Genetic Algorithm Approaches for Addressing Unmodeled Objectives in Optimization Problems

Abstract

Q 2001 OPA (Omam Publihm M a t i o n ) N.V. Published by b Undn the Gordon and B d Science Fnblilhcn imprint.Public sector decision-making typically involves complex problems that are often not completely understood. In these problems, there are invariably unmodeled issues that can greatly impact the acceptability of solutions. Modeling to Generate Alternatives (MGA) is an approach for addressing unmodeled issues in an optimization context. MGA techniques are used to generate a small number of good, yet very different, solutions to optimization problems. Because these solutions are different in decision space, they may differ considerably in performance when unmodeled objectives an considered. Many problems are sufiiciently complex that traditional optimization solution procedures, and therefore traditional MGA techniques, are not readily applicable. Two techniques for performing MGA using genetic algorithms (GAS) arc investigated and compared. One of these techniques, which uses specialized MGA operators. is shown to produce solutions that an both better in quality and more different. This technique is also demonstrated for a realistic air quality management problem.

Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning

“…Consequently, public sector environmental policy formulation proves to be an extremely complicated and challenging task. While mathematically optimal solutions can provide the best results to the modelled problems, they are frequently not the best solutions to the underlying real problems as there are invariably unquantified issues and unmodelled objectives not apparent at the time the models were constructed [1,2,5]. This is a familiar concern in public sector settings where final decisions tend to be shaped not only by quantified objectives, but also by stakeholder preferences and socio-economic/ political objectives that are extremely subjective in nature.…”

“…In response to this option creation requirement, several approaches collectively referred to as modelling-to-generate-alternatives (MGA) have been developed [5,[8][9][10][11][12]. The primary motivation behind MGA is to produce a manageably small set of alternatives that are good with respect to modelled objectives yet as different as possible from each other in the decision space.…”

Modelling to generate alternatives in waste management facility expansion planning via simulation-optimization

“…In order to properly motivate the following SO MGA procedure, it is necessary to provide a more formal definition of the goals of an MGA process (Loughlin et al, 2001;Gunalay et al, 2012). Suppose the optimal solution to an original mathematical model is X* with objective value Z* = F(X*).…”

“…In response to this option creation requirement, several approaches collectively referred to as modelling-to-generate-alternatives (MGA) have been developed (Loughlin et al, 2001). The primary motivation behind MGA is to produce a manageably small set of alternatives that are good with respect to modelled objective(s) yet are as different as possible from each other in the decision space.…”

Using Niched Co-Evolution Strategies to Address Non-Uniqueness in Characterizing Sources of Contamination in a Water Distribution System

“…A Genetic Algorithm for Modeling-to-Generate Alternatives (GAMGA) was explored by Loughlin et al (2001). GAMGA used a genetic algorithm with the maximal difference function to generate alternatives.…”