An improved cuckoo search algorithm for semiconductor final testing scheduling

Document Type

Conference Proceeding

Publication Date

7-1-2017

Abstract

This paper presents a cuckoo search algorithm to minimize makespan for a semiconductor final testing scheduling problem. Each solution is a two-part vector consisting of a machine assignment and an operation sequence. In each iteration, a parameter feedback control scheme based on reinforcement learning is proposed to balance the diversification and intensification of population, and a surrogate model is employed to reduce computational cost. According to the Rechenberg's 1/5 Criterion, reinforcement learning uses the proportion of beneficial mutation as feedback. As a result, the surrogate modeling only needs to evaluate the relative ranking of solutions. A heuristic approach based on the smallest position value rule and a modular function is proposed to convert continuous solutions obtained from Levy flight into discrete ones. The computational complexity analysis is presented, and various simulation experiments are performed to validate the effectiveness of the proposed algorithm.

Identifier

85044947996 (Scopus)

ISBN

[9781509067800]

Publication Title

IEEE International Conference on Automation Science and Engineering

External Full Text Location

https://doi.org/10.1109/COASE.2017.8256241

e-ISSN

21618089

ISSN

21618070

First Page

1040

Last Page

1045

Volume

2017-August

Grant

NJ07102

Fund Ref

National Natural Science Foundation of China

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