Using Lagrangean Techniques to Solve Hierarchical Production Planning Problems
提出一种将大规模生产计划问题分解为两个子问题的拉格朗日方法，通过迭代求解得到接近最优的可行解，测试中平均偏差仅2.2%。
This paper proposes and tests a procedure for decomposing a large scale production planning problem modeled as a mixed-integer linear program. We interpret this decomposition in the context of Hax and Meal's hierarchical framework for production planning. The procedure decomposes the production planning problem into two subproblems which correspond to the aggregate planning subproblem and a disaggregation subproblem in the Hax-Meal framework. The linking mechanism for these two subproblems is an inventory consistency relationship which is priced out by a set of Lagrange multipliers. The best values for the multipliers are found by an iterative procedure which may be interpreted as a feedback mechanism in the Hax-Meal framework. At each iteration, the procedure finds both a lower bound on the optimal value to the production planning problem and a feasible solution from which an upper bound is obtained. Our computational tests show that the best feasible solution found from this procedure is very close to optimal. For thirty-six test problems the percentage deviation from optimality never exceeds 4.4%, and the average percentage deviation is 2.2%. In addition, these best feasible solutions dominate the corresponding solutions obtained by a hierarchical procedure.