Applying Methods from Combinatorial Optimization in Solving Large-Scale Real World Problems

应用组合优化方法解决大规模现实世界问题

• 批准号: 36740-2013
• 负责人: Boyd, Sylvia
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=544764
• 关键词: Applying; Methods; Combinatorial; Optimization; Solving

Many practically important optimization problems are combinatorial in nature. Some examples include the minimum cost design of reliable communication networks, fast printed circuit board production, the building of genetic evolutionary trees as well as the optimization of various scheduling and routing problems. Unfortunately, may such problems belong to a class called "NP-hard", which is considered strong evidence that they are extremely difficult to solve, and that it is highly unlikely that we will find techniques for solving them that are guarantee to be practical and efficient in all cases. The lack of efficient methods for such problems is of significant importance as there exist large-scale, real world instances requiring solutions. Fortunately, in practice, it is often sufficient to obtain solutions for these problems which, if not optimal, are at least guaranteed to lie within a certain satisfactory percentage of the optimal (best) solution, which in many cases is much more viable task.

实际上，许多实际上重要的优化问题本质上是组合。 一些示例包括可靠通信网络的最低​​成本设计，快速印刷电路板的生产，遗传进化树的建造以及各种调度和路由问题的优化。 不幸的是，此类问题可能属于称为“ NP-HARD”的类别，该类别被认为是很难解决的有力证据，而且我们很不可能找到解决方案的技术，这些技术在所有情况下都可以保证实用和有效。 由于存在需要解决方案的大规模现实世界实例，因此缺乏有效的方法很重要。 幸运的是，实际上，获得这些问题的解决方案通常足够，如果不是最佳的，至少可以保证在最佳（最佳）解决方案的一定令人满意的百分比之内，在许多情况下，这是更可行的任务。