Review Article
1) Article Reference
Pan TC, Kao JJ (2009) GA-QP Model to Optimize Sewer System Design, JOURNAL OF ENVIRONMENTAL ENGINEERING, 135(1) 17-24
2) Summary
This paper presents an application of genetic algorithms to optimize the design of a sewer system. A case study is presented to illustrate the methodology using system composed of 79 links, 56 nodes and a drainage area of 260 ha.
The author’s presents a discussion of the complexity of designing a sewer system targeting the minimum cost, maintaining the system hydraulic and construction constraints. A description of the concepts of genetic algorithm is also presented including all its elements and behavior. For this problem the decision variable of the sewer are coded as genes and each chromosome represents one design. The first constraint assures that the pipe has enough flow capacity, the second constraint takes care of ensuring the downstream pipe has diameter equal or greater then the upstream neighbor. It is also considered the possibility of installing a pump in each node. The fitness function is used considering the construction cost of the network. A description of the selection, crossover and mutation methods is provided. For this particular study, the author used also a quadratic programming (QP) model, to improve efficiency, the nonlinear cost fitness function was approximated to a QP. The design parameters for this case study were the maximum velocity, minimum velocity, minimum slope, maximum proportional water depth and minimum cover depth. To avoid finding a solution which was not the true best, due to simplifications or factors that are hard to formulate into the model, it was also used a MGA function.
Different results for the case study were presented achieving a minimum cost of approximate 1.7 million dollars. According to the author the GA and quadratic model approach were satisfactory to find a good solution for the problem.
3) Discussion
It really surprised me to see that this paper was published in 2009 and nobody had ever tried such approach for solving sewer systems network. After all our classes, I felt quite confident in understating all the terminology and the methodology for solving the GA presented in this paper. Actually I think the paper had even too much of GA theory instead of new contributions for science. The best part, I think, is that I feel very encouraged to apply GA optimization methods for my research area.
Tuesday, April 28, 2009
Assignment #10a - Optimization in public sector
Review Article
1) Article Reference
BRILL ED (1979) USE OF OPTIMIZATION MODELS IN PUBLIC-SECTOR PLANNING, MANAGEMENT SCIENCE, 25
2) Summary
This paper dates from the seventies and deals with the use of optimization models applied to public sector problem solving. According to the authors, many optimization models were concentrated in finding the best economic solution, but failure to consider equity and have empirical shortcoming in estimating benefits and costs. The author also suggests that multi objective programming can examine tradeoffs between different objectives and mention the use of goal programming as well. Many limitations to the use of optimization techniques are pointed, specially related to complete and incomplete multi objective programming, with some examples as the planning of a lakeside park with solutions favoring boaters or swimmers and the location of regional facilities.
An important issue, as pointed by the author, is that usually optimization models have been used to find the “answer” for the public sector, considering the economic efficiency and social optimality. As empirical problems arise, new approaches had to be developed. Among many reasons pointed by the author, the fact that many of the problems are wicked, demonstrated the difficulty to find one best approach for solving this problems.
A general flow chart on how to use optimization techniques for the planning process is presented and some alternatives discussed in more detail such as the join use of models: optimization and simulation; analytical and optimization; and using a toolbox of models. Several examples are presented, including one study developed in the Netherlands to analyze the implementation of an estuary dam, considering several factors such as flood protection, ecological aspects, costs and social impacts providing elements for the parliament ultimate decision.
Two main advantages by that time are pointed by the author as the capability of generating alternatives and facilitating evaluations and generating alternative solutions that are different from each other.
The author finally concludes that although multiojective planning in the way it was developed by his time, “as the second generation of optimization techniques”, had improved the way of solving optimization problems, but was still limited to the wicked organization of public sector problems. He also argues that such techniques should be used to gain insights about the problem itself, develop alternative scenarios and support human creativity to find the best solutions of a problem.
3) Discussion
I personally think it is great to have a historical overview on how the author, and scientific community, is approaching optimization problems. Besides the historical importance, I think an important issue that puts his discussion a little out of date is the considerable advance of computer potential and capacity to solve mathematical problems nowadays.
1) Article Reference
BRILL ED (1979) USE OF OPTIMIZATION MODELS IN PUBLIC-SECTOR PLANNING, MANAGEMENT SCIENCE, 25
2) Summary
This paper dates from the seventies and deals with the use of optimization models applied to public sector problem solving. According to the authors, many optimization models were concentrated in finding the best economic solution, but failure to consider equity and have empirical shortcoming in estimating benefits and costs. The author also suggests that multi objective programming can examine tradeoffs between different objectives and mention the use of goal programming as well. Many limitations to the use of optimization techniques are pointed, specially related to complete and incomplete multi objective programming, with some examples as the planning of a lakeside park with solutions favoring boaters or swimmers and the location of regional facilities.
An important issue, as pointed by the author, is that usually optimization models have been used to find the “answer” for the public sector, considering the economic efficiency and social optimality. As empirical problems arise, new approaches had to be developed. Among many reasons pointed by the author, the fact that many of the problems are wicked, demonstrated the difficulty to find one best approach for solving this problems.
A general flow chart on how to use optimization techniques for the planning process is presented and some alternatives discussed in more detail such as the join use of models: optimization and simulation; analytical and optimization; and using a toolbox of models. Several examples are presented, including one study developed in the Netherlands to analyze the implementation of an estuary dam, considering several factors such as flood protection, ecological aspects, costs and social impacts providing elements for the parliament ultimate decision.
Two main advantages by that time are pointed by the author as the capability of generating alternatives and facilitating evaluations and generating alternative solutions that are different from each other.
The author finally concludes that although multiojective planning in the way it was developed by his time, “as the second generation of optimization techniques”, had improved the way of solving optimization problems, but was still limited to the wicked organization of public sector problems. He also argues that such techniques should be used to gain insights about the problem itself, develop alternative scenarios and support human creativity to find the best solutions of a problem.
3) Discussion
I personally think it is great to have a historical overview on how the author, and scientific community, is approaching optimization problems. Besides the historical importance, I think an important issue that puts his discussion a little out of date is the considerable advance of computer potential and capacity to solve mathematical problems nowadays.
Assignment #9 - Compromise programming - instream flow - multiobjective
1) Article Reference
Shiau JT, Wu FC (2006) Compromise programming methodology for determining instream flow under multiobjective water allocation criteria, JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 42(5), pp. 1179-1191
2) Summary
This paper presents a multiobjective approach for evaluating instream flow water allocation considering increasing water uses within the basin. This approach uses the concept of Range of Variability (RVA) Approach and the Indicators of Hydrologic Alterations (IHAs).
A case study is presented for the Kaoping creek in southwestern Taiwan. Extensive description of the hydrologic and water use characteristics of the basin is provided. Special concerns are related to endangered and endemic species. By date, instream flows are been provide to support the ecological health of the systems, but is believed that those releases are incapable of guarantying sufficient flow variation required for the sustainability of the aquatic biota. Tables presented monthly average inflow and water uses for agricultural and municipal uses within the basin.
For the optimization analyses of the water allocation schemes, a description of the methodology used is provide, including a short review of the Range of Variability Approach concepts and the overall degree of hydrologic alteration. A detailed table is provided to describe the IHAs used in the RVA. The IHAs are grouped in 5 different specifications which represent different hydrologic parameters.
A description of the weir (main diversion point) operation model is also provided. Within this description the operation of the weir and the different water uses are described.
The main goal of the weir is to supply the registered agricultural demand , the projected municipal water supply and the instream flow conditions. Considering the municipal water supply and the agricultural use, the objective is to minimize the shortages periods, represented by a shortage ratio.
To solve the minimization function, the authors used a Multiobjective compromise programming approach. The results evaluated the current schedule operation impacts on water shortages and hydrologic alterations, the effects of different instream flow releases ,the effects of weighting factors and the Ecological Effects of proposed instream flow release
The main conclusions of the paper were that the inclusion of the individual degrees of alteration associated with the 32 IHAs made possible to optimize the weir operation scheme through compromise programming and showed that the current instream releases do not meet minimum requirements for guarantee ecological health downstream.
3) Discussion
I enjoyed reading this paper as it gave me a better sense on the practical application of multi objective problem solving. I think this kind of optimization is very useful, as it really approaches real life problems. Usually there are more than one objective that almost always differ from each other. Although, I think the evaluation of the intream flow necessities could have been better evaluated.
Shiau JT, Wu FC (2006) Compromise programming methodology for determining instream flow under multiobjective water allocation criteria, JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 42(5), pp. 1179-1191
2) Summary
This paper presents a multiobjective approach for evaluating instream flow water allocation considering increasing water uses within the basin. This approach uses the concept of Range of Variability (RVA) Approach and the Indicators of Hydrologic Alterations (IHAs).
A case study is presented for the Kaoping creek in southwestern Taiwan. Extensive description of the hydrologic and water use characteristics of the basin is provided. Special concerns are related to endangered and endemic species. By date, instream flows are been provide to support the ecological health of the systems, but is believed that those releases are incapable of guarantying sufficient flow variation required for the sustainability of the aquatic biota. Tables presented monthly average inflow and water uses for agricultural and municipal uses within the basin.
For the optimization analyses of the water allocation schemes, a description of the methodology used is provide, including a short review of the Range of Variability Approach concepts and the overall degree of hydrologic alteration. A detailed table is provided to describe the IHAs used in the RVA. The IHAs are grouped in 5 different specifications which represent different hydrologic parameters.
A description of the weir (main diversion point) operation model is also provided. Within this description the operation of the weir and the different water uses are described.
The main goal of the weir is to supply the registered agricultural demand , the projected municipal water supply and the instream flow conditions. Considering the municipal water supply and the agricultural use, the objective is to minimize the shortages periods, represented by a shortage ratio.
To solve the minimization function, the authors used a Multiobjective compromise programming approach. The results evaluated the current schedule operation impacts on water shortages and hydrologic alterations, the effects of different instream flow releases ,the effects of weighting factors and the Ecological Effects of proposed instream flow release
The main conclusions of the paper were that the inclusion of the individual degrees of alteration associated with the 32 IHAs made possible to optimize the weir operation scheme through compromise programming and showed that the current instream releases do not meet minimum requirements for guarantee ecological health downstream.
3) Discussion
I enjoyed reading this paper as it gave me a better sense on the practical application of multi objective problem solving. I think this kind of optimization is very useful, as it really approaches real life problems. Usually there are more than one objective that almost always differ from each other. Although, I think the evaluation of the intream flow necessities could have been better evaluated.
Wednesday, April 1, 2009
Assignment #8 - Neural Network / Reservoir Optimization
Review Article
1) Article Reference
Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 126(2) pp. 57-64
2) Summary
This paper presents an application of neural network-based simulation optimization model for reservoir operation. A case study is presented for Chennai city, in India. The general framework of the study presented by the author follows: 1) Develop a conventional simulation model to obtain results from different operation policies; 2) Train a back propagation neural network model using those results; 3) Link the neural network model as a sub model with direct search non-linear programming optimization models; 4)Find the optimal policy and near optimal policies using neural network –optimization model; 5)Refine the optimal policies obtained using conventional simulation optimization model and determine the optimal policy.
The optimization is done using the Hookes and Jeeves direct search method. For this case it was considered better to have small water supply restrictions along the time from having one big drought. Then the optimization goal was to minimize the minimum sum of the deficits. For that a deficit index was defined. The optimization procedure started at an initial point and progress using Hookes and Jeeves method until an optimal solution. Several initial points were used as this algorithm can get stacked in local minimums.
A neural network simulation model was used in order to enhance the speed of the process. For this case the authors used a back propagation approach. The network was trained using pairs of input and output vectors.
The decision set was composed of 18 decision variables. Based in inflow pattern the year was divided into six time periods. Four out of five supply levels were considered at each time. The results are presented in two different scenarios. For each scenario, the authors presents the results for 4 different policies including the storage for each time period, the release and the deficit. The inclusion of two additional reservoirs is also analyzed.
As the major result, the authors concluded that the neural network based simulation-optimization model performed satisfactory. Also the authors mention that reservoir operation problems considering several and more complicated networks could be handled by this method.
3) Discussion
The paper presents a very interesting approach to solve a very common reservoir operation problem. I believe several attempts have been made to find methodologies for optimal operation rules for reservoir. I think this is an emerging issue as water demand is increasing significantly in large metropolitan areas and the reservoirs systems have to be used optimally.
1) Article Reference
Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 126(2) pp. 57-64
2) Summary
This paper presents an application of neural network-based simulation optimization model for reservoir operation. A case study is presented for Chennai city, in India. The general framework of the study presented by the author follows: 1) Develop a conventional simulation model to obtain results from different operation policies; 2) Train a back propagation neural network model using those results; 3) Link the neural network model as a sub model with direct search non-linear programming optimization models; 4)Find the optimal policy and near optimal policies using neural network –optimization model; 5)Refine the optimal policies obtained using conventional simulation optimization model and determine the optimal policy.
The optimization is done using the Hookes and Jeeves direct search method. For this case it was considered better to have small water supply restrictions along the time from having one big drought. Then the optimization goal was to minimize the minimum sum of the deficits. For that a deficit index was defined. The optimization procedure started at an initial point and progress using Hookes and Jeeves method until an optimal solution. Several initial points were used as this algorithm can get stacked in local minimums.
A neural network simulation model was used in order to enhance the speed of the process. For this case the authors used a back propagation approach. The network was trained using pairs of input and output vectors.
The decision set was composed of 18 decision variables. Based in inflow pattern the year was divided into six time periods. Four out of five supply levels were considered at each time. The results are presented in two different scenarios. For each scenario, the authors presents the results for 4 different policies including the storage for each time period, the release and the deficit. The inclusion of two additional reservoirs is also analyzed.
As the major result, the authors concluded that the neural network based simulation-optimization model performed satisfactory. Also the authors mention that reservoir operation problems considering several and more complicated networks could be handled by this method.
3) Discussion
The paper presents a very interesting approach to solve a very common reservoir operation problem. I believe several attempts have been made to find methodologies for optimal operation rules for reservoir. I think this is an emerging issue as water demand is increasing significantly in large metropolitan areas and the reservoirs systems have to be used optimally.
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