A Robust Optimization Method for Location Selection of Parcel Lockers under Uncertain Demands

Abstract

Parcel lockers have continuously growing in popularity as an alternative mode for last-mile delivery services due to their capability of effectively alleviating the risk of a delivery failure, increasing the possibility of delivery consolidation, and reducing the number of drop-off sites. However, poorly located of parcel lockers be less efficient. When determining the parcel locker location, inadequate consideration of uncertain demands can potentially increase the risk of unsatisfied demands. To remedy this issue, a robust optimization model is proposed in this paper with consideration of the demand uncertainties, including the large and small parcels to be received and sent. Not only can the collection locations be optimally determined, but so can the number of large and small parcel lockers for each location at the same time under various robust levels. Meanwhile, the sites whose demands are covered by one of the collection locations are also determined by the constraints of acceptable walking distance. A series of numerical experiments has been performed to evaluate the proposed model, with the main focus being on the solution robustness. Since the set of non-linear constraints are transformed into the linear counterparts, the robust solution can be obtained by the existing solvers within a reasonable time with moderate computing power. The experimental results also provide useful guidance for the practical application of the method, as slightly more conservative decision making can secure the solution robustness with only a marginal increase in costs.

Geometrical Parcel Locker Network Design with Consideration of Users’ Preferences as a Solution for Sustainable Last Mile Delivery

“…Only by knowing the distribution of preferences can one decide to meet the needs of a given part of society. In this research, the range of the parcel lockers that satisfied 75% of users was 300 m and for 90%, it was 100 m, when, in other studies, values adopted arbitrarily were 250 m [17] and 250 × 500 m [19], and the value determined by an experiment was 150 m [20].…”

“…Y. Wang et al [20] In the model, 150 m was determined as the maximum distance to reach a parcel locker on foot. The location and size of parcel lockers could be determined by a single model.…”

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

“…Scott et al [23] proposed two models related to the design of the UAV medical service network to address the location decisions, which can promote more efficient emergency medical delivery to save more lives. Wang et al [24] studied the location selection of parcel lockers under uncertain demands and gained a highly robust solution. Pan et al [18] studied the location of UAV logistics hubs in rural areas, which reduced the cost of logistics and accelerated the mass application of UAV logistics.…”

Optimization of Asset and Liability Management of Banks with Minimum Possible Changes

“…At the end, for future research, the effects of data ambiguity and uncertainty in asset-liability management can be considered [134][135][136][137][138][139][140][141][142]. For this purpose, the popular and applicable uncertain programming approaches such as fuzzy optimization and robust optimization [165][166][167][168][169][170][171][172][173][174][175][176][177][178][179][180][181] can be used to deal with uncertainty of data.…”