Dataless Neural Networks for Resource-Constrained Project Scheduling

• URL: http://arxiv.org/abs/2507.05322v1
• Date: Mon, 07 Jul 2025 15:59:57 GMT
• Title: Dataless Neural Networks for Resource-Constrained Project Scheduling
• Authors: Marc Bara,
• Abstract summary: This paper presents the first dataless neural network approach for the Resource-Constrained Project Scheduling Problem (RCPSP)<n>We provide a complete mathematical framework that transforms discrete scheduling constraints into differentiable objectives suitable for gradient-based optimization.<n>We detail the mathematical formulation for both precedence and renewable resource constraints, including a memory-efficient dense time-grid representation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dataless neural networks represent a paradigm shift in applying neural architectures to combinatorial optimization problems, eliminating the need for training datasets by encoding problem instances directly into network parameters. Despite the pioneering work of Alkhouri et al. (2022) demonstrating the viability of dataless approaches for the Maximum Independent Set problem, our comprehensive literature review reveals that no published work has extended these methods to the Resource-Constrained Project Scheduling Problem (RCPSP). This paper addresses this gap by presenting the first dataless neural network approach for RCPSP, providing a complete mathematical framework that transforms discrete scheduling constraints into differentiable objectives suitable for gradient-based optimization. Our approach leverages smooth relaxations and automatic differentiation to unlock GPU parallelization for project scheduling, traditionally a domain of sequential algorithms. We detail the mathematical formulation for both precedence and renewable resource constraints, including a memory-efficient dense time-grid representation. Implementation and comprehensive experiments on PSPLIB benchmark instances (J30, J60, and J120) are currently underway, with empirical results to be reported in an updated version of this paper.

Related papers

• Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming [0.0]
  Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets.<n>We propose a hybrid optimization method that combines Unconstrained Binary Quadratic Programming (UBQP) with Gradient Descent (SGD) to accelerate CNN training.<n>Our approach achieves a 10--15% accuracy improvement over a standard BP-CNN baseline while maintaining similar execution times.
  arXiv  Detail & Related papers  (2025-05-30T21:25:31Z)
• Neural Pathways to Program Success: Hopfield Networks for PERT Analysis [0.0]
  This paper presents a novel formulation of PERT scheduling as an energy minimization problem within a Hopfield neural network architecture.<n> Numerical simulations on synthetic project networks comprising up to 1000 tasks demonstrate the viability of this approach.
  arXiv  Detail & Related papers  (2025-05-08T08:34:16Z)
• An Expectation-Maximization Algorithm-based Autoregressive Model for the Fuzzy Job Shop Scheduling Problem [12.862865254507177]
  The fuzzy job shop scheduling problem (FJSSP) emerges as an innovative extension to the job shop scheduling problem (JSSP)<n>This paper investigates the feasibility of employing neural networks to assimilate and process fuzzy information for the resolution of FJSSP.
  arXiv  Detail & Related papers  (2025-01-11T10:20:16Z)
• Differentiable Quadratic Optimization For The Maximum Independent Set Problem [23.643727259409744]
  We show that pCQO-MIS scales only with the number nodes in graph, not number edges.<n>Our experimental results demonstrate the effectiveness of the proposed method compared to exact, sampling and data-centric approaches.
  arXiv  Detail & Related papers  (2024-06-27T21:12:48Z)
• Implicit Generative Prior for Bayesian Neural Networks [8.013264410621357]
  We propose a novel neural adaptive empirical Bayes (NA-EB) framework for complex data structures. The proposed NA-EB framework combines variational inference with a gradient ascent algorithm. We demonstrate the practical applications of our framework through extensive evaluations on a variety of tasks.
  arXiv  Detail & Related papers  (2024-04-27T21:00:38Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks [94.85780721466816]
  A novel framework for proactive caching is proposed in this paper. It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
  arXiv  Detail & Related papers  (2021-08-15T21:32:47Z)
• Optimal Transport Based Refinement of Physics-Informed Neural Networks [0.0]
  We propose a refinement strategy to the well-known Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs) based on the concept of Optimal Transport (OT) PINNs solvers have been found to suffer from a host of issues: spectral bias in fully-connected pathologies, unstable gradient, and difficulties with convergence and accuracy. We present a novel training strategy for solving the Fokker-Planck-Kolmogorov Equation (FPKE) using OT-based sampling to supplement the existing PINNs framework.
  arXiv  Detail & Related papers  (2021-05-26T02:51:20Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)
• 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)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)

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.