A Study on Sharing Logistics Network Design Considering Demand Uncertainty.

The evolution of e-retailing is driving a rise in logistics costs and risk of late delivery. Collaborative logistics has become the key to help businesses eliminate inefficiencies, improve responsiveness to market changes, and reduce overall supply chain costs by adjusting transportation capacity efficiently. In this study, we propose a stochastic mixed integer linear programming model that incorporates shipper’s transportation operations via truck sharing service. The model supports strategic network design decisions in uncertain market environments by optimizing the number of trucks under uncertain demand. Through several case studies on a small-scale truck sharing network, we show the influence of demand uncertainty on the network performance in terms of the on-time delivery ratio and the gross profit margin ratio. We also show the influence of the sharing platform features such as the transaction price and the number of available trucks, on shippers as well as a platformer in terms of the turnover of trucks.