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:本研究提出了一种集装箱物流行业采购运营的模拟框架,可支持动态采购策略的开发。这个想法得益于航空公司收益管理领域的乘客起始-目的模拟器(PODS)的成功。总体来说,采购方面的研究一直集中在采购决策的优化,即何时订购和供应商选择,但缺乏有原则的采购运营方法。我们通过开发采购系统的概率模型来填补这一空白。使用离散事件模拟逻辑来驱动系统的演变。在一项小型案例研究中,我们使用模拟来比较动态现货市场环境下不同的供应商选择策略,以提供见解。基于上下文多臂赌博机的策略被证明对于有限的访问决定结果分布的信息具有鲁棒性。本文提供了用于模拟和观察研究的建模思路。此外,概率公式铺平了在采购中实现先进的机器学习技术和数据驱动优化的道路。