Quantum Annealing Approaches to Solving the Shipment Rerouting Problems
- URL: http://arxiv.org/abs/2501.05624v1
- Date: Thu, 09 Jan 2025 23:47:23 GMT
- Title: Quantum Annealing Approaches to Solving the Shipment Rerouting Problems
- Authors: Fei Li, Arul Rhik Mazumder, Max Zhao,
- Abstract summary: We study a shipment rerouting problem (SRP) which generalizes many NP-hard sequencing and packing problems.
The objective is to select a set of trucks and to schedule these trucks' routes so that the total cost is minimized.
We use novel mathematical programming formulations and new insights into solving sequencing and packing problems simultaneously.
- Score: 7.888128236684232
- License:
- 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.
Related papers
- A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation [61.08720171136229]
Coalition structure generation is a fundamental computational problem in multiagent systems.
We develop SALDAE, a multiagent path finding algorithm for CSG that operates on a graph of coalition structures.
arXiv Detail & Related papers (2025-02-14T15:21:27Z) - Quantum Annealing based Hybrid Strategies for Real Time Route Optimization [0.0]
We propose a method to aid classical computers in solving problems faster while reducing complexity.
We employ two algorithms: Hybrid Two Step (H2S) and Hybrid Three Step (H3S)
Both algorithms produce promising results, both in terms of solution time and solution cost.
arXiv Detail & Related papers (2024-11-21T14:45:40Z) - Qubit efficient quantum algorithms for the vehicle routing problem on
NISQ processors [48.68474702382697]
Vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry.
In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables.
arXiv Detail & Related papers (2023-06-14T13:44:35Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Quantum Neural Networks for a Supply Chain Logistics Application [0.0]
We investigate one such hybrid algorithm on a problem of substantial importance: vehicle routing for supply chain logistics with multiple trucks and complex demand structure.
We use reinforcement learning with neural networks with embedded quantum circuits.
We find results comparable to human truck assignment.
arXiv Detail & Related papers (2022-11-30T15:35:53Z) - Supply Chain Logistics with Quantum and Classical Annealing Algorithms [0.0]
Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance.
We investigate a problem of substantial commercial value, multi-truck vehicle routing for supply chain logistics, at the scale used by a corporation in their operations.
Our work gives a set of techniques that can be adopted in contexts beyond vehicle routing to apply NISQ devices in a hybrid fashion to large-scale problems of commercial interest.
arXiv Detail & Related papers (2022-05-09T17:36:21Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Analysis of Vehicle Routing Problem in Presence of Noisy Channels [0.0]
Vehicle routing problem (VRP) is an NP-hard optimization problem.
This work builds a basic VRP solution for 3 and 4 cities using variational quantum eigensolver on a variable ANSATZ.
arXiv Detail & Related papers (2021-12-28T10:20:42Z) - Deep Policy Dynamic Programming for Vehicle Routing Problems [89.96386273895985]
We propose Deep Policy Dynamic Programming (D PDP) to combine the strengths of learned neurals with those of dynamic programming algorithms.
D PDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions.
We evaluate our framework on the travelling salesman problem (TSP) and the vehicle routing problem (VRP) and show that the neural policy improves the performance of (restricted) DP algorithms.
arXiv Detail & Related papers (2021-02-23T15:33:57Z) - A Quantum Annealing Approach for Dynamic Multi-Depot Capacitated Vehicle
Routing Problem [5.057312718525522]
This paper presents a quantum computing algorithm that works on the principle of Adiabatic Quantum Computation (AQC)
It has shown significant computational advantages in solving optimization problems such as vehicle routing problems (VRP) when compared to classical algorithms.
This is an NP-hard optimization problem with real-world applications in the fields of transportation, logistics, and supply chain management.
arXiv Detail & Related papers (2020-05-26T01:47:39Z) - Reinforcement Learning Based Vehicle-cell Association Algorithm for
Highly Mobile Millimeter Wave Communication [53.47785498477648]
This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks.
We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem.
The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
arXiv Detail & Related papers (2020-01-22T08:51:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.