Quantum Annealing based Hybrid Strategies for Real Time Route Optimization

• URL: http://arxiv.org/abs/2412.02720v1
• Date: Thu, 21 Nov 2024 14:45:40 GMT
• Title: Quantum Annealing based Hybrid Strategies for Real Time Route Optimization
• Authors: Sushil Mario, Pavan Teja Pothamsetti, Louie Antony Thalakottor, Trisha Vishwanath, Sanjay H. A, Anees Ahmed, Salvatore Sinno, Shruthi Thuravakkath, Sinthuja M,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: One of the most well-known problems in transportation and logistics is the Capacitated Vehicle Routing Problem (CVRP). It involves optimizing a set of truck routes to service a set of customers, subject to limits on truck capacity, to reduce travel costs. The biggest challenge faced whilst attempting to solve the issue is that the time complexity of the issue grows exponentially with the number of customers and trucks, rendering it virtually intractable to traditional computers and algorithms. In this paper, we propose a method to circumvent this limitation, employing quantum computers to aid classical computers in solving problems faster while reducing complexity. To obtain our results, we employ two algorithms: Hybrid Two Step (H2S) and Hybrid Three Step (H3S). Both algorithms involve two phases: clustering and routing. It has been observed that both algorithms produce promising results, both in terms of solution time and solution cost.

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 Approaches to Solving the Shipment Rerouting Problems [7.888128236684232]
  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.
  arXiv  Detail & Related papers  (2025-01-09T23:47:23Z)
• 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)
• QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines [81.4597482536073]
  Quantum approximate optimization algorithms (QAOAs) utilize the power of quantum machines and inherit the spirit of adiabatic evolution. We propose QAOA-in-QAOA ($textQAOA2$) to solve arbitrary large-scale MaxCut problems using quantum machines. Our method can be seamlessly embedded into other advanced strategies to enhance the capability of QAOAs in large-scale optimization problems.
  arXiv  Detail & Related papers  (2022-05-24T03:49:10Z)
• 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)
• Applying quantum approximate optimization to the heterogeneous vehicle routing problem [0.7503129292751939]
  We investigate the potential use of a quantum computer to find approximate solutions to the heterogeneous vehicle routing problem. We find that the number of qubits needed for this mapping scales quadratically with the number of customers.
  arXiv  Detail & Related papers  (2021-10-13T15:38:25Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• A Three-Stage Algorithm for the Large Scale Dynamic Vehicle Routing Problem with an Industry 4.0 Approach [3.6317403990273402]
  Industry 4.0 is a concept which concentrates on mobility and real-time integration. The aim of this research is to solve large-scale DVRP (LSDVRP)
  arXiv  Detail & Related papers  (2020-08-26T10:39:36Z)
• 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.