Quantum Annealing Approaches to Solving the Shipment Rerouting Problems

TL;DR

This paper explores quantum annealing methods for solving the NP-hard shipment rerouting problem, demonstrating that quantum approaches can find near-optimal solutions more efficiently than classical algorithms in certain scenarios.

Contribution

It introduces novel quantum annealing algorithms and mathematical formulations for the shipment rerouting problem, advancing the application of quantum computing in logistics optimization.

Findings

Quantum annealing achieves near-optimal solutions faster than classical algorithms.

New mathematical programming formulations improve problem-solving efficiency.

Quantum methods show promise in practical logistics scenarios.

Abstract

In this paper, we study a shipment rerouting problem (SRP) which generalizes many NP-hard sequencing and packing problems. A SRP's solution has ample practical applications in vehicle scheduling and transportation logistics. Given a network of hubs, a set of goods must be delivered by trucks from their source-hubs to their respective destination-hubs. The objective is to select a set of trucks and to schedule these trucks' routes so that the total cost is minimized. The problem SRP is NP-hard; only classical approximation algorithms have been known for some of its NP-hard variants. In this work, we design classical algorithms and quantum annealing algorithms for this problem with various capacitated trucks. The algorithms that we design use novel mathematical programming formulations and new insights into solving sequencing and packing problems simultaneously. Such formulations take advantage of network infrastructure, shipments, and truck capacities. We conduct extensive experiments showing that in various scenarios, the quantum annealing solver generates near-optimal or optimal solutions much faster than the classical algorithm solver.