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.

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Fig. 1

Top-down structure of process planning

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Fig. 2

Selection of process

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Fig. 3

Selection of physical resources

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Fig. 4

Structure of traditional ABCA

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Fig. 5

Encoding and decoding of process plan

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Fig. 6

Sequence graph of a product with ten features

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Fig. 7

The encapsulation of the process plan of clusters

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Fig. 8

The calculation of overall fitness

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Fig. 9

Structure of the proposed ABCA

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Fig. 10

The structure of ANC 101 [15]

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Fig. 11

Feature precedence graph of a product [15]

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Fig. 12

Convergence curve of the proposed algorithm

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Fig. 13

Running results of five algorithms (NF = 20)

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Fig. 14

Running results of five algorithms (NF = 30)

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Fig. 15

Running results of five algorithms (NF = 40)

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Fig. 16

Values of the objectives (NF = 20)

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Fig. 17

Values of the objectives (NF = 30)

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Fig. 18

Values of the objectives (NF = 40)



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