Doctoral Dissertations

Date of Award

8-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Industrial Engineering

Major Professor

Dr. Andrew Yu

Committee Members

Dr. John E. Kobza, Dr. James L. Simonton, Dr. Qiang He

Abstract

Previous research only focused on an unrelated parallel machine scheduling problem with setup and processing resources. However, some manufacturing environments, such as plastic injection molding, need different sequential and parallel processes before the facility can process jobs in the machines. For example, some raw materials are hygroscopic, and a dryer must remove moisture before being processed in the injection molding machine. These dryers are portrayed as parallel machines. The job rather than the machine determines the drying time. Once the drying stage is complete and the raw materials are transferred to the actual machines to run jobs, the scheduling problem becomes an unrelated parallel machine scheduling problem with multiple setup and processing activities. This study focuses on building a practical model considering drying requirements, machine eligibility, and assembly requirements. The problem is modeled using mixed-integer linear programming. The objective of this study is to minimize the makespan. The setup and processing times are machine- and job-dependent in the unrelated parallel machines. A limited number of resources are available to share. The number of resources needed also depends on the job-machine pair. Setup resources like mold hangers are employed to disassemble and reassemble molds while setup technicians prepare the machine for processing. Operators then execute the jobs. Some of these jobs are necessary for an assembly. The assembly jobs also necessitate the use of a machine. The commercial solver Gurobi can solve the smaller instances. In contrast, a two-phase algorithm is required for instances of greater size since previous research has shown these types of scheduling problems are NP-hard.

Download
Files over 3MB may be slow to open. For best results, right-click and select "save as..."

Share

COinS