The cutting stock problem with mixed objectives: Two heuristics based on dynamic programming

The purpose of this paper is to provide efficient methods for solving a large spectrum of industrial cutting stock problems. We propose two methods. Both are based on dynamic programming. In both methods, we reduce the computation burden by keeping, at each stage of the dynamic programming process, only the states which seem to be the most promising in terms of cost. One of the methods favours the computation time at the expense of the quality of the solution; this method is used in the sale department, where the goal is to propose a “good” solution in less than one minute. The second method favours the quality of the solution. Industrial applications are proposed.