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作仿真框架。该构想受航空业收益管理领域乘客起讫点仿真器（PODS）成功应用的启发。总体而言，现有采购研究主要聚焦于采购决策优化（如订货时机与供应商选择），但缺乏系统化的采购运作方法论。本研究通过构建采购系统的概率模型填补了这一空白，采用离散事件仿真逻辑驱动系统演化。在小型案例研究中，我们通过对比动态现货市场环境下不同供应商选择策略的效能，利用仿真获得管理启示。基于情境多臂老虎机的策略在信息获取受限（信息决定结果分布）条件下展现出稳健性。本文为仿真研究与观测研究提供了模型思路库，同时概率建模框架为采购领域引入先进机器学习技术与数据驱动优化奠定了基础。