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.
Monday, April 20, 2009
Monday, April 13, 2009
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.
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.
Sunday, March 29, 2009
Assignment 8
Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” Journal of Water Resources Planning and Management, 126(2) pg. 57-64.
Summary
This study was to act as an effort to develop the planning model for a reservoir operation. The reservoir operation model was based on a simulation-optimization approach, which was chosen for time consumption reasons. Simulation modeling was practical for an operation schedule because of the way that the modeling could accurately signify the reservoir’s qualities and characteristics that may be too complex or difficult to model. The model was also used to portion water use in the reservoir for reasons of relieving future drought conditions. The location that the model was tested on was in the Chennai water supply system in India.
The nonlinear programming model that the authors chose to use was the Hooke and Jeeves unconstrained linear programming model. This model included a “neural-network-based simulation sub-model.” This was introduced as a model that will closely mimic brain neurology.
The research took multiple steps to identify the optimal reservoir schedule. Firstly, the network was adjusted to simulate the accurate operation of the reservoir system. Secondly, the neural network model was built and linked as the sub-model, which was used together to “screen the operation policies.” Lastly, the optimization stage of the model was conducted. The operation policy that will yield the better objective function value is chosen from the dual simulation-optimization results. These results will further be filtered through using the “conventional simulation model”, versus the neural network simulation model.
Discussion
This article was interesting to me, because I think reservoir operation policy techniques are intriguing. The results of the model were found to be satisfactory, compared to the conventional simulation-optimization model. The authors said that a certain amount of exemplars is necessary for the network to be trained accurately.
This method was also found to be quite flexible and can easily adjust to complex operations. The authors seemed to have no superiority towards a Hooke and Jeeves nonlinear optimization model. They said that other optimization models can be used in place of Hooke and Jeeves. So what I would like to know is how much the results would be modified by using a different nonlinear optimization model. We all saw in our homework that results can vary a significant amount when comparing 5 distinctive nonlinear optimization models.
Summary
This study was to act as an effort to develop the planning model for a reservoir operation. The reservoir operation model was based on a simulation-optimization approach, which was chosen for time consumption reasons. Simulation modeling was practical for an operation schedule because of the way that the modeling could accurately signify the reservoir’s qualities and characteristics that may be too complex or difficult to model. The model was also used to portion water use in the reservoir for reasons of relieving future drought conditions. The location that the model was tested on was in the Chennai water supply system in India.
The nonlinear programming model that the authors chose to use was the Hooke and Jeeves unconstrained linear programming model. This model included a “neural-network-based simulation sub-model.” This was introduced as a model that will closely mimic brain neurology.
The research took multiple steps to identify the optimal reservoir schedule. Firstly, the network was adjusted to simulate the accurate operation of the reservoir system. Secondly, the neural network model was built and linked as the sub-model, which was used together to “screen the operation policies.” Lastly, the optimization stage of the model was conducted. The operation policy that will yield the better objective function value is chosen from the dual simulation-optimization results. These results will further be filtered through using the “conventional simulation model”, versus the neural network simulation model.
Discussion
This article was interesting to me, because I think reservoir operation policy techniques are intriguing. The results of the model were found to be satisfactory, compared to the conventional simulation-optimization model. The authors said that a certain amount of exemplars is necessary for the network to be trained accurately.
This method was also found to be quite flexible and can easily adjust to complex operations. The authors seemed to have no superiority towards a Hooke and Jeeves nonlinear optimization model. They said that other optimization models can be used in place of Hooke and Jeeves. So what I would like to know is how much the results would be modified by using a different nonlinear optimization model. We all saw in our homework that results can vary a significant amount when comparing 5 distinctive nonlinear optimization models.
Sunday, March 8, 2009
Assignment 7
Perez-Pedini C, Limbrunner JF, Vogel RM (2005) “Optimal location of infiltration-based best management practices for storm water management,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 131(6) pg. 441-448.
Summary
This essay was to look at best management practices (BMPs) based on infiltrations versus storage-based to mitigate flood and water quality damages. The objective of their model was to minimize the peak flows at the watershed outlets by determining optimal locations of the BMPs. The event-based hydrologic model used for the optimization was expanded upon the Natural Resource Conservation Services Curve Number method. This model was incorporated in Microsoft Excel, using a genetic algorithm methodology. The genetic algorithm established areas in which the BMP would be most efficiently used in order to decrease the flood flow. The topographical constraints incorporated in the model was developed by ArcGis, and then was brought into Excel. There was also curve number and slope constraints. The algorithm used for the flow direction simulation was the D8 algoritm due to simplicity.
The model was used on a watershed called Averjona River, near Boston. This was a highly urban watershed. The model was calibrated using two distinct storm events. However, the authors concluded that the output of the model was not interrelated to the observed, long term data. Therefore the authors decided that there was a problem with the groundwater storage characteristics in the formulation of the model. Another disadvantage of the model is the time step has the most control over the entire model in terms of locations for the infiltration-based BMPS. It was also found that optimal locations of BMPS were subsets of larger sets of BMPs.
Discussion
This article was interesting to me, because I think ArcGIS is a powerful tool that should be implemented when using the Natural Resources Conservation Services Curve Number method. It would make the Curve Number calculations easy, and has accurate topographical information readily available. Furthermore, this optimization model would be quite useful in practice.
Even though I’m still somewhat new to the concept of genetric algorithms, I noticed the authors noted that the D8 algorithm was used for simplicity. This could definitely be a fault of the research, and definitely something I would expand on if I were to continue this research. Also, I would like to see how the output of this infiltration-based BMP model compares to a storage-based technique. Lastly, I would conclude that this model wouldn’t be very replicable.
Summary
This essay was to look at best management practices (BMPs) based on infiltrations versus storage-based to mitigate flood and water quality damages. The objective of their model was to minimize the peak flows at the watershed outlets by determining optimal locations of the BMPs. The event-based hydrologic model used for the optimization was expanded upon the Natural Resource Conservation Services Curve Number method. This model was incorporated in Microsoft Excel, using a genetic algorithm methodology. The genetic algorithm established areas in which the BMP would be most efficiently used in order to decrease the flood flow. The topographical constraints incorporated in the model was developed by ArcGis, and then was brought into Excel. There was also curve number and slope constraints. The algorithm used for the flow direction simulation was the D8 algoritm due to simplicity.
The model was used on a watershed called Averjona River, near Boston. This was a highly urban watershed. The model was calibrated using two distinct storm events. However, the authors concluded that the output of the model was not interrelated to the observed, long term data. Therefore the authors decided that there was a problem with the groundwater storage characteristics in the formulation of the model. Another disadvantage of the model is the time step has the most control over the entire model in terms of locations for the infiltration-based BMPS. It was also found that optimal locations of BMPS were subsets of larger sets of BMPs.
Discussion
This article was interesting to me, because I think ArcGIS is a powerful tool that should be implemented when using the Natural Resources Conservation Services Curve Number method. It would make the Curve Number calculations easy, and has accurate topographical information readily available. Furthermore, this optimization model would be quite useful in practice.
Even though I’m still somewhat new to the concept of genetric algorithms, I noticed the authors noted that the D8 algorithm was used for simplicity. This could definitely be a fault of the research, and definitely something I would expand on if I were to continue this research. Also, I would like to see how the output of this infiltration-based BMP model compares to a storage-based technique. Lastly, I would conclude that this model wouldn’t be very replicable.
Sunday, March 1, 2009
Assignment 6
Behera, P, Papa, F., Adams, B (1999) “Optimization of Regional Storm-Water Management Systems” Journal of Water Resources Planning and Management, 125(2) pg. 107-114.
Summary
This essay is an expansion of a previous study, authored by the same individuals. It introduced design of ponds that will follow constraints of runoff control along with quality control. There is always a problem of designing multiple storm-water management ponds in a site that will outfall to the same point. The writers extended their previous study’s methodology by introducing a dynamic programming model to three parallel catchments, each of which had their own detention pond.
Obviously, the objective function of this model was to minimize cost of the ponds in each catchment, which included initial construction cost, operation cost, and maintenance costs. The variables that were optimized were the storage volume, location, pond depth, and release rate. The major constraints of the model were the runoff control constraint and the pollution control constraint.
The cost of the individual SWM pond was the sum of pond surface area multiplied by the land value and the product of the volume of the pond and value of construction and OMR costs. Furthermore, this cost was a function of the active storage volume of the pond, the pond depth, and the surface area of the catchment.
The performance (runoff and pollution) constraints were that the trunk sewer (discharge point) was required to meet or exceed a certain level of both quantity and quality control. The authors chose to optimize the blend of catchment controls in the multiple catchment system, compared to providing uniform control. The runoff control constraint was a function of storage and release rate, and the pollution control constraint was dependent on the settling velocity and the mass of suspended solids. This constraint was a function of the three decision variables: pond depth, release rate, and storage volume. Since these two major constraint equations were quite complex in the sense of isolating the decision variables, “isoquants” were generated to find the optimal solution for the three ponds.
Discussion
This paper was significant due to the large interest in designing storm-water management ponds in optimized fashions. Land developers wish to maximize the developable land, leaving little to be used for detention ponds. Therefore there is a high interest in minimizing the cost of the pond, and furthermore minimizing the storage volume (volume is proportional to cost). There is often pollution constraints set on an outlet that will be released back to the rivers, and many designers just choose to design the individual pond to follow that constraint. These authors decided to implement a model that would allow each pond to have a lower runoff and pollution control percentage; however the entire system (at the outfall point) will reach the minimal value. The authors proved that this methodology in fact decreased the cost, as shown in their example.
The assumptions that were made for the model were meteorological conditions, and that the ponds all outfall to eventually the same location. It is important that someone building research on this model understand that a multiple catchment pond design cannot be designed with this methodology if these assumptions are not satisfied. The authors did a great job on this research, and I cannot find any faults. If I were to build on this research, I would look into incorporating land use patterns with the model, creating the most developable land.
Summary
This essay is an expansion of a previous study, authored by the same individuals. It introduced design of ponds that will follow constraints of runoff control along with quality control. There is always a problem of designing multiple storm-water management ponds in a site that will outfall to the same point. The writers extended their previous study’s methodology by introducing a dynamic programming model to three parallel catchments, each of which had their own detention pond.
Obviously, the objective function of this model was to minimize cost of the ponds in each catchment, which included initial construction cost, operation cost, and maintenance costs. The variables that were optimized were the storage volume, location, pond depth, and release rate. The major constraints of the model were the runoff control constraint and the pollution control constraint.
The cost of the individual SWM pond was the sum of pond surface area multiplied by the land value and the product of the volume of the pond and value of construction and OMR costs. Furthermore, this cost was a function of the active storage volume of the pond, the pond depth, and the surface area of the catchment.
The performance (runoff and pollution) constraints were that the trunk sewer (discharge point) was required to meet or exceed a certain level of both quantity and quality control. The authors chose to optimize the blend of catchment controls in the multiple catchment system, compared to providing uniform control. The runoff control constraint was a function of storage and release rate, and the pollution control constraint was dependent on the settling velocity and the mass of suspended solids. This constraint was a function of the three decision variables: pond depth, release rate, and storage volume. Since these two major constraint equations were quite complex in the sense of isolating the decision variables, “isoquants” were generated to find the optimal solution for the three ponds.
Discussion
This paper was significant due to the large interest in designing storm-water management ponds in optimized fashions. Land developers wish to maximize the developable land, leaving little to be used for detention ponds. Therefore there is a high interest in minimizing the cost of the pond, and furthermore minimizing the storage volume (volume is proportional to cost). There is often pollution constraints set on an outlet that will be released back to the rivers, and many designers just choose to design the individual pond to follow that constraint. These authors decided to implement a model that would allow each pond to have a lower runoff and pollution control percentage; however the entire system (at the outfall point) will reach the minimal value. The authors proved that this methodology in fact decreased the cost, as shown in their example.
The assumptions that were made for the model were meteorological conditions, and that the ponds all outfall to eventually the same location. It is important that someone building research on this model understand that a multiple catchment pond design cannot be designed with this methodology if these assumptions are not satisfied. The authors did a great job on this research, and I cannot find any faults. If I were to build on this research, I would look into incorporating land use patterns with the model, creating the most developable land.
Monday, February 23, 2009
Assignment 5
Berry, J., Fleisher, L, Hart, W. Phillips, C. and Watson, J.-P. (2005) “Sensor Placement in Municipal Water Networks”, Journal of Water Resources Planning and Management, 131(3) pg. 237-243.
Summary
This paper was quite similar to the last paper. They used a mixed integer programming to optimize placements of sensors in municipal water systems. The objective function was to minimize the fraction of population exposed to contaminants, in which each node of the model was weighed based on population that will consume the water. Currently, the early warning system (EWS) is used, which identifies the contamination incident while allowing time for response. The EPA used these EWS systems by placing sensors at multiple location in which the coverage of flow in terms of detection of contamination is maximized. The way in which a contamination event was to occur was modeled by a fixed probability distribution.
The assumptions that the authors used was: An attack occurs at one point in the network; The total population is considered exposed without looking at health impacts; Downstream populations are protected by nodes with sensors, meaning that a population is considered exposed if its node is reached by a flow path that doesn’t pass a sensor; Time periods are treated independently. These assumptions allowed the researched to ignore both the temporal effects of the contaminant, along with the concentration effects. The authors said that this enabled them to say that their model can be re-used for situations where large volumes of contaminants flow quickly through a network.
The network was modeled in which the pipes were “edges”, and the nodes were vertices. The authors’ input data was: probability of an attack at each node, population density at each node, the network layout. The constraints also included were flow direction constraints along with maximum number of sensors. The authors tested the model with example data sets using the program EPANet. The results showed that the population at risk from contamination is reduced as the number of sensors utilized is increased. When the authors realized that the results were showing little sensitivity to the objection function, they then discussed areas in which their model can be more generalized for every day use: using a model that incorporates temporal effects, placement of sensors other than nodes, incorporating cost and maintenance cost of sensors, modified objective function to relate to multiple objectives.
Discussion
This paper was significant, because this is still quite a large issue in water resources these days. However, there are some faults in this paper. It seems that the model isn’t taking any consideration of the water demands of each node, just the population. The authors put large constraints on the direction of flow, but the velocity of the flow was not incorporated at all. If I were to expand on the research, I would start with the four future goals of the model that the authors discussed at the end of the paper.
Summary
This paper was quite similar to the last paper. They used a mixed integer programming to optimize placements of sensors in municipal water systems. The objective function was to minimize the fraction of population exposed to contaminants, in which each node of the model was weighed based on population that will consume the water. Currently, the early warning system (EWS) is used, which identifies the contamination incident while allowing time for response. The EPA used these EWS systems by placing sensors at multiple location in which the coverage of flow in terms of detection of contamination is maximized. The way in which a contamination event was to occur was modeled by a fixed probability distribution.
The assumptions that the authors used was: An attack occurs at one point in the network; The total population is considered exposed without looking at health impacts; Downstream populations are protected by nodes with sensors, meaning that a population is considered exposed if its node is reached by a flow path that doesn’t pass a sensor; Time periods are treated independently. These assumptions allowed the researched to ignore both the temporal effects of the contaminant, along with the concentration effects. The authors said that this enabled them to say that their model can be re-used for situations where large volumes of contaminants flow quickly through a network.
The network was modeled in which the pipes were “edges”, and the nodes were vertices. The authors’ input data was: probability of an attack at each node, population density at each node, the network layout. The constraints also included were flow direction constraints along with maximum number of sensors. The authors tested the model with example data sets using the program EPANet. The results showed that the population at risk from contamination is reduced as the number of sensors utilized is increased. When the authors realized that the results were showing little sensitivity to the objection function, they then discussed areas in which their model can be more generalized for every day use: using a model that incorporates temporal effects, placement of sensors other than nodes, incorporating cost and maintenance cost of sensors, modified objective function to relate to multiple objectives.
Discussion
This paper was significant, because this is still quite a large issue in water resources these days. However, there are some faults in this paper. It seems that the model isn’t taking any consideration of the water demands of each node, just the population. The authors put large constraints on the direction of flow, but the velocity of the flow was not incorporated at all. If I were to expand on the research, I would start with the four future goals of the model that the authors discussed at the end of the paper.
Monday, February 16, 2009
Assignment 4
Lee, B. H. and Deininger, R. A. (1992) “Optimal Locations of Monitoring Stations in Water Distribution Systems”, Journal of Environmental Engineering, 118(1) pg. 4-16.
Summary
The Environmental Protection Agency has had a requirement of monitoring water quality of potential drinking water. The guideline of the sampling frequency is based on the size of population that the distribution system serves. Furthermore, the EPA doesn’t prescribe a certain methodology in which monitoring stations should be chosen. This study by Lee and Deininger propose a integer programming model that introduces a rubric that optimizes locations of monitoring station(s).
First, a test model is introduced to explain in detail how the optimized model works. The model first creates a water fraction matrix in which the amount of water going through a certain node is contributed by a certain percentage from another node. From this matrix, a coverage matrix is created in which all the non-zero values above a certain “coverage criterion” are substituted by a 1. The “coverage criterion” is defined by the minimum percentage of flow contributed from node i to node j to be in order to be assigned a coverage value of 1.
The formulation of the optimization model includes an objective function that minimizes the product of the demand of node i and the binary integer that decides whether or not the sampling station will be location at node i. The constraints include the number of sampling stations wished to be used, along with the demand constraints for the nodes.
In the practical models, the demands of the nodes were the maximum daily flow demands for drinking water for the node. The demand coverage was optimized for a location in Michigan, in which the coverage increased from 18.5% to 54%. Also, this example incorporated multiple demand patterns, in which a two-scenario optimization problem was created. There was also a model created for a location in Connecticut that had incorporated four different flow scenarios that included demand nodes and pumping well fields.
Discussion
The authors argue that the EPA has strict requirements to monitor the quality of drinking water at specific locations in the system, however there is no formal procedures that are to be followed. It was interesting that the authors seemed to be completely uneducated about the previous methodologies of the past practices in this field. Surely they knew a little bit of information of why certain stations were monitored.
The authors introduced an integer programming model that optimized certain monitoring locations based on a “coverage criterion”. The authors believe that this factor is the optimal way to choose these specific locations. However is there a different criterion that other researchers have looked into? Also, it seems like a lot of information about the system is omitted, and nothing was added to the system to compensate. Lastly, I would be interested in the further development of this research, post 1992.
Summary
The Environmental Protection Agency has had a requirement of monitoring water quality of potential drinking water. The guideline of the sampling frequency is based on the size of population that the distribution system serves. Furthermore, the EPA doesn’t prescribe a certain methodology in which monitoring stations should be chosen. This study by Lee and Deininger propose a integer programming model that introduces a rubric that optimizes locations of monitoring station(s).
First, a test model is introduced to explain in detail how the optimized model works. The model first creates a water fraction matrix in which the amount of water going through a certain node is contributed by a certain percentage from another node. From this matrix, a coverage matrix is created in which all the non-zero values above a certain “coverage criterion” are substituted by a 1. The “coverage criterion” is defined by the minimum percentage of flow contributed from node i to node j to be in order to be assigned a coverage value of 1.
The formulation of the optimization model includes an objective function that minimizes the product of the demand of node i and the binary integer that decides whether or not the sampling station will be location at node i. The constraints include the number of sampling stations wished to be used, along with the demand constraints for the nodes.
In the practical models, the demands of the nodes were the maximum daily flow demands for drinking water for the node. The demand coverage was optimized for a location in Michigan, in which the coverage increased from 18.5% to 54%. Also, this example incorporated multiple demand patterns, in which a two-scenario optimization problem was created. There was also a model created for a location in Connecticut that had incorporated four different flow scenarios that included demand nodes and pumping well fields.
Discussion
The authors argue that the EPA has strict requirements to monitor the quality of drinking water at specific locations in the system, however there is no formal procedures that are to be followed. It was interesting that the authors seemed to be completely uneducated about the previous methodologies of the past practices in this field. Surely they knew a little bit of information of why certain stations were monitored.
The authors introduced an integer programming model that optimized certain monitoring locations based on a “coverage criterion”. The authors believe that this factor is the optimal way to choose these specific locations. However is there a different criterion that other researchers have looked into? Also, it seems like a lot of information about the system is omitted, and nothing was added to the system to compensate. Lastly, I would be interested in the further development of this research, post 1992.
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