Systems Courses

DOGEE 570.305 Environmental Engineering Systems Design
Fall 2010

Course Syllabus

Course Times:           Lecture TTh 10:00-11:45, Ames 302

Course Credits: 4

Prerequisites: 500.200 Introduction to Computing for Engineers and Scientists or equivalent.

Instructor: Prof. Hugh Ellis
210 Ames Hall
516-6537
hugh.ellis@jhu.edu

JHU Catalog Description: Techniques from systems analysis applied to environmental engineering design and management problems: reservoir management, power plant siting, nuclear waste management, air pollution control, and transportation planning. Design projects are required.

Required Text: None

Grading: 8-9 homework assignments (25 %), a mid-term exam (25 %), a final exam (30 %) and a project (20 %).

Course Objectives:
1)    Learn the fundamentals of applied optimization
2)    Develop competence in formulating optimization models and translating problem descriptions into mathematically solvable models
3)    Learn systems techniques including linear programming, integer, multiobjective, stochastic, and dynamic programming
4)    Solve challenging engineering problems that involve constrained resource allocation
5)    Effectively communicate systems methods and modeling results

Course Outcomes:

This course introduces students to systems analysis concepts and techniques applied to engineering problems. The focus is primarily on optimization methods but simulation is addressed as well. Students demonstrate their knowledge of the course material through problem sets and examinations. A major component of the course is the team project where students select and execute a comprehensive computing project.

Contribution of course to meeting ABET professional component:

This is a core course in engineering science for majors in environmental engineering.

Relationship of course to undergraduate program outcomes:

EE1(b) – Understand and apply the principals upon which engineering practice is based: mathematics and scientific computation.
EE1(d) – Understand and apply the principals upon which engineering practice is based: engineering science.
EE4 – Be able to design systems, components, or processes that provide engineering solutions to environmental problems given realistic economic, social, political, ethical, health, safety, and sustainability constraints.
EE5 – Demonstrate critical thinking skills and ability for independent study needed to engage in life-long learning.
EE6 – Possess the knowledge and skills to identify, formulate, and implement solutions to engineering problems using modern engineering tools and synthesizing different fields of knowledge.
EE7 – Communicate clearly orally and in writing, and function effectively in multidisciplinary teams.

Course Topics

Introduction to linear programming

  • activity analysis, classic problem formulations, graphical solution methods
  • properties of linear programs
  • extreme points, basis vectors, convexity
  • the Simplex algorithm
  • derivation of the Simplex procedure, sign-unrestricted variables
  • computational implementation: manual tableaus, spreadsheet-based solvers

Duality theory

  • the primal/dual relationship, economic interpretation of dual variables, complementary slackness

Multiobjective programming

  • approach philosophy, weighting and constraint methods, Kuhn-Tucker conditions,

Integer programming

  • integer programming formulations, branch and bound, computational implementation: spreadsheet-based solvers

Deterministic dynamic programming

  • the principle of optimality, state-stage system representations, recursive structure, serial multistage decision systems

Nonlinear programming

  • convexity, local optimality, piecewise linearization, nonlinear optimization basics

Stochastic programming

  • review of probability/statistics fundamentals, chance constrained programming, stochastic linear programming, two-stage programming with recourse, stochastic dynamic programming

Markov decision processes

  • probability transition matrices, steady-state probabilities,  finite state Markov processes, completely observable MDP’s, brief introduction to partially observable MDP’s

Optimal spatial interpolation

  • classical weighting methods for spatial interpolation, semivariogram estimation, Kriging

Stochastic simulation

  • random number generation, normal deviate generation, correlated random vector generation

Kalman filtering

  • state-space system representation, ordinary and recursive least squares estimation, minimum variance state estimation

Time series analysis

  • correlation, covariance, autoregressive Markov models, ARMA, ARIMA

 

Prepared by:  Hugh Ellis

Revised: 05/19/2011

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DOGEE 570.418  Multiobjective Programming and Planning

Spring 2011

ABET Syllabus

Credits: 3 credit hours

Instructor:  Dr. Justin Williams

Textbook:   J.L. Cohon, Multiobjective Programming and Planning, Dover, reprinted 2000.

Other required readings:

*  Schilling, D. A., C. ReVelle, and J. Cohon, 1983, “An approach to the display and analysis of multiobjective problems”, Socio-Economic Planning Sciences 17(2), pp. 57-63.

*  Solanki, R. S., P. A. Appino, and J. L. Cohon, 1993, “Approximating the noninferior set in multiobjective linear programming problems, European Journal of Operational Research 68, pp. 356-373.

*  Ulungu, E. L. and J. Teghem, 1994, “Multi-objective combinatorial optimization problems: a survey”, Journal of Multi-Criteria Decision Analysis 3, pp. 83-104.

*  Wright, J., C. ReVelle, and J. Cohon, 1983, “A multiobjective integer programming model for the land acquisition problem”, Regional Science and Urban Economics 13, pp. 31-53.

This course is an introduction to approaches to thinking about tradeoffs among multiple objectives in policy and the design and operation of engineering systems.   The course addresses both valuation (how can we compare “apples and oranges”?) and description of tradeoffs for optimization problems (how much improvement in one objective can we get if we give up some of another objective?)  Linear programming methods for generating non-inferior solutions will be covered, as well as preference-based methods for evaluating alternatives in single and multi-decision maker contexts.

This is an elective course.  Prerequisite: a course in operations research with linear programming (e.g. 570.305 or 570.495 or 550.361 & 362).

Course Goals:

a.  To be able to use linear and mixed-integer multi-objective programming (MOP) methods to generate and display tradeoffs among multiple objectives for engineering and policy analysis.

b.  To be able to apply several commonly used multi-criteria decision making (MCDM) methods for quantifying people’s priorities, and to understand their theoretical foundations.

c.  To be able to critique the use of MOP and MCDM methods in engineering and policy analysis.

Course Outcomes:

a.  Students will demonstrate their mastery of the mechanics and theoretical underpinnings of multiobjective (MOP and MCDM) methods by completion of homework problem sets and in a final examination.  Homeworks and the final exam will also document the ability of students to formulate and solve multiobjective optimization models and to display and analyze the resulting tradeoffs.

b.  Students will demonstrate their ability to apply these methods in two computer projects that require the use of optimization (linear programming) software.

c.   Each student will select and review a paper published in a peer-reviewed journal.  This review will be a critique of the assumptions, methodology, and execution of a multiobjective engineering or policy application.

Topics to be covered:  introduction  multiple objectives problems in policy and planning; review of math concepts and terminology; multi-objective programming (MOP) concepts and terminology; Karush-Kuhn-Tucker (KKT) conditions for optimality; KKT conditions for non-inferiority; MOP generating methods — weighting method; MOP generating methods — NISE method; MOP generating methods — constraint method; MOP generating methods — multi-objective simplex method; displaying non-inferior solutions; multi-objective integer programming (IP); multi-objective IP methods and modeling; goal programming; geometric methods for finding a best compromise solution; preference-based methods (PBM) — introduction; PBM — multi-attribute utility theory; PBM — value function methods; PBM — utility function assessment; PBM — goal and reference point methods; PBM — outranking methods; PBM — applications.

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DOGEE 570.496 Mathematical Models for Managing Urban and Environmental Systems

Spring 2010

ABET Syllabus

Credits: 3 credit hours

Instructor:  Dr. Justin Williams

Course readings:

(a) Selected chapters from Design and Operation of Civil and Environmental Engineering Systems, ed. by C. ReVelle and A. McGarity, John Wiley, NY, 1997 (“R&M”);

(b) Selected chapters from Facility Location: Applications and Theory, ed. by Z. Drezner and H. W. Hamacher, Springer, NY, 2001 (“D&H”);  and

(c) Handouts:

*  Branas, C. C. and C. S. ReVelle, 2001, “An iterative switching heuristic to locate hospitals and helicopters”, Socio-Economic Planning Sciences 35, pp. 11-30.

*  Glover, F., C. C. Kuo, and K. S. Dhir, 1995, “A discrete optimization model for preserving biological diversity”, Applied Mathematical Modelling 19, pp. 696-701.

*  Hof, J., M. Bevers, D. Uresk, and G. L. Schenbeck, 2002, “Optimizing habitat location for black-tailed prairie dogs in southwestern South Dakota”, Ecological Modelling 147, pp. 11-21.

*  Kingsland, S. E., 2002, “Creating a science of nature reserve design: perspectives from history”, Environmental Modeling and Assessment 7, pp. 61-69.

*  ReVelle, C. S., J. C. Williams, and J. J. Boland, 2002, “Counterpart models in facility location science and reserve selection science”, Environmental Modeling and Assessment 7, pp. 71-80.

*  Williams, J. C., C. S. ReVelle, and S. A. Levin, 2005, “Spatial attributes and reserve design models: a review”, Environmental Modeling and Assessment 10, pp. 163-181.

Description of Part 1 (Urban Systems Models):  The first part of this course is an introduction to mathematical optimization models for facility siting and transportation network design in an urban / regional planning and policy making context.  Topics include: (a) linear and mixed-integer programming models for the location of supply chain facilities (e.g., production plants, warehouses), public facilities (e.g., schools, libraries), emergency response facilities (e.g., fire stations, hospital trauma centers), and “obnoxious” facilities (e.g., landfills, hazardous waste storage); and (b) network-based optimization models for airline hub location, hazardous waste routing, and ride-sharing.

Description of Part 2 (Environmental Systems Models):  The second part of the course is an introduction to mathematical optimization models in the fast-growing field of planning and management for biological conservation.  Decision models for making land use, management, and policy decisions are examined when biological and natural resources conservation must be considered together with economic objectives.  Topics covered include:  management of a critical target species; reserve site selection for multiple species; spatially optimized reserve designs; and wildlife corridors.

Both parts of the course will focus on problem description and model formulation.  Types of models used include linear programming, mixed integer programming, and discrete-time dynamic models.  Analytical, algorithmic, and computer-based solution methods will be discussed and applied to problems.

This is an elective course.  Prerequisite: a course in operations research with linear programming (e.g. 570.305 / 495, or 550.361 & 362).

Educational Goals:

a. To improve skills in formulating and interpreting optimization models by applying them to a range of urban/ regional and biological conservation planning, policy, and decision problems.

b. To learn about advanced methods and applications in engineering optimization, including linear and mixed-integer programming.

c. To develop skills in solving optimization problems involving the design and operation of natural and human-built systems, and to generate solutions using both computer solvers and analytical methods.

Contribution to Professional Component:

a. Mathematics, engineering, and ecological principles, including logical interdependencies are integrated in order to identify improved design and operating policies for facility location, transportation, and conservation management systems.

b. Societal constraints and objectives are explicitly considered in the formulation of the objective functions and feasible regions of optimization models.

Course Outcomes:

a. In homework problem sets, students will demonstrate their ability to formulate and solve optimization models for a range of urban/ regional and biological conservation planning, policy, and decision problems.

b. Students will apply linear and mixed-integer programming (optimization) methods in two computer assignments, demonstrating their understanding of the methods’ mechanics and use.

c. Students will demonstrate their knowledge of modeling theory, formulations, and applications, and problem contexts in a comprehensive final exam.

List of topics to be covered:

Part 1:  the transportation problem; capacitated warehouse & plant location problems; P-median problem; CPLEX solver tutorial; capacitated P-median location problem; P-center location problem; location set covering problem; maximal covering location problem; dispersion (anti-covering) problem; trauma center & helicopter location problem; hazardous waste routing problem; hazardous waste storage location problem; complete-assignment hub location problem; single-assignment hub location problem; ride-share problem.
Part 2:  introduction, overview, concepts; types of models – four examples; biodiversity maximization problem; reserve set covering and maximal covering problems; reserve maximal expected covering problem; reserve compactness and buffer zones; wildlife corridors; reserve connectivity and proximity; single species management – prairie dogs.

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DOGEE 570.497: Risk and Decision Analysis Course Syllabus & Schedule

Fall 2010

Seth Guikema, 205 Ames Hall, sguikema@jhu.edu, 410-516-6042. Office Hours: Monday, TBD

Course Description
This course introduces students to the methods of probabilistic risk analysis and decision analysis. Both quantitative and qualitative methods will be covered. Topics will include qualitative risk analysis methods (risk lists, matrices, FEMA, FMECA, etc.), quantitative engineering risk analysis methods (fault trees, event trees, etc.), environmental health risk analysis methods, decision bases, the axioms underlying decision analysis, and quantitative decision analysis methods (decision trees, utility functions, risk attitude, value of information calculations, etc.). The focus of this course is on the fundamentals of risk and decision analysis rather than their application in a particular field. Examples will be given from a variety of different fields.

Course Objectives
After taking this class students should be able to:

  • Describe the decision basis for a given decision situation
  • Compose a decision tree to describe a decision situation
  • Solve for the utility-maximizing choice using a decision tree
  • Calculate the value of perfect and imperfect information in a given situation
  • Use event trees to estimate risk in a given situation
  • Select and then use appropriate methods to estimate risk in a given situation
  • Understand and manage the role of public perception and the media in forming views about risk due to engineering projects and systems
  • Make informed decisions about the risks they face in daily life
 

Through these objectives students are expected to develop toward the following ABET outcomes:

  • Ability to apply knowledge of basic mathematics, science, and engineering to solving engineering problems
  • Ability to design a engineering system to meet desired needs while incorporating engineering standards and realistic constraints such as those based on economic, environmental, sustainability, constructability, ethical, health and safety, social, and political issues
  • Ability to formulate and solve civil and environmental engineering problems
  • Understanding of professional and ethical responsibility
  • Ability to communicate effectively in oral and written forms
  • Understanding of the impact of civil engineering solutions in a global/political/societal context
  • Ability to use modern tools, techniques, and computation methods necessary for civil and environmental engineering practice
  • Ability to apply probability, statistics, and economics in engineering decisions
 

Course web page: Access will be through course web page via Blackboard.

Topics, Assignments, and Class Preparation
Topics, reading assignments and homework exercises are described and will be available on the course web page. Reading assignments should be completed prior to the class for which they are assigned. I will likely not cover all of the reading material in class but you will be responsible for assigned readings. Class attendance and participation will affect your grade. Please come to class prepared to ask and answer questions. The course’s “Work Policies” and “Examination Rules” that will be attached to the course web site are made a part of this syllabus by reference. Please read and follow them.

Grading
Homework             10%
Project                    25%
Examination #1       25%
Final Examination (comprehensive – may be take-home)        40%
Total  100%

The grade for each student will be determined according to following scale:

Grade A B C D F
Score 90-100 80-89 70-79 60-69 <60

However, the minimum score needed to get a specific grade may be lowered at the discretion of the Professor. It will not be raised. For example, if a student earns a score of 89 he or she is guaranteed to get at least some form of B. This student may get an A- or better. Similarly, if a student earns a score of 80, he or she will be guaranteed to get some form of B. It is possible for 100% of the class to get an A (or F!) if the entire class earns these grades.

Homework
There will be homework assignments through the semester. Portions of these assignments will be grade, but for other portions points will be assigned for completeness. Solutions will be distributed in class. You are strongly encouraged to work together on these assignments. Some of these assignments may take the form of small case studies for which group work will be required.

Required Text
There will not be a required textbook.

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Infrastructure Asset Management (Draft Syllabus)

Designation: Graduate and elective for undergraduate senior

Pre-Requisite: N/A

Recommended Textbook and Reading Materials:

  • W.R. Hudson, R. Haas and W. Uddin, Infrastructure Management, McGraw-Hill, 1997
  • Readings will be drawn from academic and professional journals, reports of the National Academies and other organizations, professional manuals, and print and on-line media.  Reading materials will be posted on Blackboard.
 

Course Description and Objectives:
Modern society and economies rely on the infrastructure to move goods, people, and information safely and reliably. However, the vital infrastructure is at continuing risk from factors ranging from physical deterioration and underinvestment to natural hazards and acts of terrorism. It is necessary to operate, maintain, and upgrade infrastructure facilities cost-effectively using engineering and mathematical analyses with business practice and economic theory.

The course will introduce graduate and advanced undergraduate students to the concept of infrastructure asset management. This course reviews the status of infrastructure and infrastructure management, the analytical methods, tools, data, technologies, and political and financial framework and constraints for managing infrastructure systems and facilities as assets. This course explains the concepts and principles underlying asset management and introduces the students the concepts of cost-effective life-cycle management of infrastructure assets by integrating design, construction, maintenance, rehabilitation, and renovation strategies.

Topics of discussion include analytical methods, development of data collection technologies, nondestructive evaluation, database management, geographical information system (GIS), life-cycle economic analysis, performance modeling, prioritization, and optimization. Types of infrastructure considered in the course include: pavements, bridges, drainage and sewer systems, etc.

Upon completing this course, students are expected to understand the issues involved in infrastructure asset management. Students are expected to develop ability to function on multi-disciplinary teams, ability to identify, formulate, and solve engineering problems, and understanding of the impact of engineering solutions in a global and societal context. Students are expected to demonstrate a basic understanding of the fundamental principles of professional communications: written, oral and visual, in their term project reports and oral presentations.

The syllabus provides the general framework for the course. However, the instructors reserves the right to make any modifications or changes to the course, depending on the class progress, or on any special circumstance that may arise during the semester.

Topics Covered (Tentative):
1. Introduction to Infrastructure Asset Management (IAM) and the need for IAM
2. Life-cycle analysis concepts; IMS framework; needs assessment; performance indicators
3. Database management; GIS; inventory, historical & environmental data; remote sensing technologies
4. In-service monitoring and evaluation; nondestructive evaluation
5. Performance modeling; failure analysis
7. Modeling techniques; sampling design and statistical analysis
8. Total quality management; design and construction phases; new materials and concepts
9. Maintenance, rehabilitation & reconstruction (M,R&R) strategies; intervention policies)
10. Agency costs; user costs; benefits; life-cycle economic analysis; computer applications
11. Establishing priorities; optimization; M,R&R programming and budget analysis
12. IMS implementation; interface with other management systems; innovative technologies
13. IMS case studies and examples

Class Schedule and Office Hours

Each week the three-hour time slot will be used for lecture and discussion. This may include videos, in-class exercises, and guest lecturers. The class will ordinarily meet on ________.  However, I do travel for the other responsibilities of my job and thus may miss some lectures.  These lectures will be made up at times that are acceptable to most, but not necessarily all students.

Regarding office hours, please note that there will inevitably be times during the semester when scheduled office hours conflict with other schedules such as travel, and etc. I will try my best to notify the students via e-mail about any changes ahead of time.

Grading and Requirements:

Grading will be based on homework assignments, class participation, final exam, and project.

Grade Distribution:
Homework Assignments               40 %
Final Exam (take-home)                20 %
Term Project and Presentation       30%
Class Participation                      10 %

Assignments and Exam

The due date for the homework assignment that is required for each unit will be specified by the instructor.  Homework should be turned in at the beginning of the class on the assigned due date.  Do not place homework in my mailbox.  Assignments turned in up to one day late will receive 50% credit, after which no credit will be given.  Your assignment should be turned in with your name, course number, assignment number, and page number on each sheet. Students are expected to perform their homework neatly and in an organized fashion. Neatness and presentation are important and will be considered when grading assignments. Any homework which is sloppy, difficult to read, or difficult to understand will receive a reduced grade. Finally, it is the responsibility of the student to determine the correct solution of the problems, which contained errors.

No make-up exams and assignments are given or accepted except for medical or other similar hardships where advanced arrangements are made with the instructor; or in case of non-selective medical emergencies with appropriate physician’s note or documentation. Other than circumstances describe above, failure to take the exam or turn in assignments at the scheduled time will constitute a grade of zero in the exam and assignment. It is the student’s obligation to contact the instructor, generally before the examination so that appropriate arrangement (if any) may be made. It should be noted that the effort a student puts into performing and understanding the homework is often reflected in the student’s performance on the exams.

Various journal/conference papers will be handed as reading assignments during the semester.  Students are responsible for all material in the assigned readings.  The material readings cover will not necessarily be discussed in detail in the lecture.

Attendance & Special Arrangements

Regular attendance and participation in the class is the best way to grasp the concepts and principles being discussed. The students are responsible for any material covered during their absence. The students are expected to read the material to be discussed in class before coming to the class.

If you observe a religious holiday and would like to ask for a change in your schedule, please inform me at least a week ahead of it.  Also I will make every effort to accommodate you if you need a special arrangement due to your disability.

Attendance in Inclement Weather: Official closures and delays are announced on the campus website, as well as on local radio and TV stations. If inclement weather conditions force to cancel a class even though the university is open, students will be notified through blackboard email system. If you have any questions, please contact the instructor.

Academic Ethics Statement

Cheating is wrong. Cheating hurts our community by undermining academic integrity, creating mistrust, and fostering unfair competition. The university will punish cheaters with failure on an assignment, failure in a course, permanent transcript notation, suspension, and/or expulsion. Offenses may be reported to medical, law, or other professional or graduate schools when a cheater applies.

Violations can include cheating on exams, plagiarism, reuse of assignments without permission, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition. Ignorance of these rules is not an excuse.

You may collaborate with other students in this course, however each student must do his or her own homework and case studies. Discussion among students on homework assignments and cases is encouraged for clarification of assignments, technical details of using software, and structuring major steps of solutions—especially on the course’s discussion site in Blackboard. Students must do their own work on the homework assignments and exam. If you have questions about this policy, please ask the instructor.

On every exam, you will sign the following pledge: “I agree to complete this exam without unauthorized assistance from any person, materials or device. [Signed and dated]”

For more information, see the guide on Academic Ethics for Undergraduates at http://portalcontent.johnshopkins.edu/bin/g/u/Johns%20Hopkins%20Ethics%20Guide.pdf

Term Project and Presentation

During the semester, you are required to complete a case study of a particular infrastructure management problem as the term project. This term project will be a group project and include a written report of a topic related to the course and a 15 minutes in-class presentation of the selected topic.  Each presentation will be followed by a 5 minutes Question & Answer session.  Each group should be composed of two students.  The groups are required to prepare their presentations in Microsoft Power Point and to present it using a computer projector. Slides and transparencies are discouraged; however, if the groups would like to use these materials they should consult with the instructor ahead of time.  The groups are advised to arrive 30 minutes early and prepare (i.e. loading the file, ensuring the file is accessible, etc.).  The grading scheme is based on the instructor’s grade and peers’ grades.  The presentation grade will be based on organizational skills, time management, clarity of visuals, and delivery of the speech.

The groups may select a topic among the ones covered in class or other topics after consulting with the instructor.  Students working on a project (i.e. thesis, dissertation, scholarly paper) are encouraged to select a topic relevant to their area of interest.  If you are having problems with project topics, please discuss with the instructor and the instructor provides some ideas. The topic should be focusing on identifying the types of infrastructure assets to be considered, describing current management strategy/plan, developing infrastructure asset management frame work, formulating model or decision analyses (such as cost & benefit analyses) for managing (as opposed to planning or costing) infrastructure, presenting recommendations or conclusions. Please consider the boundary of the problem that you want to cover in your term project and ensure the particular problem you choose will fit your term project so that you can make progress on it before the end of the semester.

Maximum report length is 15 pages and this includes double-spaced text, figures, tables, references, and appendices.  Points will be taken off for reports exceeding 15 pages.  A font size of 12 and a font type of Arial or Times New Roman should be used.  A cover sheet and a table of contents should be provided and they are not included in the 15 page maximum length.  Equations should be properly written using the Equation Editor.  Handwritten equations or table/figure captions are not acceptable.  Figures should be drawn using a computer plotting software.  However, if they are scanned from a source, the scan quality will be considered in grading.  Neatness and organization of the paper will also be considered during grading.  It is strictly forbidden to directly copy sentences from references.  Students are expected to read and understand a number of references and summarize the information that they obtained in their own words.  The content of the report should provide a general background and historical perspective of the subject matter, and analysis and discussion of the applicable design methods used.

All sources used must be properly referenced.

In addition, a one-page summary of the report should be distributed to the class (including the instructor) on the presentation day.  The summary should include a brief background of the selected topic with an emphasis on recommendations, considerations, or results.  A list of 3-4 recommended references on the selected topic should also be included at the end of the summary.

Due Dates for term project:

  • MM/DD: Project topic proposal due (submit the summary report, no more than two pages)
  • MM/DD: In class presentation of case studies.
  • MM/DD: Written report due

Sample Homework Assignments (Tentative)

  • Homework Week 4: Information Management and Database

Write an essay (no more than 5 pages) on data management. Discuss what information is important. Students will assume the role of a data management team in a public agency and define a process for determining how a decision is made on what information is needed and at what cost.

  • Homework Week 5: Geographic Information System (GIS)

This assignment requests students register at ESRI Vitual Campus and complete online free training module “Getting Started with GIS (for ArcGIS 10)” (approximately 9 hours). This training module provides a foundation for understanding what a geographic information system is and the possibilities it offers for discovering patterns, relationships, and trends. Students will learn how GIS maps are different from other types of paper and digital maps, what makes the data used in a GIS unique, and how to use GIS software to obtain information and create meaningful maps. In interactive exercises and activities throughout the course, students will work with ArcGIS software and see how a GIS supports problem solving in many different contexts. Certificates of Completion provided by ESRI at the end of each Virtual Campus courses will be used as proof of course completion, and must be provided to the instructor by the due date in order to obtain credit for this homework assignment.

  • Homework Week 8: Evaluation of Alternatives and Prioritization

Read the US Department of Transportation (DOT) “Asset Management Primer” (available on the Internet athttp://www.fhwa.dot.gov/infrastructure/asstmgmt/amprimer.pdf).  Write a summary of this document that includes a comparison of their approach to the issues we have discussed in class, as well as any relevant criticism or differences from issues we have discussed. Also compare the DOT Primer with the following paper suggesting how the US Federal government should organize its information infrastructure and management: http://www.nap.edu/html/whitepapers/ch-2.html.  Please limit your answer to 2-3 pages double spaced.

  • Homework Week 9 Infrastructure Asset Management Cases Study -1: Bridge Management System

Access the National Bridge Inventory Database (http://nationalbridges.com/). Using only the limited amount of public data available on the website for Maryland (from 1998-2002), create a ‘management system’ capable of estimating the following values over a 40-year period:

  • the number of bridges that will be structurally deficient and functionally obsolete
  • the amount of expected expenditure on these bridges to improve them

Be sure to consider the effect of new bridges constructed and demolished bridges. Note that completing this will likely require some use of the probabilistic or statistical models discussed in class. You can assume functional forms, estimate parameters, etc.

Center for Systems Science and Engineering