Resolution of one-dimensional bin packing problems using augmented neural networks and minimum bin slack. (2016)

• Record Type: Journal Article
• Authors: Almeida, Ricardo De; Steiner, Maria Teresinha Arns
• Is Part Of: International journal of innovative computing and applications. Volume 7:Number 4(2016)
• Page Start: 214
• Page End: 224
• Publication Date: 2016
• Subjects: artificial neural networks -- ANNs -- minimum bin slack -- MBS -- design of experiments -- DOE -- 1D bin packing -- cutting and packing -- metaheuristics; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.