A Dynamic Pricing Strategy for a 3PL Provider with Heterogeneous Customers
A Dynamic Pricing Strategy for a 3PL Provider with Heterogeneous Customers
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DOI：
10.1016/j.ijpe.2015.07.017
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Ray Zhang;B. R. Nault;Yiliu (Paul) Tu
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文献类型：
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作者：
Ray Zhang;B. R. Nault;Yiliu (Paul) Tu
研究了一个提供仓储和运输服务的第三方物流供应商的定价问题。当客户到达第三方物流供应商时，他们指定了货物的交付日期，在指定的交付日期之前，他们的货物被存放在第三方物流供应商的仓库中。我们提出了一个动态定价策略（DPS），并开发了一个随机非线性规划（SNLP）模型，该模型计算不同交货日期的最优运费率，将第三方物流供应商的当前持有成本和每条路线的可用运输能力。由于客户对交付日期的估值和价格敏感性是异质的，并且第三方物流供应商不知道客户交付日期偏好的分布，因此我们修改了标准的多项logit（MNL）函数来预测客户的选择。通过仿真实验，我们表明，建议的MNL函数可以很好地替代混合MNL函数时，混合MNL函数是不适用的。通过仿真，我们还比较了建议DPS与静态定价策略。我们表明，与我们的DPS的第三方物流供应商和它的客户都更好，和第三方物流供应商有不同的投资动机，以增加运输能力。我们的研究结果也可以应用于类似的设置，功能持有成本，有限的生产能力和交货日期敏感的客户。
We study the pricing problem for a third-party-logistics (3PL) provider that provides ware-housing and transportation services. When customers arrive at the 3PL provider, they specify the delivery dates for their freight, and before the specified delivery dates, their freight is stocked in the 3PL provider׳s warehouse. We propose a dynamic pricing strategy (DPS) and develop a stochastic-nonlinear-programming (SNLP) model which computes the optimal freight rates for different delivery dates incorporating the 3PL provider׳s current holding cost and available transportation capacity for each route. As customers are heterogeneous in their valuations and price sensitivities for delivery dates, and the distributions of the customers׳ delivery date preferences are unknown to the 3PL provider, we modify the standard multinomial logit (MNL) function to predict customer choices. Through a simulation experiment, we show that the proposed MNL function can be a good replacement for the mixed MNL function when the mixed MNL function is not applicable. Through simulation we also compare the proposed DPS with a static pricing strategy. We show that with our DPS both the 3PL provider and its customers are better off, and the 3PL provider has different investment incentives for increasing transportation capacity. Our results can be also applied in similar settings that feature holding costs, limited production capacity and delivery-date-sensitive customers.