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Author ORCID Identifier
https://orcid.org/0009-0005-5209-9801
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Industrial Engineering & Operations Research
Year Degree Awarded
2023
Month Degree Awarded
May
First Advisor
Ana Muriel
Second Advisor
Chaitra Gopalappa
Third Advisor
Senay Solak
Subject Categories
Industrial Engineering | Operational Research
Abstract
In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation.
DOI
https://doi.org/10.7275/34368754
Recommended Citation
Mallach, Ron, "A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers" (2023). Doctoral Dissertations. 2829.
https://doi.org/10.7275/34368754
https://scholarworks.umass.edu/dissertations_2/2829
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.