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Multistage Hierarchical Fuzzy Decision-Making for Improved Sustainable Development of Industrial Zones

Cristina Piluso and Yinlun Huang. Wayne State University, 5050 Anthony Wayne Dr., Detroit, MI 48202

Industrial sustainability is a vital issue in pursuing the long-term sustainable development of a given industrial zone, in which the improvement of the efficiency of material and energy usage becomes beneficial to the sustainable development of an industrial system. The size and scope of an industrial sustainability problem is large, therefore, sustainable decision-making, which involves decisions at the local, regional, or national levels, often involves complex and ill-defined parameters with a high degree of uncertainty due to incomplete understanding of the underlying issues.[1] In order for such improvements to be successfully implemented within an industrial zone, the industrial leaders must possess accurate decision-making abilities.

This work will propose the use of a multistage hierarchical fuzzy logic-based decision-making methodology, to provide the necessary information to industrial level decision-makers, for improved decision-making abilities. The fuzzy logic-based method aids in the determination and selection of various decisions that must be implemented in order to achieve an improved state of sustainable development in the future. The methodology can be applied in a forward time direction, i.e. given the current state of sustainability and the implementation of various decisions at multiple time intervals (i.e. multistage) into the future, what will be the new state of sustainable development years into the future. The methodology can also be implemented in a backward time direction, i.e. given the current state of sustainability within an industrial zone and a target sustainable development goal some years into the future, what decisions must be executed and when, in order to realize the pre-determined goal. Utilization of the methodology will provide decision-makers with a higher level of confidence in the fact that their decisions will result in a state of improved sustainable development, throughout their industrial zone, over time. This methodology is computationally very efficient, suitable for analyzing the sustainability status of any system, or predicting the system 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 within an industrial region, and determine recommendations for future policies and actions that would increase the values of the indicators identified as promoting or decrease the values of those identified as impeding sustainability can be made.

[1] Andriantiatsaholiniaina, L. A., V. S. Kouikoglou, and Y. A. Phillis. Evaluating Strategies for Sustainable Development: Fuzzy Logic Reasoning and Sensitivity Analysis. Ecol. Econ. 2004, 48, 149-172.