A parallel stochastic optimization algorithm for solving 2D bin packing problems

This study describes a stochastic approach to the problem of packing two-dimensional figures in a rectangular area efficiently. The techniques employed are similar to those used in genetic algorithms or in simulated annealing algorithms, algorithmic methods which are grouped under the general classification of stochastic optimization. A parallel processing system, an Intel i860 hypercube, is used to speed up execution. Execution time is quite lengthy due to the costly process of evaluating the lengths of layouts. Load balancing is quite efficient and near-perfect load balancing is achieved. Four different data sets were tested, the simplest consisting of 129 figures, each of seven possible shapes and of differing sizes. The goal of a minimum of 80% efficiency or utilization based on bin length was achieved in all runs performed.<
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