The Influence of the Picking Times of the Components in Time and Space Assembly Line Balancing Problems: An Approach with Evolutionary Algorithms

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@InProceedings{Alsina:2015:ieeeSSCI,
  author =       "Emanuel F. Alsina and Nicola Capodieci and 
                 Giacomo Cabri and Alberto Regattieri",
  booktitle =    "2015 IEEE Symposium Series on Computational
                 Intelligence",
  title =        "The Influence of the Picking Times of the Components
                 in Time and Space Assembly Line Balancing Problems: An
                 Approach with Evolutionary Algorithms",
  year =         "2015",
  pages =        "1021--1028",
  abstract =     "The balancing of assembly lines is one of the most
                 studied industrial problems, both in academic and
                 practical fields. The workable application of the
                 solutions passes through a reliable simplification of
                 the real-world assembly line systems. Time and space
                 assembly line balancing problems consider a realistic
                 versions of the assembly lines, involving the
                 optimisation of the entire line cycle time, the number
                 of stations to install, and the area of these stations.
                 Components, necessary to complete the assembly tasks,
                 have different picking times depending on the area
                 where they are allocated. The implementation in the
                 real world of a line balanced disregarding the
                 distribution of the tasks which use unwieldy components
                 can result unfeasible. The aim of this paper is to
                 present a method which balances the line in terms of
                 time and space, hence optimises the allocation of the
                 components using an evolutionary approach. In
                 particular, a method which combines the bin packing
                 problem with a genetic algorithm and a genetic
                 programming is presented. The proposed method can be
                 able to find different solutions to the line balancing
                 problem and then evolve they in order to optimise the
                 allocation of the components in certain areas in the
                 workstation.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2015.148",
  month =        dec,
  notes =        "Dept. of Phys., Inf. & Math., Univ. of Modena & Reggio
                 Emilia, Modena, Italy Also known as \cite{7376724}",
}

Genetic Programming entries for Emanuel Federico Alsina Nicola Capodieci Giacomo Cabri Alberto Regattieri

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