Enterprise Resource Planning Using Multi-type Transformers in Ferro-Titanium Industry

TL;DR

This paper introduces a novel Multi-Type Transformer approach for solving complex combinatorial optimization problems like JSP and KP, demonstrating competitive results and real-world industrial application in Ferro-Titanium manufacturing.

Contribution

It is the first to apply multi-type transformers to real manufacturing problems, unifying solutions for JSP and KP within a single framework.

Findings

MTT achieves competitive performance on benchmark datasets.

Multi-type attention effectively models complex industrial problems.

Successful application demonstrated in Ferro-Titanium industry.

Abstract

Combinatorial optimization problems such as the Job-Shop Scheduling Problem (JSP) and Knapsack Problem (KP) are fundamental challenges in operations research, logistics, and eterprise resource planning (ERP). These problems often require sophisticated algorithms to achieve near-optimal solutions within practical time constraints. Recent advances in deep learning have introduced transformer-based architectures as promising alternatives to traditional heuristics and metaheuristics. We leverage the Multi-Type Transformer (MTT) architecture to address these benchmarks in a unified framework. We present an extensive experimental evaluation across standard benchmark datasets for JSP and KP, demonstrating that MTT achieves competitive performance on different size of these benchmark problems. We showcase the potential of multi-type attention on a real application in Ferro-Titanium industry. To the best of our knowledge, we are the first to apply multi-type transformers in real manufacturing.