Integrated Logistics Optimization

综合物流优化涉及运输、选址和库存管理方面的问题。它的动机是公司在处理其分销和补充活动时产生的高成本。该研究计划的主要目标是描述、建模和开发一组集成库存管理和配送问题的解决技术。这些问题出现在公司需要做出的三个决策层面：战略、战术和操作。本研究计划的长期目标是描述、建模和开发一套集成库存管理和配送问题的解决技术。确定设施位置和建设车辆路线，同时最大限度地减少库存持有和满足需求的成本，将真实地描述全球供应链中发生的分销活动。本研究的短期目标是：(1)设计战略性设施和设备选址问题的模型和算法；(2)提出将可持续性和环境因素纳入库存-路线组合问题的战术模型和解决技术；(3)建立长途和短途运输问题的模型和开发综合需求和库存考虑的解决方案。在整个研究计划中使用的通用方法侧重于为综合物流中出现的复杂问题开发数学整数规划模型和有效算法。这些方法包括保证最优解或提供强大下界的精确算法，以及分支和切割等分解技术。我们还将开发强大的启发式算法，能够快速产生高质量的解决方案。其中包括自适应大邻域搜索、可变邻域搜索、数学法、滚动范围启发式算法、近似数学规划算法以及其他已知和新技术。这项工作的关键步骤和重要部分在于确定如何为每个应用程序建模，确定准确的算法还是启发式算法是合适的，哪种特定的方法是最好的，以及如何实现和测试它。

Integrated logistics optimization involves transportation, location and inventory management concerns. It is motivated by the high cost incurred by companies when dealing their distribution and replenishment activities. The main objective of this research program is to describe, model and develop solution techniques for a set of integrated inventory management and distribution problems. These problems arise at the three levels of decisions that a company needs to make: strategic, tactical and operational.The long-term objectives of this research program are the description, modeling and development of solution techniques for a set of integrated inventory management and distribution problems. Determining facilities location and constructing vehicle routes while minimizing inventory holding and demand satisfaction costs, will provide a realistic depiction of distribution activities taking place in global supply chains. The short-term objectives of this research proposal are (1) to design models and algorithms for the strategic facility and equipment location problems, (2) to propose tactical models and solution techniques to incorporate sustainability and environmental aspects into the combined inventory-routing problem, and (3) to model and to develop solutions for long- and short-haul transportation problems that integrate demand and inventory considerations. The common methodology used throughout this research program focuses on the development of mathematical integer programming models and efficient algorithms for complex problems arising in integrated logistics. The methods include exact algorithms that guarantee optimal solutions or provide strong lower bounds, coupled with decomposition techniques such as branch-and-cut. We will also develop powerful heuristics capable of quickly producing high-quality solutions. These include adaptive large neighborhood search, variable neighborhood search, matheuristics, rolling horizon heuristics, approximate mathematical programming algorithms, and other known and new techniques. A crucial step and significant part of the work lies in determining, for each application, how to model it, whether an exact or heuristic algorithm is appropriate, which specific approach is best, and how to implement and test it.