Profit-maximizing parcel locker location problem under threshold Luce model

Abstract

The growth of e-commerce has created increasing complexity in logistics services. To remain competitive, logistics and e-commerce companies are exploring new modes as supplements to traditional home delivery, one of which is the self-service parcel locker. This paper studies a parcel locker location problem where a company plans to introduce the locker service by locating locker facilities to attract customers. The objective is to maximize the profit, accounting for the revenue and the cost of facilities, under the competition of other delivery modes. To estimate the revenue, we use the threshold Luce model (TLM) to predict customers’ likelihood of using the locker service. We then propose a combinatorial optimization model and develop exact solution methodologies that are practically implementable according to our extensive computational experiments. In effect, our modeling framework generalizes the traditional facility location problems based on the binomial logit model (BNL) and the multinomial logit model (MNL), both of which impose strong and strict assumptions on the customer’s choice sets. That is, they assume that the choice set will either contain only one facility or all facilities. In our numerical experiment, we demonstrate that using the BNL and the MNL could lead to, respectively, pessimistic and optimistic revenue estimation. Consequently, the suggested location decisions will be either conservative or aggressive. Our proposed model, by contrast, can effectively relax these assumptions. Our results also reveal that the aggressive decision due to the use of the MNL will incur an unnecessarily high facility cost that cannot be compensated by the additional revenue, leading to profit loss that could be significant in various scenarios. Finally, we conduct sensitivity analysis to shed light on how the relative attraction of locker services (compared to other delivery options) affects the location decisions and highlight the importance of calibrating the input parameters in TLM.