This study proposes a simulation framework of procurement operations in the container logistics industry that can support the development of dynamic procurement strategies. The idea is inspired by the success of Passenger Origin-Destination Simulator (PODS) in the field of airline revenue management. By and large, research in procurement has focused on the optimisation of purchasing decisions, i.e., when-to-order and supplier selection, but a principled approach to procurement operations is lacking. We fill this gap by developing a probabilistic model of a procurement system. A discrete-event simulation logic is used to drive the evolution of the system. In a small case study, we use the simulation to deliver insights by comparing different supplier selection policies in a dynamic spot market environment. Policies based on contextual multi-armed bandits are seen to be robust to limited access to the information that determines the distribution of the outcome. This paper provides a pool of modelling ideas for simulation and observational studies. Moreover, the probabilistic formulation paves the way for advanced machine learning techniques and data-driven optimisation in procurement.

翻译：本研究提出了一种支持动态采购策略开发的集装箱物流行业采购运营仿真框架，其设计灵感源于航空收益管理领域中的旅客起讫点仿真器（PODS）的成功实践。现有采购研究主要聚焦于购买决策优化，即订购时机与供应商选择问题，但缺乏对采购运营的系统性方法论。为弥补这一空白，我们构建了采购系统的概率模型，采用离散事件仿真逻辑驱动系统演化。通过小型案例研究，我们运用该仿真框架对比分析了动态现货市场环境下的不同供应商选择策略，揭示了相关管理启示。基于情境的多臂老虎机策略在信息获取受限条件下仍能保持鲁棒性，有效维持了结果分布判别能力。本文为仿真实验与观测研究提供了模块化建模思路，同时其概率化建模框架为采购领域高级机器学习技术与数据驱动优化奠定了理论基础。