A cost function approximation method for dynamic vehicle routing with docking and LIFO constraints

TL;DR

This paper introduces a cost function approximation approach combined with variable neighborhood search to efficiently solve a dynamic vehicle routing problem with docking and LIFO constraints, outperforming existing methods.

Contribution

It develops a novel cost function approximation method with a new local search operator for dynamic routing with docking and LIFO constraints, improving solution quality.

Findings

Significantly outperforms current state-of-the-art methods.

Effectively handles real-time request arrivals and docking constraints.

Demonstrates robustness on benchmark datasets.

Abstract

In this paper, we study a dynamic pickup and delivery problem with docking constraints. There is a homogeneous fleet of vehicles to serve pickup-and-delivery requests at given locations. The vehicles can be loaded up to their capacity, while unloading has to follow the last-in-first-out (LIFO) rule. The locations have a limited number of docking ports for loading and unloading, which may force the vehicles to wait. The problem is dynamic since the transportation requests arrive real-time, over the day. Accordingly, the routes of the vehicles are to be determined dynamically. The goal is to satisfy all the requests such that a combination of tardiness penalties and traveling costs is minimized. We propose a cost function approximation based solution method. In each decision epoch, we solve the respective optimization problem with a perturbed objective function to ensure the solutions remain adaptable to accommodate new requests. We penalize waiting times and idle vehicles. We propose a variable neighborhood search based method for solving the optimization problems, and we apply two existing local search operators, and we also introduce a new one. We evaluate our method using a widely adopted benchmark dataset, and the results demonstrate that our approach significantly surpasses the current state-of-the-art methods.