Optimal Stochastic Delivery Planning in Full-Truckload and Less-Than-Truckload Delivery
This addresses delivery planning under uncertainty for logistics businesses, but it is incremental as it extends existing deterministic VRPPC models to include stochastic elements.
The paper tackles the Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) by introducing a stochastic version with random demand and hard time windows, called Optimal Delivery Planning (ODP), which minimizes total delivery cost while meeting constraints, and it is evaluated using benchmark datasets and real Singapore road map data.
With an increasing demand from emerging logistics businesses, Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been introduced to manage package delivery services from a supplier to customers. However, almost all of existing studies focus on the deterministic problem that assumes all parameters are known perfectly at the time when the planning and routing decisions are made. In reality, some parameters are random and unknown. Therefore, in this paper, we consider VRPPC with hard time windows and random demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to minimize the total package delivery cost while meeting the customer time window constraints. We use stochastic integer programming to formulate the optimization problem incorporating the customer demand uncertainty. Moreover, we evaluate the performance of the ODP using test data from benchmark dataset and from actual Singapore road map.