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

TL;DR

This paper introduces a stochastic optimization model for vehicle routing with uncertain demand and time windows, aiming to minimize costs in logistics delivery scenarios, validated on benchmark and real-world data.

Contribution

It develops a novel stochastic integer programming approach for VRP with demand uncertainty and time windows, extending beyond deterministic models.

Findings

The proposed model effectively reduces delivery costs under demand uncertainty.

Validation on benchmark and Singapore data demonstrates practical applicability.

The approach outperforms traditional deterministic routing methods.

Abstract

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.