Iterative aggregation and disaggregation algorithm for pseudo-polynomial network flow models with side constraints
提出一种基于聚合技术的迭代算法，用于精确求解可建模为带侧约束的循环模型的NP难问题，在车辆路径和下料问题上优于已知方法。
This paper develops a general solution framework based on aggregation techniques to solve NP-Hard problems that can be formulated as a circulation model with specific side constraints. The size of the extended Mixed Integer Linear Programming formulation is generally pseudo-polynomial. To efficiently solve exactly these large scale models, we propose a new iterative aggregation and disaggregation algorithm. At each iteration, it projects the original model onto an aggregated one, producing an approximate model. The process iterates to refine the current aggregated model until the optimality is proved. The computational experiments on two hard optimization problems (a variant of the vehicle routing problem and the cutting-stock problem) show that a generic implementation of the proposed framework allows us to outperform previous known methods.