Stochastic optimization of network design and transportation problems

点击翻译按钮获取中文摘要

Transportation and logistics systems play a paramount role in how modern economies and societies function. The ever-increasing volumes of people and freight that need to be efficiently transported worldwide to properly support economic and social activities while, at the same time, minimize the negative footprint caused by these movements, pose complex planning problems for the decision makers managing these systems. The complexity of such problems is considerably increased by the presence of various sources of uncertainty that randomly and drastically change the contexts in which such planning is conducted. Therefore, solving transportation and logistics problems while explicitly considering all relevant sources of uncertainty has become an important issue to address by both companies, who are continuously striving to remain competitive on the markets, and governments, who are tasked with reducing the negative effects associated with transportation. In all cases, solving such problems poses significant challenges that the present proposal will meet.******Two general types of optimization problems, which are central to the planning and operation of transportation and logistics systems, will be studied: dynamic and stochastic vehicle routing problems and stochastic network design problems. My short-term objectives are twofold: 1) to create novel stochastic optimization methods, by combining machine learning techniques, parallel computation and mathematical programming strategies, to efficiently solve the studied problems and 2) design analytical tools capable of systematically assessing the overall effects of the various sources of uncertainty on the decision-making processes.******Therefore, the various studies that will be performed in the present proposal will bring significant benefits in the form of new and efficient decision support tools for a series of important optimization problems under uncertainty that arise in the context of planning and operating transportation and logistics systems. From a scientific perspective, this proposal will push boundaries by bridging the fields of machine learning and stochastic optimization. Ultimately, the research program will produce a novel methodological framework to assess and study the impacts of uncertainty on optimization problems and enable such problems to be efficiently solved for realistically sized instances (i.e., such as they appear in practical settings).