Consistent Stochastic Inventory Routing Management (COSIMA)

该项目集成了供应链中通常独立或顺序研究的两个核心物流决策：库存管理和运输路线优化。虽然这种集成并不新鲜，在文献中被称为库存路线问题，但对于不确定情况下的现实问题，很少有方法论的决策支持。因此，我们建议扩展现有的关于现实世界几个重要特征的知识，并开发新的整合方法。零售商的随机需求通常是非平稳和相关的，允许紧急发货，并且必须满足服务水平要求。此外，我们纳入了一致性，即某家零售商的送货应该始终在相同的时间间隔内进行。本文从两个领域的分析入手，结合上述实际问题的特点，以另一个领域的决策为给定参数，开发和研究了两个领域整合的新的预期和迭代方案，并利用随机动态规划方法给出了精确解。为了解决现实世界中的问题，我们将开发，特别是集成来自库存和路由域的启发式方法。不确定性将使用情景和基于抽样的方法进行建模和表示。启发式求解方法将建立在近似动态规划、基于模板的自适应大邻域搜索和分支与后悔基础上。生成的方法将使用人工问题实例和真实世界的测试用例进行验证。

The project integrates two core logistics decisions in supply chains typically investigated independently or sequentially: inventory management and transportation routing optimization. Although this integration is not new and known as the Inventory Routing Problem in the literature, there is little methodological decision support for real-world problems under uncertainty. We therefore propose to extend the existing knowledge with regard to several important real-world characteristics and develop new approaches for the integration.Stochastic demands at the retailers are typically non-stationary and correlated, emergency shipments are allowed and service level requirements have to be met. Moreover, we incorporate consistency, i.e. delivery at a certain retailer should always take place within the same time interval. This leads to a two-stage multi-echelon inventory problem with routing, stochastic lead times and non-equidistant review periods.Starting from an analysis of both individual domains and including the above real-world problem characteristics, we take the decisions of the respective other domain as given parameters and develop and investigate new anticipatory and iterative schemes for the integration of both domains.For benchmarking purposes, we develop exact solutions using stochastic dynamic programming approaches. For the solution of real-world sized problems, we will develop and in particular integrate heuristic approaches from the inventory and routing domains. Uncertainty will be modeled and represented using scenarios and sampling-based methods. Heuristic solution methods will build on approximate dynamic programming, template-based adaptive large neighborhood search, and branch & regret. The resulting approaches will be validated using artificial problem instances and real-world test cases.