自提点选址是企业推广顾客自提模式和抢占末端配送市场份额的战略决策之一。与其它物流设施不同，自提点直接面向顾客，顾客可选择是否接受区域中自提点的服务。为使顾客自提量最大化，考虑顾客如何选择目标自提点是至关重要的。为此，考虑顾客取货距离和自提点吸引力等因素，基于竞争选址和逐渐覆盖理论，设计了顾客对自提点的分段效用函数。在此基础上，利用MNL模型和需求弹性函数分别构建了随机选择模型和最优选择模型。为高效地求解模型，设计了免疫算法，并用一个随机算例进行验证。结果显示，顾客选择行为对自提点选址有重要的影响，企业在作决策之前有必要详实地调查顾客的选择行为。
Last mile delivery (LMD) problems have become increasingly prominent with the rapid development of e-commerce. And traditional home delivery service can't meet customers' demand. Pickup service provides customers with a more flexible distribution solution, gaining scale benefit of LMD and reducing the delivery pressure of the city logistics system. Pickup point location is a strategic decision for enterprises to broaden customers' pickup mode and promote market share of LMD. In contrast with other logistics facilities, pickup point is a direct terminal to customers, and customers have a choice in whether to receive service from pickup point in the region or not. In order to maximize the customers' total pickup demand, it is necessary to put emphasis on how customers choose the pickup points to patronize. To this end, assuming that customers' pickup distance and pickup points' attraction are two important attractiveness attributes considered by customers. Based on the competitive location and gradual coverage theory, a piecewise utility function between customer and pickup point is constructed. Under this circumstance, two alternative models are respectively presented with the MNL model and demand elasticity function:in the "probabilistic-choice model", a customer may patronize each pickup point with a certain probability, which increases with the attractiveness of available pickup point. In contrast, the "optimal-choice model" stipulates that each customer will go to the most attractive pickup point. Both models are formulated as a mixed-integer program. To solve the problems efficiently, an immune algorithm is proposed and a random example is used for verification. The results show that pickup point's coverage distance, customer rational degree and demand elasticity coefficient affect the location decisions. In particular, the comparison between the two models indicates that the choice behavior of the customer has a significant impact on the pickup point location decisions, and a thorough empirical investigation of this behavior prior to choosing a model is necessary.
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