Synchronization, cross-docking, and decoupling in supply chain networks

At any distribution centre (DC), the decision of whether to synchronise inbound and outbound flows for cross-docking, or to decouple these flows by maintaining inventory, has a significant impact on supply chain performance. Key drivers of this decision, in turn, are the sizes of the discrete lots that comprise the flows. Thus, we formulate an original optimisation model that determines order lot-sizing decisions to minimise, for given constant arc flows, the sum of ordering cost and pipeline inventory cost on arcs and buffer inventory at DCs. The model employs an average throughput as a surrogate to estimate buffer inventory at facilities at which synchronisation is not economical and therefore serves to decouple inbound and outbound flows. Perfect lot-for-lot matching of shipments would impose very restrictive constraints on supply chain operations, but equality of average throughput indicates an innovative, relaxed mode of synchronisation. This mode is practicable for cross-docking by means of bulk-breaking or consolidation of shipments. A heuristic approach based on the Lagrangian relaxation and subgradient optimisation is developed for the non-linear mixed-general integer optimisation model, which is illustrated by numerical examples and tested using a benchmark data set.

在任意配送中心（distribution centre, DC）中，选择对入站与出站物流流实施交叉配送（cross-docking）协同调度，或是通过持有库存将两类流程解耦，该决策对供应链绩效具有显著影响。而该决策的关键驱动因素，恰恰是构成物流流的离散批量规模。据此，我们构建了一款原创性优化模型，用于在给定恒定弧流量（arc flows）的前提下，确定最优订单批量决策，以最小化链路端的订购成本与在途库存成本，以及各配送中心处的缓冲库存成本。该模型采用平均吞吐量作为替代指标，用以估算那些不适合采用协同调度、因此需将入站与出站流程解耦的设施的缓冲库存水平。完美的批量对批量（lot-for-lot）货物匹配会对供应链运营施加极强的约束条件，而平均吞吐量相等则代表一种创新性的宽松协同调度模式。该模式可通过拆零拼箱（bulk-breaking）或货物整合（consolidation）的方式应用于交叉配送场景。针对该非线性混合整数优化模型，我们开发了一种基于拉格朗日松弛（Lagrangian relaxation）与次梯度优化（subgradient optimisation）的启发式求解方法，并通过数值算例对该方法进行了演示，同时采用基准数据集开展了测试验证。