Resolution of 1-D Bin Packing Problem using Augmented Neural Networks and Minimum Bin Slack

Abstract

The objective of this work is to compare the Augmented Neural Network (AugNN) metaheuristic to Minimum Bin Slack (MBS) heuristic to solve Combinatorial Optimization Problems, specifically, in this case, the one-dimensional Bin Packing Problem (BPP), a class of Cutting and Packing Problems (CPP). CPPs are easily found among various industry sectors and its proper treatment can improve use of raw material and/or physical space. In order to optimize AugNN parameters a Design of Experiment (DOE) was applied. The tests, developed in many benchmark problems found in the literature, showed that MBS heuristic was, in general superior, both in terms of quality of solution (approximately 70 percent better) as in terms of computational time (about 90 percent less).