Planning of Truck Platooning for Road-Network Capacitated Vehicle Routing Problem

TL;DR

This paper develops a new optimization framework and solution algorithm for truck platooning in capacitated vehicle routing problems on road networks, aiming to minimize total operation costs while satisfying delivery time windows.

Contribution

It introduces the first model integrating truck platooning with a road-network CVRPTW, including a tailored solution algorithm and a comprehensive operation plan.

Findings

The proposed model effectively minimizes total costs.

The solution algorithm demonstrates computational efficiency.

Truck platooning yields significant cost savings.

Abstract

Truck platooning, a linking technology of trucks on the highway, has gained enormous attention in recent years due to its benefits in energy and operation cost savings. However, most existing studies on truck platooning limit their focus on scenarios in which each truck can serve only one customer demand and is thus with a specified origin-destination pair, so only routing and time schedules are considered. Nevertheless, in real-world logistics, each truck may need to serve multiple customers located at different places, and the operator has to determine not only the routing and time schedules of each truck but also the set of customers allocated to each truck and their sequence to visit. This is well known as a capacitated vehicle routing problem with time windows (CVRPTW), and considering the application of truck platooning in such a problem entails new modeling frameworks and tailored solution algorithms. In light of this, this study makes the first attempt to optimize the truck platooning plan for a road-network CVRPTW to minimize the total operation cost, including vehicles' fixed dispatch cost and energy cost, while fulfilling all delivery demands within their time window constraints. Specifically, the operation plan will dictate the number of trucks to be dispatched, the set of customers, and the routing and time schedules for each truck. In addition, the modeling framework is constructed based on a road network instead of a traditional customer node graph to better resemble and facilitate the platooning operation. A 3-stage algorithm embedded with a "route-then-schedule" scheme, dynamic programming, and modified insertion heuristic, is developed to solve the proposed model in a timely manner. Numerical experiments are conducted to validate the modeling framework, demonstrate the performance of the proposed solution algorithm, and quantify the benefit of truck platooning.