Randall, Dean, Leasa Cleland, Catharine S. Kuehne, George W. Link, and Daniel P. Sheer. “Water Supply Planning Simulation Model Using Mixed-Integer Linear Programming ‘Engine’.” Journal of Water Resources Planning and Management. March/April (1997): 116-124.
Summary:
This paper describes the development of a water supply planning simulation model built for Alameda County Water District (southeast of San Francisco Bay area). The goals of the system were to be user-friendly, adaptable so that little coding was necessary for major changes to the system, and be able to be integrated with other models. The developers chose to model the system with a linear program rather than a network formulation because some constraints can be solved directly with an LP while a network formulation would solve these constraints iteratively, taking more time to solve.
The paper describes the different components of the system, including aqueducts, a creek, reservoirs, percolation ponds, water treatment plants, and an aquifer. There are multiple objectives in the system but the modelers chose to assign weights to each of the objectives so that the system could be modeled with a single objective for simplicity in solving. The paper gives many details of how specific operations were modeled and the challenges in doing so.
The model was applied to help the water district plan for its future needs. A range of future demand scenarios were run to determine the potential magnitude and frequency of shortages. The modelers added various new supply-side and demand-side components to the system to see which components would best alleviate their water shortages. In conclusion, the authors say that a LP or network formulation would’ve both been good approaches, but because of a few details, such as a binary constraint and specific operating details in the diversion dam, the LP was the preferred choice.
Discussion:
The paper is an interesting case study in applying linear programming to help find water resources solutions. However, the paper didn’t seem to be using any new methodologies, nor did it give more than a few sentences on why a linear program was better than a network formulation. I’m a little surprised this paper was published in such a major journal since the work seems insignificant, at least to me. I did almost the exact same work for my undergraduate research and I didn't think it was unique enough to be published. However, this is a slightly older publication and perhaps this kind of application of linear programming was a new methodology at that time.
The model was described as being highly simplified in order to save on computing time. If I were to continue research on this topic today, I would make the model more complex for accuracy. Computing time would not be as much of an issue in today’s powerful computing world. Complexities that I would be interested in adding in would include a hydrologic model to assess the impacts of rainwater harvesting.
Monday, January 26, 2009
Sunday, January 25, 2009
Liebman 1976
Liebman, Jon (1976) “Some Simple-Minded Observations on the Role of Optimization in Public Systems Decision-Making,” Interfaces 6(4) pp. 103-108.
Summary:
Public-sector decision-making requires a different approach than private-sector decision-making. One example of successful problem-solving is with the improvement of urban firefighting organizations. A second area of applied modeling is with river basin quality management. The first example was successful and helpful to the public system, while the second did not reach useful enough conclusions to make improvements in the system.
In most initial applications of operations research, it was easy to find solutions since the problems had clear cut objectives. Nowadays, as techniques in operations research become more advanced, more complex problems can be addressed and things are no longer simple. Especially in public-sector decision-making, the goals, desired results, intended and unintended side-effects are difficult to determine. Goals are usually set by congress regardless of the many differences in public opinion, but it is uncertain if the goals set by congress always applicable and best to model. Another thing to consider is the fact that models do not have the complexities of a system built into them the way humans do, for instance complexities of morality and human values. Private-sector decision-making can avoid some of this difficulty because the goals in private sectors are often more clearly defined and shared by all members of the organization. This isn’t so in public sectors. In the case of the firefighting operations optimization, the goals are clear-cut with little argument. In the case of the river basin quality management, the goals are far from clear and the case is complicated.
With complicated problems and complicated goals, these new optimization tools are best used when they identify a group of alternatives for the decision maker to select from rather than choosing a single alternative as best. There is a certain amount of understanding gained by building a model and playing around with its parameters. This understanding might be communicated through a sensitivity analysis, but in order to gain the full understanding of the system it is best for a decision-maker to get in and work with the model him or herself. Since a model has unclear goals, different modelers might build different models for the same problem based on their own assumptions and biases. It is better to supply a decision maker with results from a number of models instead of just one. When a modeler builds a complex model, a lot of knowledge can be gained through that development, but the complexities do not benefit the decision maker much. It is better that the developer takes what he or she learns from the process of building a complex model and uses that to build a more simple model to give to the decision maker. The process of building a model helps the modeler understand the situation much better than if the modeler were to try to pick up on a model that was built by another person. Since this is the case, it is not a bad idea for a new analyst to rebuild a model from scratch rather than build upon a model previously built.
In conclusion, the article wishes that modelers are aware of when and how modeling tools can appropriately be applied to public-sector decision-making.
Discussion:
This paper does the important work of informing modelers to be wary of trying to model complex public-sector problems with the expectation that they can find a single optimum solution. This paper was fairly short in length and did not cover in depth actual cases where public-sector modeling struggled to find answers. The article would’ve been strengthened if the author had delved into the details of the river basin quality management issues that modelers have faced in the past.
If this were my research, it would be interesting to experiment with giving a complicated model to different modelers and see the differences in the models they produce. Also, as further research I may outline in a paper the methodology behind providing a range of alternatives rather than selecting an optimum, since this is in line with my interests in systems research.
Summary:
Public-sector decision-making requires a different approach than private-sector decision-making. One example of successful problem-solving is with the improvement of urban firefighting organizations. A second area of applied modeling is with river basin quality management. The first example was successful and helpful to the public system, while the second did not reach useful enough conclusions to make improvements in the system.
In most initial applications of operations research, it was easy to find solutions since the problems had clear cut objectives. Nowadays, as techniques in operations research become more advanced, more complex problems can be addressed and things are no longer simple. Especially in public-sector decision-making, the goals, desired results, intended and unintended side-effects are difficult to determine. Goals are usually set by congress regardless of the many differences in public opinion, but it is uncertain if the goals set by congress always applicable and best to model. Another thing to consider is the fact that models do not have the complexities of a system built into them the way humans do, for instance complexities of morality and human values. Private-sector decision-making can avoid some of this difficulty because the goals in private sectors are often more clearly defined and shared by all members of the organization. This isn’t so in public sectors. In the case of the firefighting operations optimization, the goals are clear-cut with little argument. In the case of the river basin quality management, the goals are far from clear and the case is complicated.
With complicated problems and complicated goals, these new optimization tools are best used when they identify a group of alternatives for the decision maker to select from rather than choosing a single alternative as best. There is a certain amount of understanding gained by building a model and playing around with its parameters. This understanding might be communicated through a sensitivity analysis, but in order to gain the full understanding of the system it is best for a decision-maker to get in and work with the model him or herself. Since a model has unclear goals, different modelers might build different models for the same problem based on their own assumptions and biases. It is better to supply a decision maker with results from a number of models instead of just one. When a modeler builds a complex model, a lot of knowledge can be gained through that development, but the complexities do not benefit the decision maker much. It is better that the developer takes what he or she learns from the process of building a complex model and uses that to build a more simple model to give to the decision maker. The process of building a model helps the modeler understand the situation much better than if the modeler were to try to pick up on a model that was built by another person. Since this is the case, it is not a bad idea for a new analyst to rebuild a model from scratch rather than build upon a model previously built.
In conclusion, the article wishes that modelers are aware of when and how modeling tools can appropriately be applied to public-sector decision-making.
Discussion:
This paper does the important work of informing modelers to be wary of trying to model complex public-sector problems with the expectation that they can find a single optimum solution. This paper was fairly short in length and did not cover in depth actual cases where public-sector modeling struggled to find answers. The article would’ve been strengthened if the author had delved into the details of the river basin quality management issues that modelers have faced in the past.
If this were my research, it would be interesting to experiment with giving a complicated model to different modelers and see the differences in the models they produce. Also, as further research I may outline in a paper the methodology behind providing a range of alternatives rather than selecting an optimum, since this is in line with my interests in systems research.
Wednesday, January 21, 2009
Assignment #0
My name is Michelle Hollingsworth.
I am enrolled in CVEN665 because I enjoy systems thinking in a Water Resources context and I'd like to improve upon my ability to take real-world systems and put it in programming terms. Most people will have models of systems in their minds but it requires a developed skill to recognize a system and translate it into mathematical terms. I'm looking forward to the class project as an opportunity to practice model formulation and evaluation of that model.
Critical thinking is when we question the model we have of a system, analyze why the system behaves and interacts in the manner that it does, and through that we should expand our thinking to all possibilities of behavior and interactions, evaluate the quality of those possibilities, and then redefine our new system with our new more reliable model.
Subscribe to:
Comments (Atom)
