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Viewing: ISE 560/OR 560 : Stochastic Models in Industrial Engineering

Last approved: Sat, 23 Apr 2016 08:16:33 GMT

Last edit: Wed, 20 Apr 2016 15:39:20 GMT

Change Type
Major
ISE (Industrial and Systems Engineering)
560
032251
Dual-Level Course
No
Cross-listed Course
Yes
Course Prefix:
OR
Stochastic Models in Industrial Engineering
Stochastic Models in IE
College of Engineering
Fitts Department Industrial & Systems Engineering (14IE)
Term Offering
Fall, Spring and Summer
Offered Every Year
Fall 2015
Previously taught as Special Topics?
Yes
5
 
Course Prefix/NumberSemester/Term OfferedEnrollment
ISE 589Spring 20118
ISE 589Fall 201116
ISE 589Fall 201226
ISE 589-002Fall 201328
ISE 589-601Fall 20135
ISE 589-002Fall 201431
Course Delivery
Face-to-Face (On Campus)
Distance Education (DELTA)

Grading Method
Graded/Audit
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Julie Ivy
Associate Professor
Full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture3535NoN/A
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Delivery FormatPer SemesterPer SectionMultiple Sections?Comments
LEC2020NoN/A

Is the course required or an elective for a Curriculum?
Yes
SIS Program CodeProgram TitleRequired or Elective?
14IEMRIndustrial Engineering- MRElective
14IEMSIndustrial Engineering- MSElective
?Operations ResearchElective
ISE/OR 560 will introduce mathematical modeling, analysis, and solution procedures applicable to uncertain (stochastic) production and service systems. Methodologies covered include probability theory and stochastic processes including discrete and continuous Markov processes. Applications relate to design and analysis of problems, capacity planning, inventory control, waiting lines, and service systems.

Our curriculum currently only has a PhD level course in stochastic models this is not appropriate for Masters level students. This course is designed for Masters level and upper level undergraduate students and focuses on the application of stochastic models in a variety of industries. 


No

Is this a GEP Course?
GEP Categories

Humanities Open when gep_category = HUM
Each course in the Humanities category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Mathematical Sciences Open when gep_category = MATH
Each course in the Mathematial Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Natural Sciences Open when gep_category = NATSCI
Each course in the Natural Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Social Sciences Open when gep_category = SOCSCI
Each course in the Social Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Interdisciplinary Perspectives Open when gep_category = INTERDISC
Each course in the Interdisciplinary Perspectives category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Visual & Performing Arts Open when gep_category = VPA
Each course in the Visual and Performing Arts category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Health and Exercise Studies Open when gep_category = HES
Each course in the Health and Exercise Studies category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
&
 

 
 

 
 

 
 

Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
Please complete at least 1 of the following student objectives.
 

 
 

 
 

 
 

 
 

 
 

US Diversity Open when gep_category = USDIV
Each course in the US Diversity category of the General Education Program will provide instruction and guidance that help students to achieve at least 2 of the following objectives:
Please complete at least 2 of the following student objectives.
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Requisites and Scheduling
 
a. If seats are restricted, describe the restrictions being applied.
 

 
b. Is this restriction listed in the course catalog description for the course?
 

 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 

 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 

Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

College(s)Contact NameStatement Summary
College of SciencesDGP Statistics
This course will be taught as a part of the instructor's normal teaching load. No additional materials are required.

This course has three components:


I.  Probability Tools: characterizing uncertainty using probabilities and random variables.


II. Decision Modeling under Uncertainty: models for decision making under uncertainty: decision tress, utility theory, value of information, Bayes rule.


III. Stochastic Modeling: characterizing uncertainty over time and space using:


A.Discrete Time Markov Processes: Markov Chains & Markov decision processes


B.Continuous Time Markov Processes: Birth & Death Processes, Poisson Processes and Queueing Theory


C.Probabilistic Inventory Models


Student Learning Outcomes

Upon the completion of this course, students will be able to use the tools of probability and stochastic processes to develop models to improve decision-making in an uncertain environment. Specifically:


Probability Objectives: Students will be able to:



  • Identify and Apply probability distributions appropriately to various applications

  • Formulate and Calculate probability distributions (unconditional and conditional) for single and multivariate random variables


Decision Modeling Objectives: Students will be able to:



  • Formulate/Structure decision problems using tables and decision trees

  • Use various criteria to evaluate decision problems

  • Estimate the value of perfect and sample information

  • Demonstrate understanding of utility theory and calculate a utility

  • Formulate dynamic decision problems using a Markov decision process


Stochastic Modeling Objectives: Students will be able to:



  • Define and characterize a stochastic process

  • Identify, Define and Apply stochastic models particularly, Markov chains (discrete and continuous), queueing models and inventory models

  • Develop Stochastic Models for various real world applications

  • Apply stochastic modeling theory to a real world problem. 


Evaluation MethodWeighting/Points for EachDetails
Quizzes30%There are two period long quizzes that are similar to midterm exams.
Final Exam35%There is one cumulative final exam that is held during the final exam period.
Homework20%There are multiple homework assignments throughout the semester. Some homework assignments are individual and others the students work in small (two to three person) teams.
Project15%There is a group project that involves the application of stochastic modeling to a real-world problem. Students work in teams of 4 to 6 people.
TopicTime Devoted to Each TopicActivity
Probability3 -4 classes This includes a review of probability theory, introductions of conditional probability and conditional expectation.
Decision Modeling2 classesThis involves the introduction of decision making under uncertainty including decision trees.
Markov Chains: DTMC6-7 classesThis involves the introduction of discrete time Markov chains and their application
Markov Decision Processes3-4 classesThis involves the introduction of Markov decision process formulation and solution methods for infinite horizon problems
Continuous Time Markov processes5-6 classesThis involves the introduction of continuous time Markov processes with a focus on Poisson processes and birth and death processes
Queueing3-4 classesThis involves the introduction of queueing systems and their relationship to CTMC.
ghodge (06/09/15 3:00 pm): need to add consultation with DGP of Statistics; need to update and re-upload syllabus to include all items (refer to checklist 8, 9, 11, 12, 13, 14); Hodge changed weeks to 16 (15 weeks lecture plus 1 week final exam) and Changed contact hours from 45 to 3 (per week); Added SIS codes for IE, but need to add SIS codes for OR. Please contact Melissa Nosbisch with questions.

ghodge 08/26/2015 need to incorporate comments from ABGS initial reviewers befor sending to whole board.

ABGS Reviewer Comments:
-Abbreviated title should be longer and more descriptive (to differentiate from other stochastic models courses on campus)
-Does this overlap with other stochastic processes courses?
-Insert summary of consultation under "Additional Comments" or in body of course action. Possible consultation with Statistics or Biomathematics
-Objective 2 under Learning Outcomes: use a stronger word than "understand," possibly, "Calculate?"

Julie Ivy's Response: I have consulted with the instructors of courses with similar names: ECE 579 and ST 546 as well as the DGP from statistics. Each of these individuals has agreed that the proposed course differs sufficiently from their courses particularly based on the fact that the proposed course is non-measure theoretic and focuses on the application of stochastic modeling to IE-based problems. There responses have been attached under the additional documentation section.

mlnosbis 9/14/2015: Instructor should provide documentation of a consultation with the Biomathematics DGP, as that program has a similar course (BMA 773)

Julie Ivy, 9/17/2015: I have consulted with the Biomathematics DGP, Alun Lloyd, regarding the proposed course relative to their courses. While they feel there is theoretical overlap with BMA 772 they believe the applications are distinct and the overlap is minimal. Also they do not cover queueing theory or inventory models. Please refer the attached response above.
reeves (Thu, 07 May 2015 13:30:40 GMT): Problems that need to be addressed: SIS program codes added The percentages of credit do not add up to 100% Should probably have consult with/approval from Statistics (ST546), possibly Computer Science (CSC579)
rfillin (Fri, 08 May 2015 19:08:14 GMT): Rollback: Once you make changes I and submit for approval please let me know.
ghodge (Tue, 09 Jun 2015 19:00:21 GMT): need to add consultation with DGP of Statistics need to update syllabus to include all items(refer to checklist 8, 9, 11, 12, 13, 14) Changed weeks to 16 (15 weeks lecture plus 1 week final exam) Changed contact hours from 45 to 3 (per week) Added SIS codes for IE
mlnosbis (Mon, 14 Sep 2015 18:59:24 GMT): Rollback: ABGS reviewers indicate that a consultation with Biomathematics is needed.
mlnosbis (Tue, 15 Sep 2015 14:37:33 GMT): The action was mistakenly taken out of the workflow. I approved at each role to bring the course to its current position (ABGS Coordinator).
Key: 7254