Large-Scale Combinatorial and Network Optimization

大规模组合和网络优化

• 批准号: RGPIN-2017-06054
• 负责人: Gendron, Bernard
• 金额: $ 5.17万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=651676
• 关键词: Large; Scale; Combinatorial; Network; Optimization

This research program is dedicated to the development of novel methods for solving large-scale combinatorial optimization problems, in particular those formulated in terms of networks. Such problems give rise to models that are difficult to solve, even using state-of-the-art optimization tools. Hence, we will develop specialized methods based on the latest advances in operations research. This research program aims to pursue and intensify the continued efforts of the applicant in developing methods for solving large-scale, difficult, combinatorial optimization problems. The long-term vision is driven by the objective of bridging the gaps between theory, methodology and algorithm implementation, thus leading to successful transfer and use of the proposed methods in real applications. In particular, the long-term goals of this research program are: 1) increase our capacity to solve large-scale combinatorial optimization problems; 2) expand the applicability of large-scale combinatorial optimization methods; 3) develop a unifying generic methodology to integrate supply optimization and demand analysis; 4) exploit knowledge to solve real applications. Our research on combinatorial optimization is driven by real applications, mostly in the area of logistics and transportation. These include applications of network design and facility location models in freight distribution and safety/security in transportation, as well as applications of staff scheduling in retail services, all of prime importance for the Canadian economy. Our ultimate goal is to provide practical solutions to end-users of these applications by developing advanced models and methods.

该研究计划致力于开发解决大规模组合优化问题的新方法，特别是那些在网络方面制定的方法。这样的问题会导致难以解决的模型，即使使用最先进的优化工具。因此，我们将根据运筹学的最新进展开发专门的方法。该研究计划旨在追求和加强申请人在开发解决大规模，困难，组合优化问题的方法方面的持续努力。长期愿景的目标是弥合理论，方法和算法实现之间的差距，从而导致成功的转让和使用所提出的方法在真实的应用。特别是，本研究计划的长期目标是：1）提高我们解决大规模组合优化问题的能力; 2）扩大大规模组合优化方法的适用性; 3）开发一个统一的通用方法，以整合供应优化和需求分析; 4）利用知识来解决真实的应用。我们对组合优化的研究是由真实的应用驱动的，主要是在物流和运输领域。其中包括网络设计和设施选址模型在货运配送和运输安全/安保方面的应用，以及零售服务中员工调度的应用，所有这些对加拿大经济都至关重要。我们的最终目标是通过开发先进的模型和方法，为这些应用的最终用户提供实用的解决方案。