Dataless Neural Networks for Resource-Constrained Project Scheduling

TL;DR

This paper introduces the first dataless neural network approach for Resource-Constrained Project Scheduling, transforming discrete constraints into differentiable objectives to enable gradient-based optimization and GPU parallelization.

Contribution

It extends dataless neural network methods to RCPSP, providing a mathematical framework and differentiable formulation for scheduling constraints.

Findings

Framework for encoding scheduling constraints into neural networks

Enables GPU-accelerated optimization for RCPSP

Preliminary experiments on benchmark instances are underway

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.