Brill, Ed (1979). Use of Optimization Models in Public-Sector Planning, Management Science, 25(5), 413-422.
In this article, Brill how multi-objective optimization models can be used for planning in the public sector. He says that multi-objective programming is descended from single-objective optimization; the result of both is to calculate "the solution". For many public sector problems, there isn't necessarily "a solution", even when using multi-objective analysis. This is because these problems are very often "wicked" problems, as described by Leibman (for more information, refer to my discussion of the article in taak een). With public sector problems, quantifying values can be very difficult. Multi-objective programming can be used for these problems in order to look at some of the possible solutions and to help encourage human creativity, but in the end there is no one "solution" for public sector problems.
I agree with Brill that public sector problems often have a number of stakeholders who often have different valuations for the different objectives. This idea is very similar, in fact nearly identical to, the ideas presented by Leibman in the article discussed earlier in the semester. I agree that malty-objective analysis could be useful for determining possible solutions. In general, this article struck me as a restatement of the Leibman article which includes some discussion of multi-objective analysis.
Pan TC, Kao JJ (2009) GA-QP Model to Optimize Sewer System Design, Journal of Environmental Engineering, 135(1) 17-24
Efficient design of sewer systems is important since they require such a large capital investment. The complex hydraulics makes optimization of a sewer system difficult. This paper discusses using quadratic programming and a genetic algorithm as methods for finding a number of possible solutions. Since these optimizations can’t include all the issues associated with the sewer system since many of these issues are unquantifiable, the paper makes a point to say that it is vital to produce a number of good solutions via GA and QP which can then be judged against the unquantifiable constraints.
For this paper, the authors incorporated a quadratic program into their genetic algorithm. Two constraints were used—the pipes had to be large enough to meet demand, and pipe diameter must increase downstream. Comparing their results to piecewise linearization and dynamic programming, the authors found that the GA-QP method they used found more feasible solutions and were more temporally efficient.
This was an interesting paper. The concepts herein discussed might suit sanitary sewer studies for certain scales of subdivisions. I wonder whether we could work with similar concepts to linearize the piecewise functions in our Delaware Project to implement GA-QP.