A Dynamic Interval Goal Programming Approach to the Regulation of a Lake-River System Raimo P. Hämäläinen Juha Mäntysaari S ystems Analysis Laboratory.

Slides:



Advertisements
Similar presentations
Multi‑Criteria Decision Making
Advertisements

Desktop Business Analytics -- Decision Intelligence l Time Series Forecasting l Risk Analysis l Optimization.
1 Research Topics Group: Professor Raimo P. Hämäläinen Graduate School Seminar University of Jyväskylä.
Goal Programming How do you find optimal solutions to the following?
Water Resources Planning and Management Daene C. McKinney Optimization.
Water Resources Systems Modeling for Planning and Management An Introduction to the Development and Application of Optimization and Simulation Models for.
S ystems Analysis Laboratory Helsinki University of Technology 1 We have the tools How to attract the people? Creating a culture of Web-based participation.
Climate and Management Alternatives in Snake River Basin Nathan VanRheenen and Richard N. Palmer Dept. of Civil and Environmental Engineering University.
Mika Marttunen Finnish Environment Institute R., P. Hämäläinen Helsinki University of Technology Project web page: DECISION ANALYSIS.
Linear Programming Optimal Solutions and Models Without Unique Optimal Solutions.
1 Swiss Federal Institute of Technology Computer Engineering and Networks Laboratory Classical Exploration Methods for Design Space Exploration (multi-criteria.
1 Contents college 3 en 4 Book: Appendix A.1, A.3, A.4, §3.4, §3.5, §4.1, §4.2, §4.4, §4.6 (not: §3.6 - §3.8, §4.2 - §4.3) Extra literature on resource.
Nathan VanRheenen Richard N. Palmer Civil and Environmental Engineering University of Washington Recasting the Future Developing.
S ystems Analysis Laboratory Helsinki University of Technology A Preference Programming Approach to Make the Even Swaps Method Even Easier Jyri Mustajoki.
S ystems Analysis Laboratory Helsinki University of Technology Decision Support for the Even Swaps Process with Preference Programming Jyri Mustajoki Raimo.
Systems Analysis Laboratory Helsinki University of Technology e-Learning Negotiation Analysis Harri Ehtamo Raimo P Hämäläinen Ville Koskinen Systems Analysis.
1 Building Strong! THE ECONOMIST’S ROLE Ken Claseman Senior Policy Advisor for Economics Office of Water Project Review HQUSACE
Masoud Asadzadeh 1, Masoud Asadzadeh 1, Saman Razavi 1, Bryan Tolson 1 David Fay 2, William Werick 3, Yin Fan 2 2- Great Lakes - St. Lawrence Regulation.
The Upper Rio Grande. Multi-objective River and Reservoir System Modeling Flood Control Water Supply Navigation Aquatic/Riparian Habitat Recreational.
S ystems Analysis Laboratory Helsinki University of Technology Using Intervals for Global Sensitivity and Worst Case Analyses in Multiattribute Value Trees.
Dr. David Ahlfeld Professor of Civil and Environmental Engineering The Westfield River Basin Optimization Model Demonstration:
IE 607 Constrained Design: Using Constraints to Advantage in Adaptive Optimization in Manufacturing.
1 S ystems Analysis Laboratory Helsinki University of Technology Kai Virtanen, Tuomas Raivio, and Raimo P. Hämäläinen Systems Analysis Laboratory (SAL)
1 S ystems Analysis Laboratory Helsinki University of Technology Decision and Negotiation Support in Multi-Stakeholder Development of Lake Regulation Policy.
Helsinki University of Technology Systems Analysis Laboratory Ahti Salo and Antti Punkka Systems Analysis Laboratory Helsinki University of Technology.
An efficient distributed protocol for collective decision- making in combinatorial domains CMSS Feb , 2012 Minyi Li Intelligent Agent Technology.
Methods for Developing Robust Climate Adaptation Plans in the Energy Sector David Groves Water and Climate Resilience Center.
DSS Modeling Current trends – Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions – Influence diagram.
1 S ystems Analysis Laboratory Helsinki University of Technology Kai Virtanen, Raimo P. Hämäläinen and Ville Mattila Systems Analysis Laboratory Helsinki.
E-participation Requires Systems Intelligence Paula Siitonen and Raimo P. Hämäläinen Helsinki University of Technology, Systems Analysis Laboratory Marcelo.
S ystems Analysis Laboratory Helsinki University of Technology We have the tools How to attract the people? Creating a culture of Web-based participation.
Chiara Mocenni Roma, June 4-5, 2009 Dept. Information Engineering – Centre for the Study of Complex Systems – Univ. of Siena The “DITTY” DSS In the DITTY.
S ystems Analysis Laboratory Helsinki University of Technology Can We Avoid Biases in Environmental Decision Analysis ? Raimo P. Hämäläinen Helsinki University.
Introduction A GENERAL MODEL OF SYSTEM OPTIMIZATION.
Multicriteria Driven Resource Management Strategies in GRMS Krzysztof Kurowski, Jarek Nabrzyski, Ariel Oleksiak, Juliusz Pukacki Poznan Supercomputing.
1 S ystems Analysis Laboratory Helsinki University of Technology Goal intervals in dynamic multicriteria problems The case of MOHO Juha Mäntysaari.
2 nd International Conference Graz, October 10 th, 2012 SHARP PP 2: Region of Western Macedonia Fig. 1: Comparing different scenarios with the use of DSS.
Mika Marttunen Mikko Dufva Finnish Environment Institute Jyri Mustajoki Tampere University of Technology Timo P. Karjalainen Thule Institute, University.
S ystems Analysis Laboratory Helsinki University of Technology 1 Raimo P. Hämäläinen Jyri Mustajoki Systems Analysis Laboratory Helsinki University of.
S ystems Analysis Laboratory Helsinki University of Technology Practical dominance and process support in the Even Swaps method Jyri Mustajoki Raimo P.
Goal Programming Linear program has multiple objectives, often conflicting in nature Target values or goals can be set for each objective identified Not.
James VanShaar Riverside Technology, inc
Modeling Development CRFS—Technical Meeting November 14, 2012.
FORS 8450 Advanced Forest Planning Lecture 5 Relatively Straightforward Stochastic Approach.
Dr. Richard Palmer Professor and Head Department of Civil and Environmental Engineering University of Massachusetts Amherst.
Central Valley Project Cost Allocation Study -- Irrigation and Municipal & Industrial (M&I) Benefits Public Meeting August 9, 2013.
Resource allocation and optimisation model RAOM October 2003.
Helsinki University of Technology Systems Analysis Laboratory 1DAS workshop Ahti A. Salo and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki.
What is OASIS? Water resources simulation/optimization model
PCWA MFP Operations Model All Models are Wrong But Some Models are Useful.
MCDA can be realized in many ways A. Decision makers and experts use MCDA on their own, no stakeholders involved B. Stakeholders opinions are.
Linear Programming Optimal Solutions and Models Without Unique Optimal Solutions.
Target Releas e Component 1 Component 3 Baseline Flow Component 2 Design a regulation plan for Lake Superior that:  is easily interpretable (piecewise.
S ystems Analysis Laboratory Helsinki University of Technology 1 Decision Analysis Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University.
Helsinki University of Technology Systems Analysis Laboratory Incomplete Ordinal Information in Value Tree Analysis Antti Punkka and Ahti Salo Systems.
1 Scenario formulation Scenario-based planning is a structured way of thinking about what might happen in the future Scenarios are descriptions of possible.
Systems Analysis Laboratory Helsinki University of Technology An e-Learning module on Negotiation Analysis Harri Ehtamo Raimo P.
1 S ystems Analysis Laboratory Helsinki University of Technology Master’s Thesis Antti Punkka “ Uses of Ordinal Preference Information in Interactive Decision.
1 S ystems Analysis Laboratory Helsinki University of Technology Effects-Based Operations as a Multi-Criteria Decision Analysis Problem Jouni Pousi, Kai.
Oct 1999 The After Effects of the 1999 fishery: Catch and Discard Rates in the 2000 fisheries in the re-opened closed areas.
S ystems Analysis Laboratory Helsinki University of Technology 15th MCDM conference - Ankara Mats Lindstedt / 1 Using Intervals for Global.
P4 Advanced Investment Appraisal. 2 2 Section C: Advanced Investment Appraisal C1. Discounted cash flow techniques and the use of free cash flows. C2.
Mustajoki, Hämäläinen and Salo Decision support by interval SMART/SWING / 1 S ystems Analysis Laboratory Helsinki University of Technology Decision support.
Solver Feature Excel’s KY San Jose State University Engineering 10.
Solver Feature Excel’s KY San Jose State University Engineering 10.
Raimo P. Hämäläinen Juha Mäntysaari
Goal Programming How do you find optimal solutions to the following?
Raimo P. Hämäläinen Systems Analysis Laboratory
Planning process in river basin management
Dr. Arslan Ornek DETERMINISTIC OPTIMIZATION MODELS
Presentation transcript:

A Dynamic Interval Goal Programming Approach to the Regulation of a Lake-River System Raimo P. Hämäläinen Juha Mäntysaari S ystems Analysis Laboratory Helsinki University of Technology Systems Analysis Laboratory Helsinki University of Technology

Päijänne-Kymijoki lake river system LAKE PÄIJÄNNE KONNIVESI RUOTSALAINEN RIVER KYMIJOKI km Jyväskylä LAKE PYHÄJÄRVI Lahti Kotka Finland

4:th largest in Finland Control: Outflow from Päijänne to the river Kymijoki Inflows: forecasted Regulation policies: Water levels at six time points Päijänne-Kymijoki lake river system

Need for modelling Development of feasible regulation strategies is a dynamic control problem –No intuitive solutions –Planning againts long historical inflow data –Analysis of regulation impacts –Many interest groups  multicriteria optimization in a dynamic system

Goals in terms of water levels Users give desired water levels at: –six different points during one year –ideal level + acceptable interval (min, max)

Dynamics of the lake Päijänne

Constraints Outflow from Päijänne: Min/max flow Fixed and hard Max change in outflow: Soft, violation penalties Water level in the lake Pyhäjärvi: Fixed rule based regulation Part of the dynamics

Criteria and penalty functions Criterion for goal levels: Quadratic cost for differences of goal points from regulated water levels Penalty outside the goal interval: Quadratic difference from the limits (min or max) Penalty for violation of change in outflow rate: Quadratic cost outside the maximum flow limit, otherwise zero

Cost function minimized = Sum of deviations from goal + penalty outside goal intervals Criteria and penalty functions

Model assumptions Lake dynamics Optimization against one to four year history Lower dam regulation by a given rule Regulator uses a rolling two goal optimization principle Adjustment rules

Generation of the optimal regulation strategy

Goal programming Goal (infeasible point) Problem: Find a point in the feasible set closest to the goal point/set  Weighted, Min Max, Lexicographic Aspects in regulation: –Dynamic problem –Goal interval (set) d Goal point/set cost function

Why goal programming ? Economic, social and environmental impacts 37 primary + 20 secondary = 57 different impacts For example: Power production, flood damages, number of destroyed loon nests Some impacts are interdependent: energy produced and the value of energy  Use of tradeoff comparison questions or criteria classification becomes difficult

ISMO spreadsheet application

Minimizes deviations from goal levels and goal intervals Satisfies flow constraints Simulates the regulator’s operating principles Preference model Set of goal levels + acceptability intervals Optimization againts history data for a selected one to four year period Modifiable parameters Flow constraints in the river steepness of the penalty function ISMO spreadsheet application

Use of ISMO

ISMO example

Inflow

Utopia and realistic solutions

Impacts Nature –Spawning areas for pike fish –Water level when ice melts –number of destroyed loon nests Social –Recreational losses –Professional fishing: Reduction of the water level during 10-Dec and 28-Feb Economic –Power production –Flood damages –Days infavourable for log floating

Comparison of impacts:  User evaluates and modifies goal levels

ISMO is implemented in MS Excel 7.0 (MS Office 95) –Solver provides optimization routines –10-20 minutes for one solution Benefits –Rapid development –Easy: data input, model modification, visualisation and printing Users accept easily –Excel is a commonly used office program Spreadsheet modelling works !

Generation of alternative regulation strategies Impact tables of regulation –a key info material in decision analysis interviews and conferences Sensitivity tool –individual changes for water levels and related impacts –helps representatives to better understand the restrictions of the system Added value

Further development Different information patterns Iterative optimization of the goal levels to produce maximum amount/value of the energy Now used to develop new regulation policies and their impacts

References –Marttunen, M., Järvinen, E. A., Saukkonen, J. and Hämäläinen, R. P., “Regulation of Lake Päijänne - a Learning Process Preceding Decision-Making”, Finnish Journal of Water Economy, 6:29-37, –Hämäläinen, R. P., Kettunen, E., Marttunen, M. and Ehtamo, H., “Evaluating a framework for multi-stakeholder decision support in water resources management”, Manuscript, (Downloadable from –Hämäläinen, R. P. and Mäntysaari, J., “A Dynamic Interval Goal Programming Approach the Regulation of a Lake-River System”, Manuscript, (Downloadable from Publications/pdf-files/mhama.pdf) –Hämäläinen, R. P., “Interactive Multiple Criteria Decision Analysis in Water Resources Planning”, Home pages of the Lake Päijänne project, 1998,