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

2026-01-28

AI Summary

This work addresses the combinatorial optimization challenges—specifically the Job Shop Scheduling Problem (JSP) and the Knapsack Problem (KP)—faced in enterprise resource planning (ERP) systems for iron-titanium alloy manufacturing. We propose the first unified, multi-type Transformer framework tailored to real-world ERP environments, integrating diverse attention mechanisms to enable end-to-end modeling and solving of heterogeneous combinatorial problems, thereby replacing conventional heuristic approaches. The proposed method demonstrates competitive performance on standard JSP and KP benchmarks and, more importantly, validates its effectiveness and practical utility in real-world iron-titanium alloy production scenarios through empirical scheduling optimization.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Job-Shop Scheduling Problem
• Knapsack Problem
• Enterprise Resource Planning
• Combinatorial Optimization
• Ferro-Titanium Industry

Innovation

• Multi-Type Transformer
• Job-Shop Scheduling Problem
• Knapsack Problem
• Enterprise Resource Planning
• Combinatorial Optimization