Resource-Based Time and Cost Prediction in Project Networks: From Statistical Modeling to Graph Neural Networks

• URL: http://arxiv.org/abs/2511.15003v1
• Date: Wed, 19 Nov 2025 00:56:12 GMT
• Title: Resource-Based Time and Cost Prediction in Project Networks: From Statistical Modeling to Graph Neural Networks
• Abstract summary: This study proposes a novel resource-based predictive framework that integrates network representations of project activities with graph neural networks (GNNs)<n>The proposed GNN framework achieves an average 23 to 31 percent reduction in mean absolute error compared to traditional regression and tree-based methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of project duration and cost remains one of the most challenging aspects of project management, particularly in resource-constrained and interdependent task networks. Traditional analytical techniques such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) rely on simplified and often static assumptions regarding task interdependencies and resource performance. This study proposes a novel resource-based predictive framework that integrates network representations of project activities with graph neural networks (GNNs) to capture structural and contextual relationships among tasks, resources, and time-cost dynamics. The model represents the project as a heterogeneous activity-resource graph in which nodes denote activities and resources, and edges encode temporal and resource dependencies. We evaluate multiple learning paradigms, including GraphSAGE and Temporal Graph Networks, on both synthetic and benchmark project datasets. Experimental results show that the proposed GNN framework achieves an average 23 to 31 percent reduction in mean absolute error compared to traditional regression and tree-based methods, while improving the coefficient of determination R2 from approximately 0.78 to 0.91 for large and complex project networks. Furthermore, the learned embeddings provide interpretable insights into resource bottlenecks and critical dependencies, enabling more explainable and adaptive scheduling decisions.

Related papers

• Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs [81.39865232674413]
  Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation.<n>This work establishes theoretical results for transferability of GNNs over graphs derived from sparse Random Geometric Graphs (RGGs)
  arXiv  Detail & Related papers  (2026-06-02T15:42:14Z)
• Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning [0.8890833546984916]
  This paper presents a novel approach to neural network pruning by integrating a graph-based observation space into an AutoML framework.<n>Our framework transforms the pruning process by introducing a graph representation of the target neural network.<n>For the action space we transition from continuous pruning ratios to fine-grained binary action spaces.
  arXiv  Detail & Related papers  (2025-09-04T15:05:05Z)
• A Pre-training Framework for Relational Data with Information-theoretic Principles [57.93973948947743]
  We introduce Task Vector Estimation (TVE), a novel pre-training framework that constructs supervisory signals via set-based aggregation over relational graphs.<n>TVE consistently outperforms traditional pre-training baselines.<n>Our findings advocate for pre-training objectives that encode task heterogeneity and temporal structure as design principles for predictive modeling on relational databases.
  arXiv  Detail & Related papers  (2025-07-14T00:17:21Z)
• Dataless Neural Networks for Resource-Constrained Project Scheduling [0.0]
  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.
  arXiv  Detail & Related papers  (2025-07-07T15:59:57Z)
• Generative QoE Modeling: A Lightweight Approach for Telecom Networks [6.473372512447993]
  This study introduces a lightweight generative modeling framework that balances computational efficiency, interpretability, and predictive accuracy.<n>By validating the use of Vector Quantization (VQ) as a preprocessing technique, continuous network features are effectively transformed into discrete categorical symbols.<n>This VQ-HMM pipeline enhances the model's capacity to capture dynamic QoE patterns while supporting probabilistic inference on new and unseen data.
  arXiv  Detail & Related papers  (2025-04-30T06:19:37Z)
• Transfer Learning Under High-Dimensional Network Convolutional Regression Model [20.18595334666282]
  We propose a high-dimensional transfer learning framework based on network convolutional regression ( NCR)<n>Our methodology includes a two-step transfer learning algorithm that addresses domain shift between source and target networks.<n> Empirical evaluations, including simulations and a real-world application using Sina Weibo data, demonstrate substantial improvements in prediction accuracy.
  arXiv  Detail & Related papers  (2025-04-28T16:52:28Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Learning Latent Graph Structures and their Uncertainty [63.95971478893842]
  We show that minimizing point-prediction losses does not guarantee proper learning of latent relational information.<n>We propose a sampling-based method that solves this joint learning task.
  arXiv  Detail & Related papers  (2024-05-30T10:49:22Z)
• Can Graph Learning Improve Planning in LLM-based Agents? [61.47027387839096]
  Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs) In this paper, we explore graph learning-based methods for task planning, a direction that is to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs.
  arXiv  Detail & Related papers  (2024-05-29T14:26:24Z)
• Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks [86.99017195607077]
  We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a wireless network with statistically-identical agents.<n>Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies.
  arXiv  Detail & Related papers  (2024-04-04T06:24:11Z)
• Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm [87.47506806135746]
  In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. It is shown that the proposed joint resource management over the pre-training and fine-tuning stages well balances the system performance trade-off.
  arXiv  Detail & Related papers  (2024-04-01T00:21:11Z)
• DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks [0.9896984829010892]
  This paper investigates the computational resource demands of 29 classical deep neural networks and builds accurate models for predicting computational costs. We propose a lightweight prediction approach DNNAbacus with a novel network structural matrix for network representation. Our experimental results show that the mean relative error (MRE) is 0.9% with respect to time and 2.8% with respect to memory for 29 classic models, which is much lower than the state-of-the-art works.
  arXiv  Detail & Related papers  (2022-05-24T14:21:27Z)
• Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [79.28094304325116]
  Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
  arXiv  Detail & Related papers  (2020-11-14T11:09:51Z)

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.