Year: 2024
Type: dissertation
Abstract: In the rapidly evolving domain of supply chain management, optimising operations for efficiency and reliability is essential.Traditional methods have struggled to keep pace with the complexity of modern logistics networks and the vast amount of data they generate.However, recent advancements in artificial intelligence offer a new avenue for innovation in this field.This research focuses on the application of Phi models, a series of small language models by Microsoft, to address two tasks in supply chain optimisation: code generation for what-if analysis and the job shop scheduling problem.We show that fine-tuning the Phi-2 model enhances its performance on these tasks, demonstrating a marked improvement in code generation capabilities with a BLEU score of 98.6%.Though it encounters challenges with the job shop scheduling problem due to its inherent complexity, it still displays a novel understanding of the problem and how to solve it, attaining a BLEU score of 21.7% using a whitespace tokenizer.
Authors: Beke, Axel, Zitouni, Théodore
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7065949328