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.