Scheduling of Discrete Manufacturing Process for Energy Saving

Article Preview

Abstract:

Rotor machining is a traditional discrete manufacturing process, among which large amount of non-essential energy is being wasted. The machining process belongs to pipeline production, so a flow-shop scheduling model is built to optimize it. But when there are over three machines, this will be an NP-hard problem. We introduce an improved ant-colony algorithm to find the best solution and then use the real machining data to test it. The total energy consumption is reduced by over 10% and this shows the model and intelligent algorithm work well.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

4248-4254

Citation:

Online since:

May 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Garey E L, Johnson D S, Sethi R: The complexity of flow-shop and job shop scheduling [J]. Mathematics of Operations Research, 1976(1): 117-129.

Google Scholar

[2] Ying Kuoching, Liao Chingjong: An ant colony system for permutation flow-shop problem [J]. Computers & Operation Research, 2004, 31: 791-801.

DOI: 10.1016/s0305-0548(03)00038-8

Google Scholar

[3] Phonsuwan, Seksan: Management system models to support decision-making for micro and small business of rural enterprise [J]. Procedia Engineering, 2010, 8: 498-503.

DOI: 10.1016/j.proeng.2011.03.090

Google Scholar

[4] Liu Q, Linge N. DEHEMS: The design and implementation of wide-scale domestic energy monitoring [J]. Environmental Energy and Structural Monitoring Systems, 2010, 23(42): 80-86.

DOI: 10.1109/eesms.2010.5634181

Google Scholar

[5] Ye Xu, Ling Wang: Differential evolution algorithm for hybrid flow-shop scheduling problems [J]. Journal of Syetems Engineering and Electronics, 2011, 22(5).

Google Scholar

[6] Colorni A, Doigo M: Heuristics from nature for hard combinatorial optimization problems [J]. International Trans Operational Research, 1996(3): 1-21.

DOI: 10.1111/j.1475-3995.1996.tb00032.x

Google Scholar

[7] Dorigo M, Maniezzo V: A colony Ant system: An Autocatalytic Optimizing Process. Technical Report, 1991, 16.

Google Scholar

[8] Ishii H, Tada M: Single machine scheduling problem with fuzzy precedence relation. European Journal of Operational Research, 1995, 87: 284-288.

DOI: 10.1016/0377-2217(94)00162-6

Google Scholar

[9] Macro D: Thomas stutzle ant colony optimization [J]. Cambridge: MIT Press, (2003).

Google Scholar

[10] DIRK C, MATTFELD ROB J M V. Flowshop-info. txt[EB/OL]. Http: /mscmga. ms. ic. ac. uk/ueb/orlib.

Google Scholar

[11] Rongwei Gan, Qingshun Guo, huiyou Chang, Yang Yi: Improved ant colony optimization for the traveling salesman problems [J]. Journal of Systems Engineering and Electronics. 2010, 21(2).

DOI: 10.3969/j.issn.1004-4132.2010.02.025

Google Scholar

[12] Guoxia Zou, Jianhua Liu, Jianqing Tang: The teaching of software engineering based on Gantt Schedule [J]. Journal of Guilin College of Aerospace Technology, 2010, 15(1).

Google Scholar

[13] Yijiang Zhu: Application of an Adaptive Ant Colony Optimization Algorithm to Flow Shop Scheduling Problem [J]. Joumal of Changzhou Institute of Technology, volume 20, (2007).

Google Scholar

[14] Qidi Wu, Fei Qiao, Li Li, Ying Wu: Data-based Scheduling for Complex Manufacturing Process [J]. ACTA AUTOMATICA SINCA, volume 35, (2009).

DOI: 10.3724/sp.j.1004.2009.00807

Google Scholar

[15] Min Liu: A Survey of Data-based Production Scheduling Methods [J]. ACTA AUTOMATICA SINCA, volume 35, (2009).

Google Scholar