Syracuse University Classroom Utilization Study Final ... · Final Presentation . August 13, 2010 ....

© 2010 IBM Corporation

Syracuse University Classroom Utilization Study Final Presentation

August 13, 2010

IBM/SU Confidential 2

Executive Summary Background

– The current class schedule is a product of incremental changes over the years.

– The room assignment and scheduling process is completed at both the university-level and at individual colleges and department.

Project Objectives

– Increase SU’s understanding of classroom usage and trends

– Enhance SU’s classroom utilization approach with consideration of scheduling and facility constraints

Approach

– Gathered and analyzed past classroom data, analyzed the current state of classroom utilization.

– Using ILOG CPLEX, developed an optimization model that determines the optimal course-to-classroom mapping.

Key Findings

– Provided SU a complete and in-depth picture of its classroom inventory and how it is used today

– Without adding new classrooms or making any changes in class times, provided an approach for SU to better assign course to classrooms to improved faculty and student experience

• For example, a 50% improvement in seat utilization and a 30% improvement in location mapping.

– “Stress test” results (squeezing in as many classes as possible) showed that SU’s current classroom inventory can support a maximum of 18% additional usable class times.


Background The current class schedule is a product of incremental changes over the years.

– The schedule contains implicit rules and preferences, including course conflicts, technology requirements, faculty preferences.

The room assignment and scheduling process is completed at both the university-level and at individual colleges and department.

Class utilization can be Improved by

– Taking a comprehensive approach, considering all courses and rooms at the same time

– Establish rules and explicit requirements that provides clear guidance

– Pooling of captive and registrar classrooms to enable a more effective use of existing resources

Potential benefits of improved classroom utilization

– A balance of optimizing the overall university classroom utilization while meeting individual department’s needs

– Avoid unnecessary costs to construct new buildings or outfit existing rooms to meet future class needs

– An improved student and faculty experience

– Increase in flexibility to accommodate future classes and/or repair needs


Project Objectives

Syracuse University is interested in enhancing its classroom utilization approach, including

– Increase SU’s understanding of current classroom usage and trends

– Establish a comprehensive and systematic analytical approach to assign courses to rooms, with consideration of scheduling and facility constraints

– Understand the potential of current facility level and identify areas of improvements.


Pre-Analysis


Pre-Analysis: Objective and Approach

To get a better understanding of the classroom inventory and how it is used today.

Gathered and analyzed past classroom schedule and inventory data. – Data Cleansing: accounted for cross-listings and incomplete classroom information – Data Source: All Registrar classrooms and the corresponding classes held during Fall

2009

Based on the Fall 2009 class schedule, the analysis: 1. Report classroom utilization per (i) days of the week and time of day, (ii) classroom

types, and (iii) department 2. Report seat fill rates per (i) days of the week and time of day, (ii) classroom types, and

(iii) department 3. Show vacancy times for each classroom 4. Identify classes that meet outside of the typical class times 5. Identify academic colleges and subjects that have a disproportion percentage of

classes that meet during prime time.


Period Utilization: All Registrar Classrooms MWF

• Max # of Classrooms = 165 • Max # of Class Hours per Time Block is approximated = 165*80min ~ 220 hour • Note that 55-min classes are included as part of the 80-minute interval when the time periods overlap, for example, classes held on Monday from 12:45-1:40pm are included in the 12:45-2:05 slot

# of Class Hours per Time Blocks for Monday, Wedensday & Friday

0

20

40

60

80

100

120

140

160

180

200

8:00-9:20am(80min) or 8:25-9:30am (55min)

9:30-10:25am(55min)

10:35-11:30am(55min)

11:45-12:35pm(55min)

12:45-2:05pm(80min) or 12:45-1:40pm (55min)

2:15-3:35pm(80min)

3:45-5:05pm(80min)

5:15-6:35pm(80min)

# of

Cla

ss H

ours

MON WED FRI


Period Utilization: All Registrar Classrooms TTh

Observations: Classroom utilization varies greatly across different days of the week. Descending order of utilization: Tue, Thur, Wed, Mon, Fri. Very few Friday classes in the last three blocks (12:45-2:05pm, 2:15-3:35pm, 3:45-5:05pm) Rooms are highly utilized in both AM and PM on Tuesdays & Thursdays. Rooms are more utilized in PM than AM on Mondays & Wednesdays.

# of Class Hours per Time Blocks for Tuesday & Thursday

0

20

40

60

80

100

120

140

160

180

200

8:00-9:20am(80min)

9:30-10:50am(80min)

11:00-12:30pm(80min)

12:30-1:50pm(80min)

2:00-3:20pm(80min)

3:30-4:50pm(80min)

5:00-6:20pm(80min)

# of

Cla

ss H

ours

TUE THUR


Technology Type UtilizationNo Technology 42%V/D Projection 46%V/D Projection with inboard computer and document camera 60%Advanced Installation possible multiple projectors 56%Cluster 32%Overall Average 54%

Period Utilization: Per Different Classroom Types Monday to Friday (8am to Various End Times*)

Room Capacity Utilization1-20 42%11-35 51%35-50 58%51-75 63%76-100 53%101-200 54%>200 44%Overall Average 54%

Room Type UtilizationClass/Multi-use 27%Cluster 32%Lecture Hall/Aud 51%Seminar Room 52%Tablet Arm Classroom 55%Overall Average 54%

Note: * Various End Times: MW 6:35pm TTh 6:20pm F 5:05pm Cells are highlighted when period utilization > 60% Travel time between classes are not included in this analysis, which equals to roughly 10% additional utilization.


Optimization Model: Fall 2009 Classroom Assignment


Approach

Using ILOG CPLEX, IBM developed an optimization model that determines the optimal course-to-classroom mapping. The model considered all the relevant operational constraints and faculty/administrator

preferences simultaneously and automatically

OUTPUTS INPUTS CLASSROOM ASSIGNMENT MODEL

Optimal Room Assignment

Class Requirements

Faculty & Student Preferences

University Priorities

Stress Test: Determined the Maximum Additional Course Capacity

Registrar Rooms


• Period Taught •Day of the week •Start and end times

•Enrollment Capacity •Technology Index





Graphical Illustration

•Department or Building •Period Taught

•Day of the week •Start and end times

•Enrollment Capacity •Technology Index •Fall 2009 Room Assignment

Course ID

Course ID

Course ID

Course ID







• Building ID •Classroom Capacity •Technology Index

•Period •Day of the week •Start and end times

Classroom ID

Classroom ID

Classroom ID

Classroom ID

Qty of 2358 courses Examples: •Course ID 16759, Writing 105, Section M220 •Course ID 16765, Writing 105, Section M240 •Course ID 19003, History 111, Section M009

Qty of 165 Classrooms Examples: •Bldg MC14, Rm AG202 •Bldg EO32, Rm SHAW029G

Assign

Implied Data


Business Rules

The mathematical model assigns courses to classrooms such that

1. Each course is assigned to at most one classroom and the assigned classroom meets the course's must-have requirements, including

• Technology, seating arrangement and capacity (seat-fill rate <= 1.0)

2. Each classroom has no more than one class at any instance (no double-booking)

The #1 model objective is to ensure all courses are assigned and meet the above two requirements.

When the #1 priority is met, the model will maximize the nice-to-have features, in descending order of priorities:

1. Proximity to the department location

2. Achieving a seat-fill rate between 0.7 to 1.0 (maximum seat-fill rate = enrollment cap / room cap. Since enrollment tends to be 10% less than enrollment cap, this characteristics will result in actual seat-fill rate will around 0.6 to 0.9)

3. Keeping the same room assignment as prior history The Tie Breaker


Course Assignment Tradeoffs

200

300

400

500

600

700

800

900

1000

1100

1200

600 700 800 900 1000 1100 1200 1300

Courses Held in Non-Department Buildings

Cour

ses

with

Util

izatio

n <

70%

D = Cost for Different Building

U = Cost for Utilization < 70%

D and U varied from 10 to 1000

Cost for Moving = 1

D/U = 2 D/U > 10

D/U = 1

U/D = 2 U/D > 10

Baseline Condition

Suggested Cost Tradeoff


Course Assignment Results

Based on discussions with the Working Group, we adopted the following priority:

Using the plot on previous slide, we adopt the condition in which (Cost for Utilization < 70%) / (Cost for Different Building) = 2. Specifically:

– Cost of move = 1, Cost of different building = 100, Cost of not meeting seat utilization = 200, Cost of Not Assigning a Course = 10,000

This condition leads to the following improvement over the baseline case, including a 65.6% reduction in classes that do not utilize at least 70% of seating:

#3. Avoid Move #1. Meet Seat Util Target #2. Stay in Dept Bldg


Optimization Model: Stress Test


Stress-Test: Objective and Approach

Objective: Evaluate the degree to which SU can gain extra classroom capacity by simply re-assigning the course-to-room assignment (without changing the actual time-schedule)

Approach: Estimate the number of additional courses we can fit in the current set-up

1. Generate “dummy courses”: one for each class block per classroom • Each dummy course can only be assigned to one of the classrooms.

Hence, there are 165 x 16 = 2,640 dummy classes • This ensures that if there is any possibility of “squeezing” in a class

under the current set up, there is a dummy class to be placed in that slot.

2. Include “dummy courses” in the model, give them 2nd priority (after the real courses).

3. Based on model result, determine how many additional courses the current set-up can accommodate.


Stress-Test: A Graphical Illustration (part 1)

8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pmRoom 1 Class 1 Class 2Room 2 Class 2 Class 3Room 3 Class 4 Class 5

8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pmRoom 1 Class 1 Class 2Room 2 Class 2 Class 3 New Class 1Room 3 Class 4 New Class 2 Class 5

Current Class Assignment

Improvement 1: Finding open slots without re-assigning rooms


Stress-Test: A Graphical Illustration (part 2)

8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pmRoom 1 Class 1 Class 2Room 2 Class 2 Class 3Room 3 Class 4 Class 5

8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pmRoom 1 Dummy a Dummy b Dummy cRoom 2 Dummy d Dummy e Dummy fRoom 3 Dummy g Dummy h Dummy i

Current Class Assignment

Improvement 2: Finding open slots with room re-assignments

Dummy Classes

ILOG Model

+

8am 9am 10am 11am 12pm 1pm 2pm 3pm 4pm 5pm 6pmRoom 1 Class 1 Dummy b Dummy cRoom 2 Class 2 Dummy e Class 5Room 3 Class 4 Class 3 Class 2


Stress-Test: Major Findings

Class Counts

Class Type Mon/Wed/Fri Tue/ThurCurrent 1,252 1,109Additional (dummy) 338 222Total 1,590 1,331

% Increase in Classes (Maximum possible) 21.3% 16.7%

We showed that SU’s current classroom inventory can support a maximum of 21.3% additional usable class times on MWF and 16.7% on TTh, according to the SU standard class blocks, – Current classes include all the classes that meet on any one of the week days. – “Dummy” classes are set up such that it meets during the standard class block.


Stress-Test: # of Current and “Dummy” Classes on MWF

** Additional Classes are designed to meet during the exact class blocks in all 3 days (Mon, Wed & Fri)

Mon/Wed/Fri # of Current and Additional Class Hours

0

20

40

60

80

100

120

140

160

180

8:00-9:20am

9:30-10:25am

10:35-11:30am

11:40-12:35pm

12:45-2:05pm

2:15-3:35pm

3:45-5:05pm

5:15-6:35pm

Monday Wednesday Friday Additional*


Stress-Test: # of Current and “Dummy” Classes on TTh

** Additional Classes are designed to meet during the exact class blocks in both Tuesday & Thursday.

Tue/Thur # of Current and Additional Class Hours

0

20

40

60

80

100

120

140

160

180

8:00-9:20am

9:30-10:50am

11:00-12:20pm

12:30-1:50pm

2:00-3:20pm

3:30-4:50pm

5:00-6:20pm

Tuesday Thursday Additional*


Effect on “Nice-to-Have” Features

# Courses that Moved

# Courses in

Different Building

# Courses with

Utilization < 70%

Baseline

- 1209 1155

Basic Analysis

1245 1112 397

Stress Test

1271 1124 406

With the additional number of “Dummy” classes, there is a minor effect on the current class (the actual classes) assignment in terms of the “nice-to-have” features:

The addition of dummy courses had a relatively small effect on the “nice-to-have feature” for the existing courses.


Prime-Time Analysis


Prime-Time Analysis

School Prime Time Limit NotesUniversity of Arizona MWF 9-3 & TTh 9:30-3:30 start times <70%University of California-Berkeley 9am to 3pm <70%Brandeis University 10am to 3pm <50%

Duke University 2 to 4 <50% for <300-level classes

Exception: Dept with less than 10 <300-level courses; Labs/Discussion Session excluded

Brown UniversityIndiana University - Bloomington 9:05 am to 2:29 pm <55%University of Maryland - College Park 9 to 3 <80% <70% in MWF, <45% in TThUniversity of Minnestota - Twin Cities 9 to 3 <60%Ohio State University - Columbus 9:30 to 2:30 <15% in any single hour

University of Southern California 8 to 3Between 16% and 23% in any single hour

University of Virginia

Objectives – To understand the concentration of classes in prime time among different

colleges and a selected set of subjects. – To inform potential policy decisions on prime time scheduling requirement

Background: Prime-time definition & scheduling requirements among various universities:


Prime Time Analysis per Academic Colleges

Colleges During Prime Time (1) Total % in Prime Time (2)

A&S 1,004 1,557 64%ARCH 13 20 65%ECS 72 125 58%

EDUC 30 97 31%HUEC 57 107 53%

IST 20 41 49%MGMT 114 200 57%UCNC 0 4 0%VPA 35 65 54%Total 1,345 2,216 61%

# of Classes

(1) Classes are considered to be during prime time if it's start time falls within one of the following:Mon/Wed 9:30am to 3:35pmTue/Thur 9:30am to 3:20pm

Fri 9:30am to 12:35pm(2) Cells are highlighted in yellow if the % of classes in Prime Time > 55% (note that the 21 out of 38 of the standard class blocks are within prime time, ~55%)


Prime Time Analysis for Selected-Subjects

Colleges Subjects During Prime Time (1) Total % in Prime Time (2)

A&S MAT 115 172 67%A&S PPA 14 30 47%A&S WRT 90 151 60%ECS ECS 24 40 60%ECS ELE 17 25 68%

EDUC EDU 8 28 29%EDUC EED 4 10 40%HUEC NSD 12 24 50%HUEC SWK 11 19 58%MGMT ACC 21 34 62%MGMT EEE 11 25 44%

PC COM 21 48 44%PC TRF 18 33 55%

# of Classes

(1) Classes are considered to be during prime time if it's start time falls within one of the following:Mon/Wed 9:30am to 3:35pmTue/Thur 9:30am to 3:20pm

Fri 9:30am to 12:35pm(2) Cells are highlighted in yellow if the % of classes in Prime Time > 55% (note that the 21 out of 38 of the standard class blocks are within prime time, ~55%)


Summary


Benefits of Improved Classroom Utilization A balance of optimizing the overall university classroom utilization while meeting

individual department’s needs

Avoid unnecessary costs to construct new buildings or outfit existing rooms to meet future class needs

An improved student and faculty experience

Increase in flexibility to accommodate future classes and/or repair needs


What We’ve Accomplished in this Study

Provided SU a complete and in-depth picture of its classroom inventory and how it is used today, for example,

– Obtained a detailed occupancy view of individual rooms – Mid-size classrooms and those equipped with technology are well utilized, but not as

fully utilized as prior expectations

The optimization model showed that, without adding new classrooms or making any changes in class times, this project provided an approach for SU to better assign course to classrooms to achieve:

– Improved faculty and student experience, e.g., 50% improvement in seat utilization and a 30% improvement in location mapping

– Increased the number of classroom availability, e.g., up to 18% increase of usable class times based on stress test results (which is intended to squeeze in as many classes as possible).

Identified the academic colleges/subjects that have a disproportion percentage of classes

that meet during prime time.


Recommendations


Classroom Utilization can be improved by

Taking a comprehensive approach, considering all courses and rooms at the same time

Establish rules and explicit requirements that provides clear guidance

Pooling of captive and registrar classrooms to enable a more effective use of existing resources

Consider both classroom assignment and class time scheduling

Initiated in This Study

Potential Next Steps


Recommended Extension Options – Associated with the Current Room Assignment Model

2) Reporting Tool to Automate the

Classroom Utilization Report

1) Business User Interface

to the Optimization Model

3) Captive Classroom Study

to include captive classrooms in the pool

Room Assignment Model to optimize room assignment

COMPLETED

The following describe slides describe various extension components related to the current room assignment model; and how they relate to each other.

The inclusion and timing of each component can be adjusted according to SU’s preference.


1) Business User Interface for the Current Room Assignment Model

Benefits: Facilitate repeatable usage of the room assignment model by designated SU business users to evaluate different policy decisions and access impact.

Summary: To simplify the usage of the optimization model by automating the data input, pre-processing and output process, such that users only need to interface with an MS Access front-end (or equivalent).

Approach: SQL will be used to conduct the data pre-processing in MS Access while VBA will be used to connect MS Access and ILOG for model input/output.

OUTPUTS INPUTS ROOM ASSIGNMENT MODEL

Optimal Room Assignment

Class Requirements


University Priorities Stress Test

Optimization Model in ILOG

RAW DATA REPORTS User Input/Output in MS Access

Pre-Processing Output Analyzer

Registrar Rooms


2) Reporting Tool Associated with the Current Room Assignment Model

Benefits: Provide an efficient, comprehensive and consistent view of SU’s classroom inventory and usage for both current and future scenarios

Summary: Formalize and automate the generation of the classroom utilization reports, which will follow the same design and format as the ones provided in the pre-modeling step (8 reports total).

Approach: Leverage the experience gained in the “pre-analysis” phase of the recent work, develop a reporting tool in the desktop environment that streamlines the creation of the classroom utilization reports.


3) Captive Classroom Study

Benefits: Evaluate and quantify the potential benefit of pooling the Registrar and captive classrooms in room assignment.

Summary: Extend the current classroom optimization model to include the captive rooms and the corresponding classes.

Approach: Gather the data on captive rooms and corresponding classes; incorporate it into the current classroom optimization model and determine the optimal assignment strategy. OUTPUTS INPUTS ROOM ASSIGNMENT

MODEL Optimal Room

Assignment

Class Requirements for all classes


University Priorities

Stress Test: Determined the Maximum Additional Course Capacity

Registrar Rooms

Captive Rooms

Revised New Footnote:


Recommended Extension Options – Extending to Class Time Scheduling

4b) Business User Interface to Optimization Model

(integrated with the user interface of the room assignment model)

4a) Class Time Scheduling Model

To extend current assignment model to optimize class time

The following describe recommended extension components associated with Class Time Scheduling.

The inclusion and timing of each component can be adjusted according to SU’s preference.


4a) Class-Time Scheduling Model

Benefits: Quantify the potential benefit of an optimal class schedule and provide a data-driven approach to revise class schedules that best meet SU’s academic mission.

Summary: Extend the current model to also optimize class time scheduling.

Approach: Construct a prototype in a similar manner to the recent classroom assignment study, during which we will focus on capturing the business rules, developing a working class-time optimization model and develop initial estimate on potential savings/benefits for various scenarios.

OUTPUTS INPUTS ROOM ASSIGNMENT & SCHEDULING

MODEL Optimal Room

Assignment

Class Requirements


University Priorities Stress Test:

Determined the Maximum Additional Course Capacity

Registrar Rooms

Optimal Class Times

Revised New Footnote:


4b) Business User Interface for the Class Time Scheduling Model

Benefits: Facilitate repeatable usage of the class time scheduling model by designated SU business users to evaluate different policy decisions and access impact.

Summary: Build upon the business user interface for the Room Assignment model, added in the components for the class time scheduling, such that users only need to interface with a simple front-end MS Access front-end (or equivalent)

Approach: SQL will be used to conduct the data pre-processing in MS Access while VBA will be used to connect MS Access and ILOG for model input/output.

OUTPUTS INPUTS ROOM ASSIGNMENT & SCHEDULING MODEL Optimal Room

Assignment Class Requirements


University Priorities Stress Test

Optimization Model in ILOG

RAW DATA REPORTS User Input/Output in MS Access

Pre-Processing Output Analyzer

Registrar Rooms Optimal Class

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