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Access Type
WSU Access
Date of Award
January 2020
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Chemical Engineering and Materials Science
First Advisor
Yinlun Huang
Abstract
Depletion of nonrenewable resources, global warming, environmental pollution, excessive population growth, etc., have challenged industries globally. Sustainable development has become a focal point of future development of manufacturing industries to address these challenges. Sustainable manufacturing has two important aspects: (i) triple-bottom-line-balanced manufacturing, and (ii) continuous efforts for progressive improvement. While the former reflects the nature of sustainability that is a complex multi-objective optimization problem, the latter is a non-conventional dynamic control problem. In short, sustainable manufacturing is a multi-objective dynamic control problem. Although many useful methodologies have been developed to support decision making for sustainable chemical manufacturing, they only focus on the first aspect which is a multi-objective optimization problem. Thus, a very important research direction is to study how system control science can be effectively used to guide the sustainability study in a holistic way. In the first study, we formulate sustainability analysis problems as a general vector-based analysis problem, where the sustainability vector is characterized by the degree of system status change and the direction in a three-dimensional (3D) sustainability space. As a sustainability improvement process involves a series of sustainability state transitions, we apply a vector analysis technique to capture key features of state transition options. In addition, we introduce an algorithm to streamline the analysis tasks in a systematic way. Sustainability performance improvement may involve multiple stages to keep the system on a sustainable trajectory. Thus, this is a multistage decision problem, which is very challenging process, especially due to the existence of uncertainties that appear in the available data. In the second study, we introduce a mathematical framework for optimal process sustainability performance enhancement. In this framework, we describe four types of optimization problems, which are defined based on decision makers’ different objectives for sustainability enhancement. Improvement of manufacturing sustainability involves a series of system (performance) state transitions in a sustainable development (SD) space. From the standpoint of control engineering, this is a multi-objective system control problem. In this study, we introduce a general decision-support framework for deriving strategies to achieve short-to-long-term sustainability goals. In this framework, a two-layered hierarchical control scheme is introduced to implement strategic and tactical control for dynamic sustainability control. In the last study, we introduce a decision support framework for sustainable and smart manufacturing. The introduced framework is equipped with a data analytics block that is responsible for both sustainability assessment and monitoring of external factors that may affect the sustainability performance of the system in its transition process. The framework can provide a structured step-by-step guide to decision makers in formulating sustainability strategies and deriving and updating sustainability strategic plans intelligently.
Recommended Citation
Moradi Aliabadi, Majid, "Sustainability Analytics And Decision Making For Sustainable Manufacturing" (2020). Wayne State University Dissertations. 3425.
https://digitalcommons.wayne.edu/oa_dissertations/3425