Multi-Objective Decision-Making for Environmental Remediation

EPA Grant Number: R826614
Title: Multi-Objective Decision-Making for Environmental Remediation
Investigators: Mayer, Alex , Enfield, Carl , Horn, Jeff
Current Investigators: Mayer, Alex , Horn, Jeff
Institution: Michigan Technological University , U. S. Environmental Protection Agency , Northern Michigan University
Current Institution: Michigan Technological University , Northern Michigan University
EPA Project Officer: Chung, Serena
Project Period: September 1, 1998 through August 31, 2001
Project Amount: $253,571
RFA: Decision-Making and Valuation for Environmental Policy (1998) RFA Text |  Recipients Lists
Research Category: Environmental Justice


Selecting the optimal design for a soil or groundwater remediation strategy is an enormous challenge for decision makers (Dms), due to the number of potential alternatives, the complexity of contaminated subsurface environments, and the need to weigh conflicting objectives such as risk and cost. Simulation/optimization models have been applied to remediation design, but current approaches do not allow for multi-objective optimization. The objective of this project is to develop, apply and test new algorithms to solve multi-objective groundwater remediation problems, with the goal of creating a new set of tools for DMs involved in groundwater remediation problems.


The work will focus on the design of remediation technologies with the objectives of minimizing cost, risk, and time. Algorithm development will build upon ongoing remediation optimization work by the PI which utilizes the genetic algorithm (GA). First, algorithms will be developed for producing tradeoff curves, or surfaces, consisting of solutions that are optimal with respect to a least one objective. DMs will be able to examine the tradeoff curves and select a solution or solutions, based on their judgments as to what tradeoffs are acceptable. These algorithms will utilize a new technique pioneered by the co-investigator, known as Niched Pareto GA. Second, new algorithms will allow the DM to determine the importance of competing objectives in a given situation. An iterative process will be used to guide the DM towards towards a preferred weighting or ranking of the multiple objectives. At each iteration, optimal solutions will be obtained using combinations of single-objective GAs. Lastly, a series of test problems based on real sites will be developed and used to evaluate and compare the performance of each algorithm.

Expected Results:

The proposed project will result in a software tool for aiding DMs who must balance multiple, conflicting objectives in the design of remediation systems. It is expected that multi-objective optimization will result in remediation designs that are significantly less expensive than those provided by traditional design approaches. In previous approaches where optimization has been used for remediation system design, cleanup goals were specified as static constraints. This work will involve the direct incorporation of risk assessment into the remediation design process. The DM will be able to view the full range of potential remediation designs in terms of the risk they would impose, while weighing the risk against estimated cost and cleanup time.

Publications and Presentations:

Publications have been submitted on this project: View all 15 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 2 journal articles for this project

Supplemental Keywords:

decision making, soil, groundwater, remediation, optimization, RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Waste, Hydrology, Remediation, decision-making, Ecological Risk Assessment, Ecology and Ecosystems, Groundwater remediation, Economics & Decision Making, multi-objective decision making, public resources, preservation priorities, economic benefits, decision making, cost benefit, groundwater remediation strategy, public values, public policy, contaminated subsurface environment, cost effectiveness, benefits assessment

Progress and Final Reports:

  • 1999
  • 2000
  • Final Report