383884 Multistage Fuzzy Decision-Making for Sustainability Performance Improvement

Wednesday, November 19, 2014: 9:45 AM
International B (Marriott Marquis Atlanta)
Hao Song, Liwei Yan and Yinlun Huang, Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, MI

Sustainability is a vital issue in pursuing the short-to-long-term development of industrial organizations and systems.  One of the main challenges in developing development strategies and plans is how to deal with uncertainties that could appear in various stages of a decision making process.  The uncertainties can be in general classified into two types: aleatory and epistemic.  Aleatory uncertainty refers to the inherent variation associated with the physical system or the environment under consideration, and it is objective and irreducible.  By contrast, epistemic uncertainty is caused by the lack of knowledge or information, and it is subjective and reducible.  Fuzzy logic based arithmetic appears an effective method to handle uncertainties associated with various types of sustainability problems.  Fuzzy logic theory is a mathematical system in which rigorous logical mathematics are used to deal with uncertain information and data that are difficult to compute using conventional mathematics.

In this paper, we introduce a mathematical framework for multistage hierarchical fuzzy logic-based decision-making when solutions need to be derived for sustainability performance improvement.  It aids in the determination and selection of decisions in different decision stages that must be implemented in order to achieve an improved state of sustainable development in the future.  The methodology can be applied in two scenarios: forward and backward decision making.  In forward decision making, for a given state of sustainability and known resources available, it is to determine how to achieve the most sustainable state at multiple time intervals (i.e. multistage) into the future.  In backward decision making, for a sustainability goal set to achieve in a target time frame, it is to determine the best development pathway from a given current stage of sustainability at the lowest cost.  This methodology is computationally very efficient, suitable for analyzing the sustainability status of any system, or predicting the system short-to-long-term behavior in the scope of sustainability under uncertainty.  The methodology has been applied to an automotive focused industrial zone case study to quantify and analyze the state of industrial sustainability, and determine recommendations for future development that would increase the values of the indicators identified as promoting or decrease the values of those identified as impeding sustainability can be made.


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