0
Research Papers

A Cooperative Co-Evolutionary Algorithm for Large-Scale Process Planning With Energy Consideration

[+] Author and Article Information
Fei Tao

School of Automation Science and
Electrical Engineering,
Beihang University,
Haidian District,
Beijing 100191, China
e-mail: ftao@buaa.edu.cn

Luning Bi, Ying Zuo

School of Automation Science and
Electrical Engineering,
Beihang University,
Beijing 100191, China

A. Y. C. Nee

Department of Mechanical Engineering,
National University of Singapore,
Singapore 117576, Singapore

1Corresponding author.

Manuscript received March 3, 2016; final manuscript received December 23, 2016; published online March 3, 2017. Assoc. Editor: Jianjun Shi.

J. Manuf. Sci. Eng 139(6), 061016 (Mar 03, 2017) (11 pages) Paper No: MANU-16-1135; doi: 10.1115/1.4035960 History: Received March 03, 2016; Revised December 23, 2016

Process planning can be an effective way to improve the energy efficiency of production processes. Aimed at reducing both energy consumption and processing time (PT), a comprehensive approach that considers feature sequencing, process selection, and physical resources allocation simultaneously is established in this paper. As the number of decision variables increase, process planning becomes a large-scale problem, and it is difficult to be addressed by simply employing a regular meta-heuristic algorithm. A cooperative co-evolutionary algorithm, which hybridizes the artificial bee colony algorithm (ABCA) and Tabu search (TS), is therefore proposed. In addition, in the proposed algorithm, a novel representation method is designed to generate feasible process plans under complex precedence. Compared with some widely used algorithms, the proposed algorithm is proven to have a good performance for handling large-scale process planning in terms of maximizing energy efficiency and production times.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Top-down structure of process planning

Grahic Jump Location
Fig. 2

Selection of process

Grahic Jump Location
Fig. 3

Selection of physical resources

Grahic Jump Location
Fig. 4

Structure of traditional ABCA

Grahic Jump Location
Fig. 5

Encoding and decoding of process plan

Grahic Jump Location
Fig. 6

Sequence graph of a product with ten features

Grahic Jump Location
Fig. 7

The encapsulation of the process plan of clusters

Grahic Jump Location
Fig. 8

The calculation of overall fitness

Grahic Jump Location
Fig. 9

Structure of the proposed ABCA

Grahic Jump Location
Fig. 10

The structure of ANC 101 [15]

Grahic Jump Location
Fig. 11

Feature precedence graph of a product [15]

Grahic Jump Location
Fig. 12

Convergence curve of the proposed algorithm

Grahic Jump Location
Fig. 13

Running results of five algorithms (NF = 20)

Grahic Jump Location
Fig. 14

Running results of five algorithms (NF = 30)

Grahic Jump Location
Fig. 15

Running results of five algorithms (NF = 40)

Grahic Jump Location
Fig. 16

Values of the objectives (NF = 20)

Grahic Jump Location
Fig. 17

Values of the objectives (NF = 30)

Grahic Jump Location
Fig. 18

Values of the objectives (NF = 40)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In