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An MILP model and a hybrid evolutionary algorithm for integrated operation optimisation of multi-head surface mounting machines in PCB assembly

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journal contribution
posted on 15.09.2016, 15:03 by Jiaxiang Luo, Jiyin Liu, Yueming Hu
This paper focuses on an operation optimisation problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzle types as well as components to heads, assigning feeders to slots and determining component pickup and placement sequences. According to the depth of making decisions, the sub-problems are first classified into two layers. Based on the classification, a two-stage mixed-integer linear programming (MILP) is developed to describe it and a two-stage problem-solving frame with a hybrid evolutionary algorithm (HEA) is proposed. In the first stage, a constructive heuristic is developed to determine the set of nozzle types assigned to each head and the total number of assembly cycles; in the second stage, constructive heuristics, an evolutionary algorithm with two evolutionary operators and a tabu search (TS) with multiple neighbourhoods are combined to solve all the sub-problems simultaneously, where the results obtained in the first stage are taken as constraints. Computational experiments show that the HEA can obtain good near-optimal solutions for small size instances when compared with an optimal solver, Cplex, and can provide better results when compared with a TS and an EA for actual instances.

Funding

The work is supported by the Fundamental Research Funds for the Central Universities of China [grant number 2014z0033].

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Production Research

Pages

1 - 16

Citation

LUO, J., LIU, J. and HU, Y., 2017. An MILP model and a hybrid evolutionary algorithm for integrated operation optimisation of multi-head surface mounting machines in PCB assembly. International Journal of Production Research, 55 (1), pp. 145-160.

Publisher

© Informa UK Limited, trading as Taylor & Francis Group

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

02/06/2016

Publication date

2017

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 21 Jun 2016, available online: http://dx.doi.org/10.1080/00207543.2016.1200154

ISSN

0020-7543

eISSN

1366-588X

Language

en

Exports