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Title: An MILP model and a hybrid evolutionary algorithm for integrated operation optimisation of multi-head surface mounting machines in PCB assembly
Authors: Luo, Jiaxiang
Liu, Jiyin
Hu, Yueming
Keywords: Operation optimisation
Production modelling
Evolutionary algorithms
Combinatorial optimisation
PCB assembly
Issue Date: 2017
Publisher: © Informa UK Limited, trading as Taylor & Francis Group
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.
Abstract: 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.
Description: 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
Sponsor: The work is supported by the Fundamental Research Funds for the Central Universities of China [grant number 2014z0033].
Version: Accepted for publication
DOI: 10.1080/00207543.2016.1200154
URI: https://dspace.lboro.ac.uk/2134/22462
Publisher Link: http://dx.doi.org/10.1080/00207543.2016.1200154
ISSN: 0020-7543
Appears in Collections:Published Articles (Business)

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