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<p>This study focuses on the distribution operations problem encountered in the context of multiproduct, multiperiod sales under demand uncertainty in a capacitated retail network for omnichannel retailers (o-tailers). By implementing the <i>ship-from-store</i> strategy, all the stores within the network can fulfil orders across channels within inventory constraints with the aim of maximizing the o-tailer’s profit. At the beginning of each period, the o-tailer determines the replenishment quantity for each product at the DC and the allocation quantity from the DC to each store. At the end of each period, the o-tailer decides whether to fulfil realized online orders from the DC, specific stores, or both simultaneously. To address this problem, we developed a multiperiod stochastic model and employed a <i>linear decision rule</i> (LDR) approach, informed by strong duality theory, to adaptively determine the <i>replenishment</i>, <i>allocation</i>, and <i>fulfilment</i> quantities. The experimental results demonstrate that the LDR consistently generates high-quality solutions, including efficiency gaps less than 8.46% in comparison with the benchmark expected value given perfect information (EVPI) across various cases. Additionally, a sensitivity analysis confirms that omnichannel fulfilment remains beneficial for retailers across different parameter settings.</p>

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Joint optimization of omni-channel distribution framing (replenishment, allocation, and fulfilment) under demand uncertainty

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Shuangpeng Yang,
  • Qiang Guo,
  • Chao Liang

摘要

This study focuses on the distribution operations problem encountered in the context of multiproduct, multiperiod sales under demand uncertainty in a capacitated retail network for omnichannel retailers (o-tailers). By implementing the ship-from-store strategy, all the stores within the network can fulfil orders across channels within inventory constraints with the aim of maximizing the o-tailer’s profit. At the beginning of each period, the o-tailer determines the replenishment quantity for each product at the DC and the allocation quantity from the DC to each store. At the end of each period, the o-tailer decides whether to fulfil realized online orders from the DC, specific stores, or both simultaneously. To address this problem, we developed a multiperiod stochastic model and employed a linear decision rule (LDR) approach, informed by strong duality theory, to adaptively determine the replenishment, allocation, and fulfilment quantities. The experimental results demonstrate that the LDR consistently generates high-quality solutions, including efficiency gaps less than 8.46% in comparison with the benchmark expected value given perfect information (EVPI) across various cases. Additionally, a sensitivity analysis confirms that omnichannel fulfilment remains beneficial for retailers across different parameter settings.