# Optimal Stochastic Delivery Planning in Full-Truckload and Less-Than-Truckload Delivery

arXiv: https://arxiv.org/abs/1802.08540 · HTML: https://mlsanity.com/paper/1802.08540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing](https://mlsanity.com/paper/2607.16875/) (2026)

## Abstract (arXiv)

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.