We explore a problem faced by agri-food e-commerce platforms in purchasing different, perishable products and collecting them from multiple producers and delivering them to a single warehouse, aiming to maintain adequate inventory levels to meet current and future customer demand, while avoiding waste. Customer demand and suppliers’ purchase prices and supply volumes are uncertain and revealed on a periodical basis. Every period, purchasing, inventory, and routing decisions are made to satisfy demand and to build inventory for future periods. For effective decisions integrating all three decision components and anticipating future developments, we propose a stochastic lookahead method that, in every period, samples future scenarios for demand, supply volumes, and prices. It then solves a two-stage stochastic program to obtain the decision for the current period. To make this approach computationally tractable, we reduce the routing decision in the two-stage program and use an approximate routing cost instead. Given the reduced decision, we then create the final decision via a conventional routing heuristic. We learn the routing cost approximation adaptively via repeated training simulations. In comprehensive experiments, we show that all three components, stochastic lookahead, routing cost approximation, and adaptive learning, are very effective individually, but especially in combination. We also provide a comprehensive analysis of the problem parameters and obtain valuable insights in problem and methodology.

中文翻译：

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用于动态多周期采购和库存路由的自适应随机前瞻策略


我们探讨农产品电商平台在采购不同的易腐产品并将其从多个生产商收集并将其运送到单个仓库时面临的问题，旨在保持充足的库存水平以满足当前和未来的客户需求，同时避免浪费。客户需求和供应商的采购价格和供应量是不确定的，并定期披露。每个时期的采购、库存和路线决策都是为了满足需求并为未来时期建立库存。为了整合所有三个决策组成部分并预测未来发展，我们提出了一种随机前瞻方法，该方法在每个时期对未来场景的需求、供应量和价格进行采样。然后，它求解两阶段随机程序以获得当前周期的决策。为了使这种方法在计算上易于处理，我们减少了两阶段程序中的路由决策，并使用近似路由成本。考虑到简化的决策，我们然后通过传统的路由启发式创建最终决策。我们通过重复的训练模拟自适应地学习路由成本近似。在综合实验中，我们表明，随机前瞻、路由成本近似和自适应学习这三个组件单独使用时非常有效，但组合起来尤其有效。我们还提供对问题参数的全面分析，并获得有关问题和方法的宝贵见解。