Neural Pathways to Program Success: Hopfield Networks for PERT Analysis

TL;DR

This paper introduces a novel neural network approach using Hopfield networks to solve PERT scheduling problems, enabling scalable and near-optimal project scheduling under uncertainty.

Contribution

It formulates PERT scheduling as an energy minimization problem within a Hopfield neural network, addressing theoretical issues and demonstrating scalability with synthetic data.

Findings

Achieves near-optimal makespans in synthetic networks

Demonstrates scalability to 1000 tasks

Maintains minimal constraint violations

Abstract

Project and task scheduling under uncertainty remains a fundamental challenge in program and project management, where accurate estimation of task durations and dependencies is critical for delivering complex, multi project systems. The Program Evaluation and Review Technique provides a probabilistic framework to model task variability and critical paths. In this paper, the author presents a novel formulation of PERT scheduling as an energy minimization problem within a Hopfield neural network architecture. By mapping task start times and precedence constraints into a neural computation framework, the networks inherent optimization dynamics is exploited to approximate globally consistent schedules. The author addresses key theoretical issues related to energy function differentiability, constraint encoding, and convergence, and extends the Hopfield model for structured precedence graphs. Numerical simulations on synthetic project networks comprising up to 1000 tasks demonstrate the viability of this approach, achieving near optimal makespans with minimal constraint violations. The findings suggest that neural optimization models offer a promising direction for scalable and adaptive project tasks scheduling under uncertainty in areas such as the agentic AI workflows, microservice based applications that the modern AI systems are being built upon.