Logistics planning under uncertainty

当今社会的主要挑战之一在于管理资源的可持续利用，以及资源转化和分配为低环境影响和高积极的社会和经济影响的商品和服务。价值链管理领域对许多公司的改进做出了重大贡献。这一发展的一个关键部分是基于公司核心业务系统中使用的决策支持系统（DSSs）。新的信息和决策技术使这种分析性规划工具的发展成为可能，而业务研究对这些技术至关重要。另一个重要贡献是信息和数据的可用性。今天，包括公共和商业数据库、传感器和地理信息系统在内的各种信息系统产生了大量的信息，称为“大数据”。然而，这些数据大多是关于过去的活动，而规划需要关于未来的数据。由于价格、需求和驾驶时间的不确定性，DSSs所需的许多数据并不确切。尽管如此，大多数（如果不是全部）物流的决策支持系统都是基于确定性和解耦的规划模型。这一缺点为物流业务的规划和操作带来了许多挑战，因为必须实施某些预期和应对措施，以应对不确定性。虽然有一些方法可以用于不确定性下的规划，但它们通常不适合实际的物流规划。这些包括鲁棒优化、随机规划以及财务和操作对冲。在本提案中，我们的目标是开发考虑到不确定性的价值链物流的有效方法。结果将导致更强大和稳定的解决方案，减少重新规划的需要，并减少陷入需要昂贵的纠正措施的困难处境的风险，减少应急管理，即只在出现紧急问题时解决问题。所有这些都会降低成本，减少对环境的负面影响，增加灵活性和公平性，最后但并非最不重要的是可持续地利用我们的集体资源。

One of the key challenges in today's society resides in managing a sustainable usage of the resources as well as of their transformation and distribution into goods and services with both low environmental impact and high positive social and economic impacts. The area of value chain management has had a major contribution for improvements at many companies. A critical part of this development is based on Decision Support System (DSSs) that are used in companies' core business systems. This development of analytical planning tools has been made possible by new information and decision-making technologies, for which operational research is critical. Another important contribution is the availability of information and data. Today, a massive amount of information, termed "big data", is generated by various information systems including public and commercial databases, sensors and geographical information systems. However, much of this data is about past activities, while planning requires data about the future. Much of the data needed by DSSs is not known exactly due to, for example, price, demand, and driving time uncertainties. Despite that, most (if not all) DSSs for logistics are based on deterministic and decoupled planning models. This drawback creates number of planning and operational challenges for logistics operations as certain anticipation and counter measures must be implemented in order to handle uncertainty. Although there are approaches available for planning under uncertainty, they are generally not adapted for practical logistic planning. These include robust optimization, stochastic programming and financial and operational hedging. In this proposal, we aim to develop efficient approaches for value chain logistics taking into account uncertainty. The results will lead to more robust and stable solutions with less need of re-planning, and have reduced risk to be stuck with a difficult position which requires expensive corrective actions, and less emergency management i.e. only solve urgent problems as they appear. All this lead to reduced costs and negative environmental impact, increased flexibility and fairness, and last but not least sustainable use of our collective resources.