Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics

• URL: http://arxiv.org/abs/2507.21701v1
• Date: Tue, 29 Jul 2025 11:25:13 GMT
• Title: Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
• Authors: Florian Krellner, Abhishek Awasthi, Nico Kraus, Sarah Braun, Michael Poppel, Daniel Porawski,
• Abstract summary: We show that quantum-classical metaheuristics can benefit from a new way of modeling mathematical optimization problems.<n>We present a detailed performance comparison of the two solution methods for each optimization model.
• Score: 0.3806074545662052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study evaluates the performance of a quantum-classical metaheuristic and a traditional classical mathematical programming solver, applied to two mathematical optimization models for an industry-relevant scheduling problem with autonomous guided vehicles (AGVs). The two models are: (1) a time-indexed mixed-integer linear program, and (2) a novel binary optimization problem with linear and quadratic constraints and a linear objective. Our experiments indicate that optimization methods are very susceptible to modeling techniques and different solvers require dedicated methods. We show in this work that quantum-classical metaheuristics can benefit from a new way of modeling mathematical optimization problems. Additionally, we present a detailed performance comparison of the two solution methods for each optimization model.

Related papers

• Direct comparison of stochastic driven nonlinear dynamical systems for combinatorial optimization [0.0]
  Combinatorial optimization problems are ubiquitous in industrial applications.<n>Tremendous effort has been devoted to developing solvers for Ising-type problems over the past decades.<n>Recent advances in controlling and manipulating both quantum and classical systems have enabled novel computing paradigms.
  arXiv  Detail & Related papers  (2025-03-19T17:08:55Z)
• Benchmarking of quantum and classical SDP relaxations for QUBO formulations of real-world logistics problems [0.4636927061010061]
  We provide a vast experimental study of semidefinite programming relaxations of Quadratic unconstrained binary optimization problems.<n>We test on QUBO reformulations of industry-based instances of the (open) vehicle routing problem and the (affinity-based) slotting problem.
  arXiv  Detail & Related papers  (2025-03-13T18:51:45Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• Optimized QUBO formulation methods for quantum computing [0.4999814847776097]
  We show how to apply our techniques in case of an NP-hard optimization problem inspired by a real-world financial scenario. We follow by submitting instances of this problem to two D-wave quantum annealers, comparing the performances of our novel approach with the standard methods used in these scenarios.
  arXiv  Detail & Related papers  (2024-06-11T19:59:05Z)
• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization [59.386153202037086]
  Predict-Then- framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
  arXiv  Detail & Related papers  (2023-11-22T01:32:06Z)
• Towards Constituting Mathematical Structures for Learning to Optimize [101.80359461134087]
  A technique that utilizes machine learning to learn an optimization algorithm automatically from data has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approach is widely applicable, the learned model can overfit and may not generalize well to out-of-distribution test sets. We propose a novel L2O model with a mathematics-inspired structure that is broadly applicable and generalized well to out-of-distribution problems.
  arXiv  Detail & Related papers  (2023-05-29T19:37:28Z)
• Quantum Inspired Optimization for Industrial Scale Problems [0.5417521241272644]
  We use a quantum-inspired model-based optimization method TN-GEO to assess the efficacy of these quantum-inspired methods when applied to realistic problems. In this case, the problem of interest is the optimization of a realistic assembly line based on BMW's currently utilized manufacturing schedule. Through a comparison of optimization techniques, we found that quantum-inspired model-based optimization, when combined with conventional black-box methods, can find lower-cost solutions in certain contexts.
  arXiv  Detail & Related papers  (2023-05-03T15:19:36Z)
• Neural Improvement Heuristics for Graph Combinatorial Optimization Problems [49.85111302670361]
  We introduce a novel Neural Improvement (NI) model capable of handling graph-based problems where information is encoded in the nodes, edges, or both. The presented model serves as a fundamental component for hill-climbing-based algorithms that guide the selection of neighborhood operations for each.
  arXiv  Detail & Related papers  (2022-06-01T10:35:29Z)
• Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems [101.18253437732933]
  We develop a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate unconstrained binary optimization problems. We showcase the framework's performance on two inverse design problems by optimizing thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering.
  arXiv  Detail & Related papers  (2021-05-06T02:22:23Z)
• Quantum variational optimization: The role of entanglement and problem hardness [0.0]
  We study the role of entanglement, the structure of the variational quantum circuit, and the structure of the optimization problem. Our numerical results indicate an advantage in adapting the distribution of entangling gates to the problem's topology. We find evidence that applying conditional value at risk type cost functions improves the optimization, increasing the probability of overlap with the optimal solutions.
  arXiv  Detail & Related papers  (2021-03-26T14:06:54Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)

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.