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.

许多实际重要的优化问题本质上是组合的。一些例子包括可靠通信网络的最小成本设计，快速印刷电路板生产，遗传进化树的构建以及各种调度和路由问题的优化。不幸的是，许多这样的问题属于一类被称为“NP-hard”的问题，这被认为是极难解决的有力证据，而且我们极不可能找到解决这些问题的技术，保证在所有情况下都是实用和有效的。缺乏有效的方法来解决这些问题是非常重要的，因为存在大规模的、现实世界的实例需要解决。幸运的是，在实践中，这些问题的解即使不是最优的，至少保证在最优（最佳）解的一定满意百分比内，这在许多情况下是更可行的任务。