Optimization methods in Healthcare, Transportation and Telecommunications applications

这项研究计划追求两个主要目标，即将优化方法应用于医疗保健系统、交通运输和电信这三个非常重要的社会经济领域，以及继续开发结合启发式搜索和精确求解技术的新方法概念。*首先关于申请，这项工作将在以前赠款范围内进行的研究的基础上进行和扩大。更确切地说，它将集中于以下主题：*1)解决紧急车队的部署/重新部署问题；2)与现实生活中的卫生服务应用(家庭护理、医疗测试样本收集等)相关的位置、路线和调度问题的不同变体的研究；3)开发解决卫生保健系统人员调度问题的高效和可移植的解决方法；4)手术室规划问题的不同变体的研究；5)精确和近似方法的建模和实施，以解决具有非凸成本结构的交通网络设计问题；6)在传输和服务层电信网络的背景下设计/确定具有成本效益的可生存网络结构。*在方法学发展方面，我们将继续研究不同的研究途径，这些途径似乎具有增强元启发式和精确算法的有效性的良好潜力。其中，我们将继续开发结合各种启发式搜索概念和经典优化方法的混合方法。此外，我们还将借鉴模拟、稳健优化和随机规划等领域的知识，以进一步加强求解方法。这些开发将在上述应用程序以及其他应用程序上进行和验证。*

This research program pursues two main objectives, which are the application of optimization methods to the three very important socio-economical fields that are health care systems, transportation, and telecommunication, as well as the continuing development of novel methodological concepts combining both heuristic search and exact solution techniques. ***First with respect to applications, this work will build and expand on the research carried out within the previous grants. More precisely, it will focus on the following themes: ***1) Solving deployment/redeployment problems for emergency vehicle fleets; 2) Study of different variants of location, routing and scheduling problems related to real-life health services applications (home care, medical test sample collection, etc.); 3) Developing efficient and portable solution methods for solving health care system personnel scheduling problems; 4) Study of different variants of operating room planning problems; 5) Modeling and implementation of exact and approximate approaches for solving transportation network design problems with non-convex cost structures; 6) Design/dimensioning of cost-effective survivable network structures in the context of transmission as well as service layer telecommunication networks.***On the methodological development side, we will continue to study different research avenues that appear to hold good potential for enhancing the effectiveness of metaheuristics as well as exact algorithms. Among these we will continue our work on the development of hybrid methods incorporating various heuristic search concepts and classical optimization approaches. In addition, we will also draw from the areas of simulation, robust optimization and stochastic programming in order to further enhance solution approaches. These developments will be carried out and validated on the applications described above as well as on other applications.***