Hybrid Intelligent Packing System (HIPS) through integration of Artificial Neural Networks, Artificial Intelligence, and mathematical programming

摘要

A successful solution to the packing problem is a major step toward material savings on the scrap that could be avoided in the cutting process and therefore money savings. Although the problem is of great interest, no satisfactory algorithm has been found that can be applied to all the possible situations. This paper models a Hybrid Intelligent Packing System (HIPS) by integrating Artificial Neural Networks (ANNs), Artificial Intelligence (AI), and Operations Research (OR) approaches for solving the packing problem. The HIPS consists of two main modules, an intelligent generator module and a tester module. The intelligent generator module has two components: (i) a rough assignment module and (ii) a packing module. The rough assignment module utilizes the expert system and rules concerning cutting restrictions and allocation goals in order to generate many possible patterns. The packing module is an ANN that packs the generated patterns and performs post-solution adjustments. The tester module, which consists of a mathematical programming model, selects the sets of patterns that will result in a minimum amount of scrap.