Assignment 10

Brill Ed (1979) Use of Optimization Models in Public-Sector Planning, Management Science, 25.

Summary
This article discusses the multiple shortcomings of optimization models utilized in public-sector planning. The ones that the author mainly pinpoint is the economic optimization models. He explained that these “economic efficiency” models only find local optima and not the global, and that the optimal solution actually lies in the inferior region of the analysis.

Models also do not consider distribution of income nor equity. Furthermore, these models have issues with estimated benefit and cost, however these subjects can be very subjective and the values can be biased depending on the stakeholder. The author said that due to the various members involved with the outcome of the public-planning model, these models should be used as an aid and not the actual solution, meaning to use the model along with a simulation model. These models can first find a preliminary planning solution using optimization, and then the solution can be observed using simulation.

He explains parametric analysis as a way to express objectives as constraints or weights assigned to objectives in order to perform a multiobjective analysis. However, his issue with this analysis is that if there are numerous objective, a complete set of trade-off relationships can be unachievable.

The author, however, included that not all important elements of the model can be captured accurately and precisely so. He also said that truly optimal solutions are very much likely to lie in the inferior region, as opposed to the noninferior frontier.

Discussion
This article sums up the problems with all public sector models. Nothing has changed since the article was written in 1979, and I don’t think it will be changing in the future. Even though computer modeling techniques have becoming more advanced with time, the subjectivity of the models placed by individuals will never be able to be modeled. There will always be a principle of indifference amongst representative stakeholders in the modeling process; there will always be different weights on multiple objective functions, etc. Economy for the nation will always be the first priority on political decision makers’ agenda, so there will always be a bias in some way placed upon these public-sector models.


Pan TC, Kao JJ (2009) GA-QP Model to Optimize Sewer System Design, Journal of Environmental Engineering, 135(1) 17-24.

Summary
This article explains an optimization model created by Tze-Chin Pan and Jehng-Jung Kao in order to design a system of sanitary sewer. The constraints included in the model were: minimum velocity requirements for particle movement, maximum velocity requirements for scour, elevation requirements for upstream and downstream pipes, diameter constraints held by commercial producers, minimum depth below ground for safety reaons, and diamaeter constrains for downstream pipes to be larger than/equal to that of the upstream pipe.

The authors used a GA algorithm that used a sort of code that included model parameters, as opposed to parameters alone. This way, the model searches for multiple points as opposed to a single point. The GA algorithm can be combined with a linear program or quadratic program. The advantage of using a quadratic program over linear is that quadratic program will work better with a GA because of the nonlinearity of the GA function. The authors chose to use the GA algorithm along with quadratic programming modeling software.

Multiple design inputs were not included in the model due to limitations of the modeling software. The main inputs that were not included were construction, geologic, traffic, public preference, and land availability. The authors concluded that since these inputs weren’t included in the model, the solution that they obtained may not be the best or even feasible solution. Nevertheless, the model created by the authors designed the system by decreasing the constructions costs, and the solution was considered satisfactory by Pan and Kao.

Discussion
It was interesting to read an article that discussed an optimization model that can be used in the practical civil engineering world. However, the authors did explain the multiple inputs that weren’t introduced for the modeling formation. I believe this is why I haven’t noticed optimization modeling being used for sanitary sewer designs in practice. There are so many special inputs for each heterogeneous design system in which it would be very time-consuming to create a new model for each situation. On the other hand, it seems possible to produce a generic modelwhere there can be user-defined inputs special to the design consideration/charactieristics of the system. If I were to look further into the research of a sanitary sewer system, this is the modeling technique that I would try to follow.

Assignment 9

Shiau JT, Wu FC (2006) Compromise programming methodology for determining instream flow under multiobjective water allocation criteria, Journal of the American Water Resources Planning and Management, 42(5), pg. 1179-1191

Summary
This report discusses at multi-objective water allocation criteria approach which determines instream flows. The case study of this methodology was the Kaoping diversion weir located in Taiwan. This research was unique in that it was considered one of the few studies that studies the compromises necessary to human water demand in order meet instream flow requirements on a quantitative level.

The objective of this report was to design the diversion weir to create a good tradeoff relationship between the water supply reliability and to sustain the natural flow variability to the best outcome. The three primary demands (constraints) for Kaoping Creek included instream flow releases, agriculture water withdrawals, and a constraint on municipal uses. The water allocation priorities are in the same order as listed. The authors of the report utilized the Range of Variability Approach. The RVA assesses the hydrologic impacts, using 32 meters of Hydrologic Alterations. These HAs that were produced were then integrated into one index that allowed one scheme among many to be optimized in lieu of the others. The RVA would work to minimize the impacts of natural hydrologic variability and ecology. The authors came up with a compromised algorithm because it was accurate for the problem in a discrete setting, and also flexible in incorporating decision makers’ inputs for each operation goal. The authors stated that compromise programming will find the optimal solution as the one with the shortest distance to an ideal point in which the multiple objectives will concurrently achieve their minimum values.

Currently, the minimum release rate from the weir does not efficiently restore the natural flow variations. The authors found that the instream flow release must be increased in other to decrease the overall rate of hydrologic variability. But the effect of increasing instream flow release means that the water supply shortage rates will also be increased. Therefore the RVA worked to minimize the overall degree of hydrologic alteration, and treated it as the same as minimizing ecological impacts. It weighs the natural flow change and water supply reliability equally in the model.

Discussion
I find multiobjective programming very interesting. Very few situations does water resource problem only have one single objective, whether it may be similar to this case study, or include a variety of other objectives. Therefore their research is definitely generic in my opinion, and models can be modeled similarly to this one, minus the decision makers’ inputs to the model. The authors claimed that their future research will include an implementation of a biological component into the RVA and optimization model. This would be very interesting to see the outcome of this model and the differences between the results of this model and the future model containing the biological input; furthermore, how the release rate would react.

One problem I have with the research, however, is the variations of agricultural and municipal water withdrawals. I know that this varies widely with respect to climate, land use, etc. However, it seemed that the agricultural demand was constant, and municipal was variable. It seems that in a usual case, the municipal withdrawal is more constant throughout the year than the seasonally variable agriculture demands. Was there a limit of withdrawal from the supply source? The authors claimed that municipalities wouldn’t receive diversions of water between January and April due to the dry season, yet agricultural withdrawal still remained constant.