A Hierarchical Lagrangean Relaxation Procedure for Solving Midterm Planning Problems
A Hierarchical Lagrangean Relaxation Procedure for Solving Midterm Planning Problems
复制标题
解决中期规划问题的分层拉格朗日松弛过程
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
C. Maranas
中科院分区：
文献类型：
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作者：
Anshuman Gupta and;C. Maranas
基于拉格朗日松弛，提出了求解中期规划问题的有效分解方法。所提出的求解技术的基本思想是通过分层放松关键复杂约束，将原始问题连续划分为更小、更易于计算的子问题。利用线性规划松弛双乘子信息实现了对这些复杂约束的系统辨识。这种分层拉格朗日松弛过程，以及上界生成启发式，被合并到一个次梯度优化框架中。这种解决方案策略被发现在解决方案的质量和计算需求方面比商业混合整数线性规划解决方案更有效，特别是对于较大的问题。
An efficient decomposition procedure for solving midterm planning problems is developed based on Lagrangean relaxation. The basic idea of the proposed solution technique is the successive partitioning of the original problem into smaller, more computationally tractable subproblems by hierarchical relaxation of key complicating constraints. The systematic identification of these complicating constraints is accomplished by utilizing linear programming relaxation dual-multiplier information. This hierarchical Lagrangean relaxation procedure, along with an upper bound generating heuristic, is incorporated within a subgradient optimization framework. This solution strategy is found to be much more effective, in terms of both quality of solution and computational requirements, than commercial mixed-integer linear programming solvers in bracketing the optimal value, especially for larger problems.