Stochastic optimization methods for transportation and network design problems

在越来越多的公司开展国际活动的经济中，物流起着至关重要的作用。 在物流领域，运输和网络设计问题是确保竞争力必须解决的一些更重要问题的核心。 由于这些问题是要解决考虑规划环境中的不确定性总是存在的，研究计划，这是本提案的一部分，致力于发展的优化模型和解决方案算法的随机问题，这一领域。 这里将涉及三个主要的研究领域。 研究领域一将考虑随机车辆路径问题，因为它们是在考虑复杂的运营问题时定义的（例如，“丰富”的规划环境）。 研究领域二将涵盖供应链的设计时，不确定性影响的参数要考虑。 在这前两个研究领域内，将研究实际和应用问题;因此，将承担三项主要任务。 第一部分将致力于对所考虑的问题进行建模。 将特别强调制定观察到的不确定性，并表达面临这些问题的公司可以采取的真实的追索行动。 第二项任务将包括制定解决问题的算法。 最后，第三项任务将包括验证所获得的解决方案。 在这项任务中，基于模拟的模型将被用来衡量所提出的随机方法的有效性。 第三个也是最后一个研究领域中定义的建议将用于发展的一般解决方法的经典随机问题。 在这一领域内，将考虑采用混合和并行战略。

In an economy where more and more companies have international activities, logistics plays a crucial role. Within the logistics field, transportation and network design issues are at the heart of some of the more important problems that must be solved to ensure competitiveness. Since these issues are to be addressed considering a planning environment where uncertainty is always present, the research program that is part of this proposal is dedicated to the development of optimization models and solution algorithms for stochastic problems related to this field. Three main research areas will be covered here. Research area one will consider stochastic vehicle routing problems as they are defined when complex operational issues are to be taken into account (e.g., "rich" planning environment). Research area two will cover the design of the supply chain when uncertainty influences the parameters to be considered. Within these first two research areas, practical and applied problems will be studied; therefore, three major tasks will be undertaken. The first will be dedicated to the modelling of the problems considered. A particular emphasis will be placed on formulating the uncertainty observed and on expressing the real recourse actions available to companies that face these issues. The second task will include the development of solution algorithms for the problems formulated. Finally, the third task will cover the validation of the solutions obtained. Within this task, simulation based models will be used to measure the effectiveness of the stochastic approach that is proposed. The third and last research area defined in this proposal will be used for the development of general solution methodologies for classical stochastic problems. Within this area, the use of both hybrid and parallel strategies will be considered.