A system of optimizing nesting with analogical learning mechanism

This paper introduces a system of optimizing nesting with an analogical learning mechanism. First, irregular graphs are processed in advance and their optimal rectangular enclosures or combined rectangular enclosures are obtained. The problem is transformed into the solution of the rectangular stock allocation problem. Then the relative optimal solution is gained using heuristic search. In the solution mechanism of the system, an analogical reasoning mechanism is used. Therefore, the system has a certain degree of learning ability, and the cost of nesting decreases gradually as nesting times increase.