Date of Award

August 2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Engineering

First Advisor

Matthew Petering

Committee Members

Matthew Petering, Kaan Kuzu, Jaejin Jang, Christine Cheng, Hamid Seifoddini

Abstract

The job shop scheduling problem is a difficult problem to solve, and it is also difficult to implement solutions found in research into real shops. In this research, a methodology is proposed to develop schedules for real shops. The methodology utilizes a genetic algorithm to select dispatching rules for each machine cell and accesses these schedules through a simulation optimization framework. The simulation framework allows for the study of random elements including variable job processing times and random machine breakdowns. This creates a robust schedule that is easy to understand, and therefore implement, while scaling to large, real-world job shops. To gain additional efficiencies, a novel methodology is proposed to classify the graphs which represent different types of shop environments, with a graph neural network, to pre-seed the initial population of the genetic algorithm. This process allows the system to leverage pre-existing knowledge of similar shops to reduce the number of generations required to reach a reasonable solution.

Download

Share

COinS