.

[8] Shi Feng. 30 Cases Analysis of Matlab Intelligent Algorithm[M]. Beihang University press, (2011).

[9] Jia Zhaohong, Chen Huaping, Sun Yao-hui. Multi-objective Particle Swarm optimization Algorithm for Flexible Job Shop Scheduling. Journal of Chinese Computer Systems, 2008, 28(5): 885-889.

DOI: 10.1109/gsis.2007.4443539

[10] Zhang Chaoyong, Liu Qiong, Qiu Haobo, et al. On Flexible Job-shop Scheduling Problem Considering Operation Cost and Time. Mechanical Science and Technology for Aerospace Engineering, 2009, 28(8): 1005-1010.

In order to see related information, you need to Login.