The Use of Optimization Models in Public-Sector Planning

Abstract

When applied to public-sector planning, traditional least-cost optimization models and their offspring, contemporary multiobjective models, have often been developed under the optimistic philosophy of obtaining "the answer." Frequently, such models are not very useful because there is a multitude of local optima, which result from wavy indifference functions, and because important planning elements are not captured in the formulations. Omitted elements, in fact, may imply that an optimal planning solution lies within the inferior region of a multiobjective analysis instead of along the noninferior frontier. The role of optimization methods should be re-thought in full recognition of these limitations and of the relevant planning process. They should be used to generate planning alternatives and to facilitate their evaluation and elaboration; they should also be used to provide insights and serve as catalysts for human creativity. As illustrated by recent examples, these roles may require the use of several models as well as new types of optimization formulations and modified algorithms and computer codes.government, optimization models, planning, policy analysis

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

“…MGA was first developed as a method to assist human decision making through mathematical programming (Brill 1979). Alternatives generation is beneficial to a problem containing non-uniqueness because other possible solutions are identified; therefore addressing the problem by providing multiple solutions.…”

Applications of Simulation-Optimization Methods in Environmental Policy Planning under Uncertainty

“…After optimizing an initial problem formulation, the deterministic Hop, Skip, and Jump (HSJ) MGA technique creates supplementary problem instances by systematically adding target constraints on both the objective function value and the decision variables to force the generation of solution alternatives (Brill, 1979;Brill et al, 1981). Huang et al (1996b) combined GP with HSJ modelling to construct a procedure referred to as the Grey, Hop, Skip and Jump (GHSJ) method.…”

“…In response to this option-generation requirement, several approaches collectively referred to as modelling-to-generatealternatives (MGA) have been developed Brill, 1979;Brill et al, 1981;Chang et al, 1980Chang et al, , 1982Church and Huber, 1979;Falkenhausen, 1979;Gidley and Bari, 1986;Rubenstein-Montano and Zandi, 1999;Rubenstein-Montano et al, 2000). The goal for all MGA methods is to create an optimal solution together with a set of several near-optimal alternatives (Gidley and Bari, 1986).…”

Are the “Best” Solutions to a Real Optimization Problem Always Found in the Noninferior Set? Evolutionary Algorithm for Generating Alternatives (EAGA)

“…Exploring the inferior region is important to make better decisions. As described in [2], the Modeling to Generate Alternatives (MGA) approach implements a systematic exploration to generate a small number of alternative solutions that are good within the modeled objective space while being maximally different in the decision space. A target constraint in the objective value is specified to allow search in a small region of the non-inferior space.…”