Development of Fractional Programming Methods for Environmental Management Under Uncertainty

Abstract

Rapid economic development and population growth has accelerated environmental degradation and resource scarcity. There is growing recognition of the importance of environmental conservation and sustainable development. Sustainable environmental management may benefit from integrating a variety of factors into decision-making processes, such as economic, environmental, social, technical, legislative, and political considerations. Moreover, environmental systems are often involved in a multitude of uncertainties, which significantly intensifies the complexity of systems analysis. In this thesis, a set of fractional programming methods are developed to solve multiobjective environmental management problems under uncertainties. Factorial analysis is introduced to explore the interactions of uncertain system parameters and quantify their interactive effects on system performance. The proposed methods include (1) an inexact fractional credibility-constrained programming method for sustainable municipal solid waste management, (2) a generalized fuzzy fractional programming method for air quality management, and (3) an inexact mixed-integer sequential factorial fractional programming method for sustainable municipal solid waste management in the City of Regina, Canada. The models can reflect the multi-objective characteristic of environmental management and address the conflicts between economic and environmental objectives without weighing them. The proposed models are able to maximize environmental benefits and obtain maximum system efficiency with minimal system costs. The objective is to optimize the ratio of environmental benefits to system costs rather than considering them separately, which provides a practical way to solve efficiency issues in environmental management. Moreover, the interactions obtained with factorial analysis may reveal implicit interrelationships between uncertain parameters and help decision makers gain insight into a complex environmental system.