Faculty of Engineering Optimization Day

 

Hosted by:

Department of Management Sciences,

Faculty of Engineering
Monday April 23rd, 2007
AL 116
(Please note the room Change)
 (Campus map)
 
 

 

Organized by:

 

Samir Elhedhli, Ph.D.,
Associate Professor,
Department of Management Sciences,
elhedhli-at-uwaterloo.ca

Tentative Schedule

 

8:30

_

9:00

 

Breakfast---Opening Remarks by Mike Worswick

 

9:00

_

9:25

 

Presentation 1

Prof. David Fuller

MSCI

A Large Scale Multi-Player Optimization Model to Assess Market Power in a Time of Use Electricity Market

9:25

_

9:50

 

Presentation 2

Prof. Lee Fu

CIVE

Optimization under Uncertainty with Applications in Intelligent Transportation Systems

9:50

_

10:15

 

Presentation 3

Prof. S. Elhedhli

MSCI

Large-scale optimization: Applications and solution approaches.

10:15

_

10:40

 

Presentation 4

Prof. James Craig

CIVE

Optimal Management of Groundwater-Surface Water Resources

10:40

_

11:55

Coffee break

11:00

_

11:25

 

Presentation 5

Prof. Beth. Jewkes

MSCI

Optimization Using Matrix Geometric and Cutting Plane Methods

 

 

 

 

11:25

_

11:50

 

Presentation 6

Masoud Mahootchi

SYDE

Reservoir Management with Reinforcement Learning

 

 

 

Prof. P. Ponnambalam & Prof. Hamid Tizhoosh

11:50

_

12:15

 

Presentation 7

Rafael Avalos

ECE/MSCI

Solving Voltage-Stability-Constrained Optimal Power Flows Using a   Singular Value Decomposition Technique

 

 

 

Prof. Miguel  Anjos & Prof.  Claudio Canizares

12:15

 

_

 

12:40

 

 

Presentation 8

Yasser Mustafa

 Prof. Ehab El-Saadany

ECE

Optimum size and location of wind based DG in distribution systems

12:40

 

_

 

1:35

 

 

Lunch break

 

 

 

 

 

Presentation 9

Hatem  ElBehairy

CIVE

Improving the Performance of Evolutionary Algorithms for Large-Scale Optimization of Bridge Deck Maintenance

1:35

_

2:00

Prof. Tarek Hegazy

 

 

 

Prof. Khaled Soudki

2:00

_

2:25

 

Presentation 10

Christina Moulton

SYDE

Post-Pareto Analysis of Evolutionary Multiobjective Programming Results for Landscape Ecology Planning

 

 

 

Prof. Paul Calamai

2:25

_

2:50

 

Presentation 11

Ismail El-Samahy

ECE

Provision of Reactive Power Ancillary Services in Deregulated Electricity Markets

 

 

 

Prof. Kankar  Bhattacharya & Prof. Claudio Canizares

2:50

_

3:15

 

Presentation 12

Sayyid Hasan Riyaz

ECE

 Span Indexed Greedy Algorithm for Task Planning in Multi Agent Systems

 

 

 

Prof. Otman Basir

3:15

_

3:30

Coffee break

 

 

 

 

Presentation 13

Abbas Ahmadi

ECE

A new approach for clustering data using

3:30

_

3:55

Prof. Fakhri Karray & Prof. M. Kamel

multiple cooperative swarms

 

 

 

 

Presentation 14

M. Ali Ulku

MSCI

Optimal Pricing and consolidation cycle for a shipper

3:55

_

4:20

Prof. Jim Bookbinder

 

 

 

 

Presentation 15

Youssef Saif

CHE

Optimal Design of Hybrid Membrane Networks for Wastewater Treatment and Minimization

4:20

_

4:45

Prof. Ali Elkamel & Prof. M. Pritzker

 

 

 

 

Presentation 16

Alice Malisia

SYDE

Applying Opposition-Based Ideas to Ant Colony Algorithms

4:45

_

5:10

Prof. Hamid Tizhoosh

 


Abstracts

 

Presentation 1:

A Large Scale Multi-Player Optimization Model to Assess Market Power in a Time

of Use Electricity Market

 

D.  Fuller

Department o f Management Sciences

 

The talk will outline a large scale model that represents the possible  behaviour of several large generating companies in a "bilateral" market, on a  transmission network, with time of use pricing.  The mathematical form of the model is a blend of Nash-Cournot and multi-commodity ideas, leading to a  formulation as a variational inequality problem (VIP).  VIPs are like  constrained optimization models, in that they have feasible regions defined by constraints, but unlike optimization models, there is not a single objective  function.  In this application, there is a profit objective function for each generating company.  Because of the connection to constrained optimization, it  has been possible to extend the classical Dantzig-Wolfe decomposition method.

 

Presentation 2:

 

Optimization under Uncertainty with Applications in Intelligent Transportation Systems

 

Lee Fu

Department o f Civil and Environmental Engineering

 

In this presentation, I will provide an overview of several optimization problems that I have been involved over the past 10 years.  Most of these problems are related to the specific transportation engineering field called Intelligent Transportation Systems (ITS), which is aimed at improving the management and operations of the existing transportation facilities by applying advanced sensors, telecommunication, and computer technologies.

Example problems include vehicle routing and scheduling in dynamic and stochastic networks, real-time transit operation control, locating of traffic changeable measure signs, and scheduling of winter road maintenance vehicles.

 

Presentation 3:

Large-scale optimization: Applications and solution approaches

 

Samir. Elhedhli

Department o f Management Sciences

 

Real-life optimization problems are typically large in size. Apart from linear programming, mixed and nonlinear programming problems could not be solved directly using commercial solvers. Fortunately, large-scale problems often exhibit special structure that can be exploited using decomposition approaches. In this talk, we briefly discuss the main decomposition principle and then discuss a number of  applications in logistics network

 

Presentation 4:

Optimal Management of Groundwater-Surface Water Resources


James Craig

Department o f Civil and Environmental Engineering


Both groundwater aquifers and surface water bodies (rivers, lakes, and wetlands) are under constant natural and human-induced stresses. While often treated as separate entities, the management of groundwater and surface water cannot be treated independently. Drinking and irrigation water is harvested from groundwater aquifers, lowering water tables and effectively removing water from rivers and wetlands. This reduces water levels and can have a significant effect upon aquatic ecosystems, recreation, and irrigation supplies downstream. Likewise, surface water is diverted to reservoirs, pipelines, and fields for irrigation, distribution, and power generation. This changes recharge rates to aquifers and significantly affects flow in the unsaturated zone, which can critically impact the sustainability of wetlands, transport of nutrients and contaminants, and the amount of moisture held in agricultural soils. Water resources managers must be able to optimize the mixed risks and benefits of these practices, both in terms of water quantity and quality. This talk will briefly summarize some of the key objectives and constraints in optimization of coupled surface-water/groundwater systems and the numerical models upon which such management practices are based

Presentation 5:

 

Optimization Using Matrix Geometric and Cutting Plane Methods

 

Beth. Jewkes

Department o f Management Sciences

The Matrix geometric method often proves very efficient in evaluating complex queuing systems. However, because it is a computational method, the performance measures obtained using matrix geometric method often restricts its utility in optimization models. We present a general framework for combining the matrix geometric approach with mathematical programming and an iterative cutting plane algorithm. Constraints to be satisfied in the problem are identified iteratively from results of the matrix geometric evaluation of the proposed system alternative, which are then added to the mathematical model for re-optimization until optimal solution is obtained. We illustrate the application of the proposed method to a problem of product pricing, capacity selection and delivery time setting in price and time sensitive markets, subject to service level constraints. The proposed approach is shown to work very efficiently, and solves the problem in a few iterations.

Presentation 6:

Reservoir Management with Reinforcement Learning

 

Masoud Mahootchi ,   P. Ponnambalam & H. Tizhoosh

Department of  Systems Design Engineering

 

Finding optimal or near-optimal solutions using large-scale optimization models for real-world problems is a problematic and challenging issue. Our focus here is to achieve optimal operations of surface water resources including multiple reservoirs in a unified system for short-term or long-term periods. This is a crucial and important problem because a system of reservoirs could potentially be sources for varieties of purposes such as drinking water, power generation, irrigation, flood control, recreation, navigation, and so on and water resources becoming more scarce. However, considering all various kinds of complexities, including stochasticity of the input parameters and nonlinearity/nonconvexity of objective functions and constraints makes the traditional optimizations methods inefficient in terms of computing time what is generally known in the literature on reservoir management as “the curse of dimensionality”

To tackle this main imperfection, we have applied a simulation-based optimization technique called Q-Learning. In order to speed up the convergence and increase the robustness of the Q-Learning method, we have also developed and implemented a new version of the opposition-based learning. We have used some stochastic optimization models to evaluate the efficiency of the method.

Presentation 7:

 Solving Voltage-Stability-Constrained Optimal Power Flows Using a

Singular Value Decomposition Technique

 

Rafael Avalos,  Miguel  Anjos & Claudio Canizares

Department o f Management Sciences & Department o f Electrical and Computer Engineering

 

This work proposes a method to enforce a voltage stability constraint in an Optimal Power Flow using the minimum singular value and minimum singular vectors of the power flow Jacobian. By ensuring this singular value is bounded away from zero, the voltage collapse point of the power system is avoided. A singular value decomposition of the power flow Jacobian at the current solution is carried out in an iterative process until the constraint is satisfied. The results show that this method can be a good alternative to other methods previously proposed for this problem.

 

Presentation 8:

Optimum size and location of wind based DG in distribution system

 

Yasser Mustafa, M.M. Salama  & . Ehab El-Saadany

Department o f Electrical and Computer Engineering

 

DG often offers a valuable alternative to traditional sources of electrical power for industrial, commercial, and residential loads, particularly, for rural areas where long transmission lines are used to transfer the electrical power from main source to end users. In this work an optimal power flow (OPF) algorithm is used to determine the optimum allocation of wind based DGs in order to minimizing the total power losses in the system. The OPF is formulated as Mixed Integer Non Linear Programming (MINLP); with an objective function to minimize the system power losses. The constraints include voltage limits at different buses (slack, generation, and load buses) of the system, feeder capacity, and maximum penetration limit of DGs. Moreover, the capacity factor of wind based DGs is calculated using Rayliegh pdf by the aid of the data of mean wind speed at the DGs locations and wind turbines data.

This proposed OPF is applied to a typical unbalanced Hydro One system with different scenarios including restrictions on the sizes and locations of DGs. The results show that the implementation of DGs has a great effect on the power losses reduction and scenario5b is the optimal case over other scenarios.

Presentation 9:

 

Improving the Performance of Evolutionary Algorithms for Large-Scale Optimization of Bridge Deck Maintenance

 

Hatem  ElBehairy, Tarek Hegazy & Khaled Soudki

Department o f Civil and Environmental Engineering

 

Bridges are vital links in infrastructure road networks and require frequent maintenance and repair in order to keep them functional throughout their service lives. Bridge decks are considered the most vulnerable component of bridges as they are exposed to harsh environment and increasing traffic volume. Prioritization of bridge decks for maintenance and repair purposes involves decisions that bring the highest return on the repair budget. These decisions, however, represent complex optimization problems that traditional optimization techniques are often unable to solve. This research introduces an integrated model for bridge deck repairs with detailed life cycle costs of network-level and project-level decisions. Two evolutionary-based optimization techniques, namely genetic algorithms and shuffled-frog-leaping are then applied on the model in order to optimize maintenance and repair decisions. Results of both techniques are compared on case study problems with different numbers of bridges. To improve their performance on large scale problems, various experiments were conducted with different objective functions, model formulations, parameter settings, and pre/post processing functions. The best optimization strategy for this typical infrastructure problem was determined to be a year-by-year optimization strategy, coupled with the use of a pre-processing function to allocate repair funds first to critical bridges.

Presentation 10:

Post-Pareto Analysis of Evolutionary Multiobjective Programming Results for Landscape Ecology Planning

 

Christina Moulton, Paul Calamai, &  Steven Roberts

Department of Systems Design Engineering

 

Multiobjective genetic algorithms such as the Non-dominated Sorting Genetic Algorithm II (Deb, Pratap, Agarak, & Meyarivan, 2002) can generate an even distribution of points over the Pareto front (Mattson

& Messac, 2005).  Many real world applications involve many objective function and the Pareto front may contain a very large number of points.  Choosing a solution from such a large set is potentially intractable for a decision maker (Purhouse & Fleming, 2003).

Cluster analysis can be used to group the Pareto front into subsets of similar solutions.  This approach is applied to a land use change problem in an urban fringe area in Southern Ontario, Canada.  Using NSGA we generate potential land use plans which are Pareto optimal on 8 landscape ecology metrics.  These plans are clustered based on their performance on the objective functions.

Presentation 11:

Provision of Reactive Power Ancillary Services in Deregulated Electricity Markets

 

Ismail El-Samahy,  Kankar  Bhattacharya & Claudio Canizares

Department o f Electrical and Computer Engineering

 

Power system operators in deregulated electricity markets are required to make provisions for reactive power services in order to maintain an adequate level of grid security and reliability. Consequently, there is a need to devise appropriate payment mechanisms for such services, considering their effect on system operating conditions. These payments should truly reflect the cost associated with reactive power production, which depends on the region of operation of the synchronous generator as a reactive power service provider. Thus, the optimization problem for service procurement should be modeled in a way that assigns, for each generator, only one of the operating regions.

 

Formulating the procurement problem as a mixed integer non-linear programming (MINLP) problem has been previously proposed in literature. However, it is computationally complex to augment this model with security constraints such as thermal limits on transmission lines. Thus, a three-step iterative scheme based on the classification of reactive power from a generator is proposed in this work to eliminate the integer constraints, and hence reformulate the problem as a non-linear programming (NLP) instead of a MINLP problem. The proposed algorithm reduces the computational burden and allows for the inclusion of essential security constraints.

Presentation 12:

Span Indexed Greedy Algorithm for Task Planning in Multi Agent Systems

 

Sayyid Hasan Riyaz and Otman Basir

Department o f Electrical and Computer Engineering

 

Multi Agent Systems (MAS) are often employed in applications having varying and uncertain dynamics resulting from a number of sources such as operational environment, agent attributes and task dynamics. Traditional static task planning optimization techniques often prove infeasible for such systems and alternate dynamic techniques are considered that are responsive to the variations and uncertainties of the system. Market mechanisms based techniques using greedy algorithm have been a popular choice for MAS task planning due to

their simplicity and effectiveness. Solutions based on greedy algorithm, although feasible, are not guaranteed to be optimal thus providing an

opportunity of improvement in the solution quality. We propose a novel Span Indexed Greedy Algorithm (SIGA) for dynamic task scheduling and allocation in MAS and demonstrate its efficiency and efficacy through comparative studies and analysis with the greedy algorithm and the optimal solutions.

Presentation 13:

A new approach for clustering data using multiple cooperative swarms

 

Abbas Ahmadi , Fakhri Karray, Mohamed Kamel

Department o f Electrical and Computer Engineering

 

Particle swarm optimization (PSO) is a search technique which is mainly introduced to deal with optimization problems by defining an objective function, called fitness function. It starts from an initial population and explores the feasible solution region, using several particles through a number of iterations to reach a near optimal solution. Due to its abilities, PSO has been applied in other applications such as classification and clustering. Current PSO-based techniques have been developed using a single swarm.

We propose a new clustering technique using multiple swarms. This technique imitates the behavior of biological swarms which search for food situated in several places. The proposed technique considers multiple cooperating swarms to find centers of clusters.

This task is done during two phases: initialization and exploration. In the initialization phase, it assigns a portion of the feasible solution region to each swarm. In the exploration phase, the search to find the near optimal solution proceeds using cooperating swarms. As compared to the single-swarm-based clustering technique, multiple cooperating swarms provide more effective solutions for the clustering task as the dimensionality of data and number of clusters increase. The proposed technique outperforms k-means clustering and single-swarm-based clustering techniques.

Presentation 14:

Optimal Pricing and consolidation cycle for a shipper

 

M. Ali Ulku &   Jim Bookbinder

Department o f Management Sciences

 

We consider a shipper whose customers are sensitive to both price and delivery-time guarantee. For various pricing schemes and via classical optimization methods, we study the problem of maximizing the shipper's profit per unit time through the optimal choice of consolidation cycle length, when a contract carrier is employed. We show that the problem is very parameter-sensitive, and the best choice of pricing scheme differs with management's particular objective. Moreover, we investigate the market conditions in which shipment consolidation might not be preferred at all. Contrary to intuition, it turns out that charging according to an order's time of arrival is not necessarily the best pricing scheme. We provide managerial insights and include a numerical example with various sensitivity analyses.

Keywords: pricing, shipment consolidation, logistics, price-and delivery-time sensitiveness

 

Presentation 15:

Optimal Design of Hybrid Membrane Networks for Wastewater Treatment and Minimization

 

Y. Saif, A. Elkamel and M. Pritzker

Department of Chemical Engineering

 

  The current research addresses mainly deterministic optimization problems of water desalination and industrial wastewater treatment networks by hybrid membranes. The mathematical optimization problem involves the solution of a nonconvex mixed integer nonlinear system of equations (MINLP). The mathematical models are formulated with the view of creating general superstructures which give a rich number of alternatives for an optimum treatment network. Several case studies will be tackled to highlight the reliability and efficiency of membranes related to water and wastewater treatments. A global optimization method is proposed to solve the membrane network based on the reformulation-linearization technique (RLT). Preliminary results are presented to illustrate the feasibility of the solution methodology.

Presentation 16:

 Applying Opposition-Based Ideas to Ant Algorithms

 

Alice R. Malisia and Hamid R. Tizhoosh

Department of Systems Design Engineering

 

The concept of opposition-based learning (OBL) was recently proposed by Dr. Tizhoosh to extend different machine learning algorithms. The main idea of OBL is to consider opposite estimates, actions or states as an attempt to increase the coverage of the solution space and to reduce exploration time. OBL has already been applied to reinforcement learning, neural networks and genetic algorithms.  Our goal is to apply OBL to ant algorithms. This presentation will provide an overview of the investigations that have been conducted. We will present five different extensions applied to a particular version of ant algorithms: ant colony system (ACS). The modifications focus on the solution construction phase of the ant colony system. Three of the proposed methods work by pairing the ants and synchronizing their path selection. The two other approaches modify the decisions of the ants by using an opposite-pheromone content. Results on the application of these algorithms to Travelling Salesman Problem instances demonstrate that the concept of opposition is not easily applied to the ant algorithm. Only one of the pheromone-based methods showed performance improvements that were statistically significant. The quality of the solutions increased and more optimal solutions were found. The other extensions showed no clear improvement.  Further work must be conducted to explore the more successful pheromone-based approach. Additionally, we began experimenting with a different application: search of a goal within a grid environment. Moreover, it would be important to establish the benefits and limitations of applying opposition to ant algorithms.