An expert system to react to defective areas in nesting problems

Production plans in the textile industry, and other practical applications, involve cutting irregular pieces from raw materials. Defective areas in the raw material may be detected during the cutting process, requiring an adaptation of the original layout. The response time to provide an alternative layout is short, precluding the use of exact methods to overcome defective areas. The main contribution of this paper is to provide an expert system to quickly obtain an alternative layout, overcoming defective areas in the object. The expert system comprises a greedy heuristic based on the allocation sequence suggested by reinforcement learning. Computational experiments have two main objectives. The first one is to validate reinforcement learning as a suitable strategy to tackle nesting problems. The results attest to the ability of the strategy to achieve the best results in the literature. The second objective is to show the ability of the expert system to provide alternative layouts within a short response time. The quality of the solutions obtained by the expert system evidence the strength of the proposed system in overcoming defective areas. •Development of the first reinforcement learning approach dedicated to generate allocations sequences for the nesting problem.•Integration of placement heuristics and reinforcement learning to generate good layouts.•The development of an expert system that allows to overcome defective areas in nesting problems within seconds.