Self-Labeling the Job Shop Scheduling Problem

• URL: http://arxiv.org/abs/2401.11849v3
• Date: Thu, 31 Oct 2024 11:33:24 GMT
• Title: Self-Labeling the Job Shop Scheduling Problem
• Authors: Andrea Corsini, Angelo Porrello, Simone Calderara, Mauro Dell'Amico,
• Abstract summary: We show that generative models can be trained by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label. We prove the robustness of SLIM to various parameters and its generality by applying it to the Traveling Salesman Problem.
• Score: 15.723699332053558
• License:
• Abstract: This work proposes a self-supervised training strategy designed for combinatorial problems. An obstacle in applying supervised paradigms to such problems is the need for costly target solutions often produced with exact solvers. Inspired by semi- and self-supervised learning, we show that generative models can be trained by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label. In this way, we iteratively improve the model generation capability by relying only on its self-supervision, eliminating the need for optimality information. We validate this Self-Labeling Improvement Method (SLIM) on the Job Shop Scheduling (JSP), a complex combinatorial problem that is receiving much attention from the neural combinatorial community. We propose a generative model based on the well-known Pointer Network and train it with SLIM. Experiments on popular benchmarks demonstrate the potential of this approach as the resulting models outperform constructive heuristics and state-of-the-art learning proposals for the JSP. Lastly, we prove the robustness of SLIM to various parameters and its generality by applying it to the Traveling Salesman Problem.

Related papers

• On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning [85.75164588939185]
  We study the discriminative probabilistic modeling problem on a continuous domain for (multimodal) self-supervised representation learning. We conduct generalization error analysis to reveal the limitation of current InfoNCE-based contrastive loss for self-supervised representation learning.
  arXiv  Detail & Related papers  (2024-10-11T18:02:46Z)
• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling [0.0]
  This study proposes a different approach that integrates gradient-based update through continuous relaxation, combined with Quasi-Quantum Annealing (QQA) Numerical experiments demonstrate that our method is a competitive general-purpose solver, achieving performance comparable to iSCO and learning-based solvers.
  arXiv  Detail & Related papers  (2024-09-02T12:55:27Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient deep ensemble method for self-attention networks. By employing a single pre-trained self-attention network with weights shared across all members, we train member-specific low-rank matrices for the attention projections. Our method exhibits superior calibration compared to explicit ensembles and achieves similar or better accuracy across various prediction tasks and datasets.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Leader Reward for POMO-Based Neural Combinatorial Optimization [8.301694061287565]
  We propose Leader Reward to enhance the model's ability to generate optimal solutions. We demonstrate that Leader Reward greatly improves the quality of the optimal solutions generated by the model.
  arXiv  Detail & Related papers  (2024-05-22T19:27:03Z)
• Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization [15.842155380912002]
  This work proposes a novel Instance-Conditioned Adaptation Model (ICAM) for better large-scale generalization of neural optimization. In particular, we design a powerful yet lightweight instance-conditioned Routing adaptation module for the NCO model. We develop an efficient three-stage reinforcement learning-based training scheme that enables the model to learn cross-scale features without any labeled optimal solution.
  arXiv  Detail & Related papers  (2024-05-03T08:00:19Z)
• Self-Improved Learning for Scalable Neural Combinatorial Optimization [15.842155380912002]
  This work proposes a novel Self-Improved Learning (SIL) method for better scalability of neural optimization. We develop an efficient self-improved mechanism that enables direct model training on large-scale problem instances without any labeled data. In addition, we design a linear attention complexity mechanism for the computation model to efficiently handle large-scale problem instances with low overhead.
  arXiv  Detail & Related papers  (2024-03-28T16:46:53Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization [6.935838847004389]
  Combinatorial optimization problems (COPs) on the graph with real-life applications are canonical challenges in Computer Science. The underlying principle of this approach is to deploy a graph neural network (GNN) for encoding both the local information of the nodes and the graph-structured data. We use the security-aware phone clone allocation in the cloud as a classical quadratic assignment problem (QAP) to investigate whether or not deep RL-based model is generally applicable to solve other classes of such hard problems.
  arXiv  Detail & Related papers  (2021-08-08T19:12:04Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

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.