A subjectivity-interval-based optimization model for solid waste management under uncertainty

In this study, a subjectivity-interval-based fuzzy robust programming (SIFRP) is developed and applied to the planning of solid waste management systems (SWMS) under uncertainty. Through the integration of the existing interval linear programming (ILP) and fuzzy robust programming (FRP) methods, the SIFRP model can explicitly address system uncertainties with multiple presentations and take decision maker's subjectivity/confidence variations into consideration, facilitating the reflection of weak-or strong-confidence gradients over various subjective judgments in the process of decision making. Relevant parameters in the SIFRP model can be represented as intervals and interval-valued fuzzy sets, thus multiple uncertainties can be directly communicated into the optimization process and resulting solution. Highly uncertain information arising from simultaneous appearance of fuzziness and vagueness for the parameters can also be effectively addressed through the integration of ILP and FRP methods into a general optimization framework. Moreover, subjectivity/confidence degrees over defining parameters can be effectively handled through the introduction of interval-valued fuzzy sets, leading to enhanced robustness of the optimization modeling process. Results of the case study indicate that useful solutions for planning solid waste management practices have been generated, indicating that decision maker's subjective judgments can be directly incorporated within the model formulation and solution process. The results also suggest that the proposed hybrid methodology is applicable to practical problems that are associated with highly complex and uncertain information.