Re-route Package Pickup and Delivery Planning with Random Demands

TL;DR

This paper introduces stochastic demand-based planning systems for vehicle routing, improving efficiency and customer service in dynamic logistics scenarios with uncertain demands.

Contribution

It proposes two novel systems, PDPSD and Re-route PDPSD, incorporating stochastic demands and re-routing algorithms into vehicle routing problems.

Findings

PDPSD achieves lower costs than baseline models.

Re-route PDPSD enables serving more customers during ongoing deliveries.

Systems validated with benchmark and real-world data.

Abstract

Recently, a higher competition in logistics business introduces new challenges to the vehicle routing problem (VRP). Re-route planning, also known as dynamic VRP, is one of the important challenges. The re-route planning has to be performed when new customers request for deliveries while the delivery vehicles, i.e., trucks, are serving other customers. While the re-route planning has been studied in the literature, most of the existing works do not consider different uncertainties. Therefore, in this paper, we propose two systems, i.e., (i) an offline package pickup and delivery planning with stochastic demands (PDPSD) and (ii) a re-route package pickup and delivery planning with stochastic demands (Re-route PDPSD). Accordingly, we formulate the PDPSD system as a two-stage stochastic optimization. We then extend the PDPSD system to the Re-route PDPSD system with a re-route algorithm. Furthermore, we evaluate performance of the proposed systems by using the dataset from Solomon Benchmark suite and a real data from a Singapore logistics 1company. The results show that the PDPSD system can achieve the lower cost than that of the baseline model. In addition, the Re-route PDPSD system can help the supplier efficiently and successfully to serve more customers while the trucks are already on the road.