Value Index, a Novel Decision Making Approach for Waste Load Allocation
Waste load allocation (WLA) policies may use multiobjective
optimization methods to find the most appropriate and
sustainable solutions. These usually intend to simultaneously
minimize two criteria, total abatement costs (TC) and environmental
violations (EV). If other criteria, such as inequity, need for
minimization as well, it requires introducing more binary
optimizations through different scenarios. In order to reduce the
calculation steps, this study presents value index as an innovative
decision making approach. Since the value index contains both the
environmental violation and treatment costs, it can be maximized
simultaneously with the equity index. It implies that the definition of
different scenarios for environmental violations is no longer required.
Furthermore, the solution is not necessarily the point with minimized
total costs or environmental violations. This idea is testified for Haraz
River, in north of Iran. Here, the dissolved oxygen (DO) level of river
is simulated by Streeter-Phelps equation in MATLAB software. The
WLA is determined for fish farms using multi-objective particle
swarm optimization (MOPSO) in two scenarios. At first, the trade-off
curves of TC-EV and TC-Inequity are plotted separately as the
conventional approach. In the second, the Value-Equity curve is
derived. The comparative results show that the solutions are in a
similar range of inequity with lower total costs. This is due to the
freedom of environmental violation attained in value index. As a
result, the conventional approach can well be replaced by the value
index particularly for problems optimizing these objectives. This
reduces the process to achieve the best solutions and may find better
classification for scenario definition. It is also concluded that decision
makers are better to focus on value index and weighting its contents
to find the most sustainable alternatives based on their requirements.
[1] E., Feizi Ashtiani, M. H. Niksokhan, M. Ardestani, “Multi-objective
Waste Load Allocation in River System by MOPSO”, International
Journal of Environmental Research, in press.
[2] D. H. Burn, B. J. Lence, “Comparison of optimization formulations for
waste-load allocations", Journal of Environmental Engineering, 118(4),
1992, 597-612.
[3] S. A. Mostafavi, A. Afshar, “Waste load allocation using non-dominated
archiving multi-colony ant algorithm”, Procedia Computer Science, 3,
2011, 64-69.
[4] N. Mahjouri, A. Ardestani, “game theoretic approach for interbasin
water resources allocation considering the water quality issues”,
Environmental Monitoring and Assessment, 167, 2010, 527–544
[5] S.R. Yandamuri, K. Srinivasan, S. Murty Bhallamudi, “Multiobjective
optimal waste load allocation models for rivers using nondominated
sorting genetic algorithm-II”, Journal of water resources planning and
management, 132(3), 2006, 133-143.
[6] W. Kurek, A. Ostfeld, “Multi-objective optimization of water quality,
pumps operation, and storage sizing of water distribution systems”,
Journal of Environmental Management, 115, 2013, 189-197. [7] B. Malekmohammadi, B. Zahraie, R. Kerachian, “Ranking solutions of
multi-objective reservoir operation optimization models using multi
criteria decision analysis”, Expert systems with applications, 38, 2011,
7851-7863.
[8] M.R. Nikoo, R. Kerachian, M.H. Niksokhan, “Equitable Waste Load
Allocation in Rivers Using Fuzzy Bi-matrix Games”, Water Resources
Management, 26(15), 2012, 4539-4552.
[9] E. Shirangi, R. Kerachian, M. Shafai Bajestan, “A simplified model for
reservoir operation considering the water quality issues: Application of
the Young conflict resolution theory”, Environmental Monitoring and
Assessment, 146 (1-3), 2007, 77-89.
[10] M. Saadatpour, A. Afshar, “Waste load allocation modeling with fuzzy
goals; simulation-optimization approach.” Water resources
management, 21(7), 2007, 1207-1224.
[11] P. Kalbar, S. Karmakar, S. Asolekar, “Selection of an appropriate
wastewater treatment technology: A scenario-based multiple-attribute
decision-making approach”. Journal of Environmental Management,
113, 2012, 158-169.
[12] N. Sa-nguanduan, V. Nititvattananon, “Strategic decision making for
urban water reuse application: A case from Thailand”. Desalination,
268, 2011, 141–149.
[13] D. Hidalgo, R. Irusta, L. Martinez, D. Fatta, A. Papadopoulos,
“Development of a multi- function software decision support tool for the
promotion of the safe reuse of treated urban wastewater”. Desalination,
215, 2007, 90–103.
[14] S. Jamshidi, S. Imani, “Wastewater Reuse, a Solution for Environmental
Risks Reduction and Value Index Development for Wastewater
Treatment Plants”, Value Engineering and Cost Management
Conference, 2014, Tehran, Iran.
[15] S. Hajkowicz, K. Collins, “A Review of Multiple Criteria Analysis for
Water Resource Planning and Management”, Water Resource
Management, 21, 2007, 1553–1566.
[16] G. Axelrad, E. Feinerman, “Regional planning of wastewater reuse for
irrigation and river rehabilitation”, Journal of Agricultural Economics,
60(1), 2009, 105–131.
[17] A. Pejman, G. Nabi Bidhendi, A. Karbassi, N. Mehrdadi, M. Esmaeili
Bidhendi, “Evaluation of spatial and seasonal variations in surface water
quality using multivariate statistical techniques”, International Journal
of Environmental Science and Technology, 6(3), 2009, 467-476.
[18] A.M. Baltar, D.G. Fontane, “Use of multi-objective particle swarm
optimization in water resource management”, Journal of water resource
planning and management, 134(3), 2008, 257-265.
[19] A. Azadnia, B. Zahraie, “Optimization of nonlinear Muskingum method
with variable parameters using multi-objective particle swarm
optimization”, World environmental and Water Resources Congress,
ASCE, 2010, 2278-2284
[20] I., Rahimi, K. Qaderi, A.M. Abasiyan, “Optimal Reservoir Operation
Using MOPSO with Time Variant Inertia and Acceleration
Coefficients”, Universal Journal of Agricultural Research, 1(3), 2013,
74-80.
[21] S. Jamshidi, M.H. Niksokhan, M. Ardestani, “Surface Water Quality
Management Using Integrated Discharge Permit and Reclaimed Water
Market”, Water Science and Technology, 70(5), 2014, 917-924.
[22] D.H. Burn, J.S. Yulianti “Waste-load allocation using genetic
algorithms”, Journal of Water Resource Planning and Management,
127(2), 2001, 121-129.
[1] E., Feizi Ashtiani, M. H. Niksokhan, M. Ardestani, “Multi-objective
Waste Load Allocation in River System by MOPSO”, International
Journal of Environmental Research, in press.
[2] D. H. Burn, B. J. Lence, “Comparison of optimization formulations for
waste-load allocations", Journal of Environmental Engineering, 118(4),
1992, 597-612.
[3] S. A. Mostafavi, A. Afshar, “Waste load allocation using non-dominated
archiving multi-colony ant algorithm”, Procedia Computer Science, 3,
2011, 64-69.
[4] N. Mahjouri, A. Ardestani, “game theoretic approach for interbasin
water resources allocation considering the water quality issues”,
Environmental Monitoring and Assessment, 167, 2010, 527–544
[5] S.R. Yandamuri, K. Srinivasan, S. Murty Bhallamudi, “Multiobjective
optimal waste load allocation models for rivers using nondominated
sorting genetic algorithm-II”, Journal of water resources planning and
management, 132(3), 2006, 133-143.
[6] W. Kurek, A. Ostfeld, “Multi-objective optimization of water quality,
pumps operation, and storage sizing of water distribution systems”,
Journal of Environmental Management, 115, 2013, 189-197. [7] B. Malekmohammadi, B. Zahraie, R. Kerachian, “Ranking solutions of
multi-objective reservoir operation optimization models using multi
criteria decision analysis”, Expert systems with applications, 38, 2011,
7851-7863.
[8] M.R. Nikoo, R. Kerachian, M.H. Niksokhan, “Equitable Waste Load
Allocation in Rivers Using Fuzzy Bi-matrix Games”, Water Resources
Management, 26(15), 2012, 4539-4552.
[9] E. Shirangi, R. Kerachian, M. Shafai Bajestan, “A simplified model for
reservoir operation considering the water quality issues: Application of
the Young conflict resolution theory”, Environmental Monitoring and
Assessment, 146 (1-3), 2007, 77-89.
[10] M. Saadatpour, A. Afshar, “Waste load allocation modeling with fuzzy
goals; simulation-optimization approach.” Water resources
management, 21(7), 2007, 1207-1224.
[11] P. Kalbar, S. Karmakar, S. Asolekar, “Selection of an appropriate
wastewater treatment technology: A scenario-based multiple-attribute
decision-making approach”. Journal of Environmental Management,
113, 2012, 158-169.
[12] N. Sa-nguanduan, V. Nititvattananon, “Strategic decision making for
urban water reuse application: A case from Thailand”. Desalination,
268, 2011, 141–149.
[13] D. Hidalgo, R. Irusta, L. Martinez, D. Fatta, A. Papadopoulos,
“Development of a multi- function software decision support tool for the
promotion of the safe reuse of treated urban wastewater”. Desalination,
215, 2007, 90–103.
[14] S. Jamshidi, S. Imani, “Wastewater Reuse, a Solution for Environmental
Risks Reduction and Value Index Development for Wastewater
Treatment Plants”, Value Engineering and Cost Management
Conference, 2014, Tehran, Iran.
[15] S. Hajkowicz, K. Collins, “A Review of Multiple Criteria Analysis for
Water Resource Planning and Management”, Water Resource
Management, 21, 2007, 1553–1566.
[16] G. Axelrad, E. Feinerman, “Regional planning of wastewater reuse for
irrigation and river rehabilitation”, Journal of Agricultural Economics,
60(1), 2009, 105–131.
[17] A. Pejman, G. Nabi Bidhendi, A. Karbassi, N. Mehrdadi, M. Esmaeili
Bidhendi, “Evaluation of spatial and seasonal variations in surface water
quality using multivariate statistical techniques”, International Journal
of Environmental Science and Technology, 6(3), 2009, 467-476.
[18] A.M. Baltar, D.G. Fontane, “Use of multi-objective particle swarm
optimization in water resource management”, Journal of water resource
planning and management, 134(3), 2008, 257-265.
[19] A. Azadnia, B. Zahraie, “Optimization of nonlinear Muskingum method
with variable parameters using multi-objective particle swarm
optimization”, World environmental and Water Resources Congress,
ASCE, 2010, 2278-2284
[20] I., Rahimi, K. Qaderi, A.M. Abasiyan, “Optimal Reservoir Operation
Using MOPSO with Time Variant Inertia and Acceleration
Coefficients”, Universal Journal of Agricultural Research, 1(3), 2013,
74-80.
[21] S. Jamshidi, M.H. Niksokhan, M. Ardestani, “Surface Water Quality
Management Using Integrated Discharge Permit and Reclaimed Water
Market”, Water Science and Technology, 70(5), 2014, 917-924.
[22] D.H. Burn, J.S. Yulianti “Waste-load allocation using genetic
algorithms”, Journal of Water Resource Planning and Management,
127(2), 2001, 121-129.
@article{"International Journal of Earth, Energy and Environmental Sciences:70043", author = "E. Feizi Ashtiani and S. Jamshidi and M.H Niksokhan and A. Feizi Ashtiani", title = "Value Index, a Novel Decision Making Approach for Waste Load Allocation", abstract = "Waste load allocation (WLA) policies may use multiobjective
optimization methods to find the most appropriate and
sustainable solutions. These usually intend to simultaneously
minimize two criteria, total abatement costs (TC) and environmental
violations (EV). If other criteria, such as inequity, need for
minimization as well, it requires introducing more binary
optimizations through different scenarios. In order to reduce the
calculation steps, this study presents value index as an innovative
decision making approach. Since the value index contains both the
environmental violation and treatment costs, it can be maximized
simultaneously with the equity index. It implies that the definition of
different scenarios for environmental violations is no longer required.
Furthermore, the solution is not necessarily the point with minimized
total costs or environmental violations. This idea is testified for Haraz
River, in north of Iran. Here, the dissolved oxygen (DO) level of river
is simulated by Streeter-Phelps equation in MATLAB software. The
WLA is determined for fish farms using multi-objective particle
swarm optimization (MOPSO) in two scenarios. At first, the trade-off
curves of TC-EV and TC-Inequity are plotted separately as the
conventional approach. In the second, the Value-Equity curve is
derived. The comparative results show that the solutions are in a
similar range of inequity with lower total costs. This is due to the
freedom of environmental violation attained in value index. As a
result, the conventional approach can well be replaced by the value
index particularly for problems optimizing these objectives. This
reduces the process to achieve the best solutions and may find better
classification for scenario definition. It is also concluded that decision
makers are better to focus on value index and weighting its contents
to find the most sustainable alternatives based on their requirements.", keywords = "Waste load allocation (WLA), Value index, Multi
objective particle swarm optimization (MOPSO), Haraz River,
Equity.", volume = "9", number = "6", pages = "638-5", }