Chance-constrained multi-objective programming for optimal multi-layer aquifer remediation design

Abstract

Groundwater remediation works are expensive, complex in nature, and generally involve uncertain design parameters and multiple conflicting objectives. Reduction in remediation cost is, normally, the primary objective of optimal remediation designs. Remediation time has significant influence on remediation cost. The main contributions of this study are: development of a chance-constrained multi-objective programming (CCMOP) model, exploration of the significance of flexible remediation time on optimal remediation designs, and exploration of some basic characteristics of multi-objective optimization algorithms. The developed methodology excludes inferior yet optimal solutions from the set of possible solutions, identifies the ranges in which non-inferior solutions lie, and prevents one from making inferior decisions. It provides insightful information about the problem and simplifies the decision making process considerably. The results suggest that by avoiding the use of faulty remediation times, highly significant amounts of remediation cost could be saved. The CCMOP is an effective uncertainty and reliability prediction technique.