Title: Resolution of one-dimensional bin packing problems using augmented neural networks and minimum bin slack

Authors: Ricardo De Almeida; Maria Teresinha Arns Steiner

Addresses: Industrial and Systems Engineering Post-Graduate Program, Pontifical Catholic University of Paraná – PUCPR, Curitiba, Brazil ' Industrial and Systems Engineering Post-Graduate Program, Pontifical Catholic University of Paraná – PUCPR, Curitiba, Brazil

Abstract: The objective of this work is to compare the augmented neural network (AugNN) metaheuristic to minimum bin slack (MBS) heuristic to solve combinatorial optimisation problems, specifically, in this case, the one-dimensional bin packing problem (1D-BPP), a class of cutting and packing problems (CPP). CPP are easily found among various industry sectors and its proper treatment can improve the use of stocks in cutting problems or optimise physical space in packing problems. In order to optimise AugNN parameters, a design of experiment (DOE) was applied in order to guide a statistical analysis of different configurations of AugNN. The tests, developed in many benchmark problems found in the literature, showed that MBS heuristic was, in general superior, both in terms of the solution quality, which is about 70% better, and computational time, which is about 90% less.

Keywords: artificial neural networks; ANNs; minimum bin slack; MBS; design of experiments; DOE; 1D bin packing; cutting and packing; metaheuristics.

DOI: 10.1504/IJICA.2016.080860

International Journal of Innovative Computing and Applications, 2016 Vol.7 No.4, pp.214 - 224

Received: 04 Feb 2016
Accepted: 14 Jun 2016
Published online: 09 Dec 2016 *

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