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Consistent Stochastic Inventory Routing Management (COSIMA)

一致随机库存路由管理 (COSIMA)

基本信息

批准号：
272984253
负责人：
Professor Dr. Stefan Minner
金额：
--
依托单位：
Institut für Betriebswirtschaftlslehre
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/272984253?language=en
关键词：
Consistent Stochastic Inventory Routing Management

项目摘要

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.

该项目整合了供应链中通常独立或顺序调查的两个核心物流决策：库存管理和运输路线优化。虽然这种整合并不新鲜，在文献中被称为库存路径问题，但在不确定性下的现实问题中几乎没有方法决策支持。因此，我们建议扩展现有的知识与几个重要的现实世界的特点，并开发新的方法的integration.Stochastic需求在零售商通常是非平稳的和相关的，允许紧急发货和服务水平的要求得到满足。此外，我们还考虑了一致性，即在某个零售商处的交付应始终在相同的时间间隔内进行。这导致了一个两阶段的多级库存问题的路由，随机提前时间和非等距的审查期。从分析两个单独的域，并包括上述现实世界的问题的特点，我们采取相应的其他域的决策作为给定的参数，并开发和研究新的预期和迭代计划的集成两个域。我们使用随机动态规划方法开发精确解。对于现实世界的大小问题的解决方案，我们将开发，特别是整合启发式方法从库存和路由域。将使用情景和基于抽样的方法对不确定性进行建模和表示。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，和分支和遗憾。将使用人工问题实例和真实世界的测试用例来验证所产生的方法。