An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling

Yanbin Jiang; Pengyu Zhou; Rongxin Zhan; Xiang Li; Dongni Li

doi:10.1109/CEC.2016.7743785

An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling

Yanbin Jiang, Pengyu Zhou, Rongxin Zhan, Xiang Li, Dongni Li^*

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The intercell scheduling problems arise due to the intercell transfers in cellular manufacturing systems. In this paper, the intercell scheduling problem with limited transportation capacity, which is essentially the coordination of part scheduling and intercell transportation, is addressed. Because it is a practical decision-making problem of high complexity and large problem size, an artificial bee colony (ABC) with self-adaptive operators and alterable search depth (SOAD) approach is presented in this paper. By introducing SOAD into the employed bee phase, the adoption of genetic operators and the corresponding search depth are adaptively determined according to their historical performance. With respect to the objective of minimizing total weighted tardiness, the proposed approach is compared with the others self-adaptive strategy for selecting operators and population-based metaheuristics. Computational results show that the proposed approach can maintain a better balance between global exploration and local exploitation.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	112-119
Number of pages	8
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7743785
Publication status	Published - 14 Nov 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

Name	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

Keywords

Artificial bee colony
Intercell scheduling
Search depth
Self-adaptive mechanism

Access to Document

10.1109/CEC.2016.7743785

Cite this

Jiang, Y., Zhou, P., Zhan, R., Li, X., & Li, D. (2016). An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 112-119). Article 7743785 (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7743785

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abstract = "The intercell scheduling problems arise due to the intercell transfers in cellular manufacturing systems. In this paper, the intercell scheduling problem with limited transportation capacity, which is essentially the coordination of part scheduling and intercell transportation, is addressed. Because it is a practical decision-making problem of high complexity and large problem size, an artificial bee colony (ABC) with self-adaptive operators and alterable search depth (SOAD) approach is presented in this paper. By introducing SOAD into the employed bee phase, the adoption of genetic operators and the corresponding search depth are adaptively determined according to their historical performance. With respect to the objective of minimizing total weighted tardiness, the proposed approach is compared with the others self-adaptive strategy for selecting operators and population-based metaheuristics. Computational results show that the proposed approach can maintain a better balance between global exploration and local exploitation.",

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author = "Yanbin Jiang and Pengyu Zhou and Rongxin Zhan and Xiang Li and Dongni Li",

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Jiang, Y, Zhou, P, Zhan, R, Li, X & Li, D 2016, An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743785, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 112-119, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7743785

An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling. / Jiang, Yanbin; Zhou, Pengyu; Zhan, Rongxin et al.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 112-119 7743785 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhan, Rongxin

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AU - Li, Dongni

PY - 2016/11/14

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N2 - The intercell scheduling problems arise due to the intercell transfers in cellular manufacturing systems. In this paper, the intercell scheduling problem with limited transportation capacity, which is essentially the coordination of part scheduling and intercell transportation, is addressed. Because it is a practical decision-making problem of high complexity and large problem size, an artificial bee colony (ABC) with self-adaptive operators and alterable search depth (SOAD) approach is presented in this paper. By introducing SOAD into the employed bee phase, the adoption of genetic operators and the corresponding search depth are adaptively determined according to their historical performance. With respect to the objective of minimizing total weighted tardiness, the proposed approach is compared with the others self-adaptive strategy for selecting operators and population-based metaheuristics. Computational results show that the proposed approach can maintain a better balance between global exploration and local exploitation.

AB - The intercell scheduling problems arise due to the intercell transfers in cellular manufacturing systems. In this paper, the intercell scheduling problem with limited transportation capacity, which is essentially the coordination of part scheduling and intercell transportation, is addressed. Because it is a practical decision-making problem of high complexity and large problem size, an artificial bee colony (ABC) with self-adaptive operators and alterable search depth (SOAD) approach is presented in this paper. By introducing SOAD into the employed bee phase, the adoption of genetic operators and the corresponding search depth are adaptively determined according to their historical performance. With respect to the objective of minimizing total weighted tardiness, the proposed approach is compared with the others self-adaptive strategy for selecting operators and population-based metaheuristics. Computational results show that the proposed approach can maintain a better balance between global exploration and local exploitation.

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Jiang Y, Zhou P, Zhan R, Li X, Li D. An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 112-119. 7743785. (2016 IEEE Congress on Evolutionary Computation, CEC 2016). doi: 10.1109/CEC.2016.7743785

An artificial bee colony with self-adaptive operators and alterable search depth approach for intercell scheduling

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