Combined cutting stock and assignment optimization based on genetic algorithms

The combined cutting stock and assignment problem is addressed. First, this problem is posed by reviewing the literature and is defined by mixing the existing cutting stock and assignment scenario. Then mathematical modeling is employed to establish a quantitative platform for this problem. Theoretical and empirical analyses are conducted to secure relations among various process parameters. An optimization methodology is developed to provide optimal or suboptimal solutions to the problem based on a heuristically guided multiple-stage genetic algorithm. Dynamic scheduling techniques are developed for conditions with varying process parameters. Finally, numerical results are included to justify the efficiency and effectiveness of this approach.