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 optimization 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 optimizes 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 optimize the allocation of the components in certain areas in the workstation.

The influence of the picking times of the components in time and space assembly line balancing problems: An approach with evolutionary algorithms / Alsina, EMANUEL FEDERICO; Capodieci, Nicola; Cabri, Giacomo; Regattieri, Alberto; Gamberi, Mauro; Pilati, Francesco; Faccio, Maurizio. - STAMPA. - (2015), pp. 1021-1028. (Intervento presentato al convegno IEEE Symposium Series on Computational Intelligence, SSCI 2015 tenutosi a Cape Town; South Africa nel December 7-10 2015) [10.1109/SSCI.2015.148].

The influence of the picking times of the components in time and space assembly line balancing problems: An approach with evolutionary algorithms

ALSINA, EMANUEL FEDERICO;CAPODIECI, NICOLA;CABRI, Giacomo;REGATTIERI, ALBERTO;
2015

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 optimization 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 optimizes 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 optimize the allocation of the components in certain areas in the workstation.
2015
IEEE Symposium Series on Computational Intelligence, SSCI 2015
Cape Town; South Africa
December 7-10 2015
1021
1028
Alsina, EMANUEL FEDERICO; Capodieci, Nicola; Cabri, Giacomo; Regattieri, Alberto; Gamberi, Mauro; Pilati, Francesco; Faccio, Maurizio
The influence of the picking times of the components in time and space assembly line balancing problems: An approach with evolutionary algorithms / Alsina, EMANUEL FEDERICO; Capodieci, Nicola; Cabri, Giacomo; Regattieri, Alberto; Gamberi, Mauro; Pilati, Francesco; Faccio, Maurizio. - STAMPA. - (2015), pp. 1021-1028. (Intervento presentato al convegno IEEE Symposium Series on Computational Intelligence, SSCI 2015 tenutosi a Cape Town; South Africa nel December 7-10 2015) [10.1109/SSCI.2015.148].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1101129
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