1 course scheduling software progress presentation december 22, 2004

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Course Scheduling SoftwareProgress Presentation

December 22, 2004

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Customer:

Jed Lippard, Upper School Director,Prospect Hill Academy Charter School

Team Membersdev@scheduler.tigris.org

Glen Winston Robert McKeever

Steve MoranValdeva Ives

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Agenda• Project Status• Risk Update• Architectural Overview• GUI Walkthrough• Model Overview• Scheduling

– Proof of Concept– Constraint Programming– Technology Alternatives– Drools pros & cons– Drools Example– Scheduling API

• Deployment Plan

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Project Status

Presented by

Glen Winston

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Project Status

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Process Overview• Project Phases: Fall

– Initiation (100% complete)– Analysis (100% complete)– Functional Design (100% complete)– Technical Design (70% complete)

• Project Phases: Spring– Completion of Technical Design– Development– Testing– Deployment

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Process Overview: Technical Design• Key Deliverable of this phase

– Technical Specifications Document• Document all non-trivial classes in the system.• Informative sequence diagrams of key controllers

in the system.• Document and prove algorithm for scheduling

classes.

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Risk Update

Presented by

Steve Moran

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Risks• Scheduling technology implementation – higher

– Advancing Rule Base during semester break– Continuing Research into Production Systems & Constraints– Assigning 50% of team to task

• Scheduling technology choice – moderate– Several well understood alternatives available

• Post-Deployment Maintenance & Support – low to moderate– Specification biased toward currently stated requirements– Will rely on actual implementation

• Added Requirement to schedule individual students – lower– Students primarily move in groups – Believe less involved than class scheduling

• Feature creep – lower– Detailed documentation & customer contact

• Insufficient time - lower– Following regimented process

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Component Overview

Presented by

Glen Winston

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Architectural Overview

View Controller

Model

Scheduler

javax.swingjavax.swing

Scheduler API

State ChangeState Query

Change Notification

View Selection

User Gestures

Update State

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GUI Walkthrough

Presented by

Dee Dee Ives

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Model Overview

Presented by

Glen Winston

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Model Overview

View Controller

Model

Scheduler

javax.swingjavax.swing

Scheduler API

State ChangeState Query

Change Notification

View Selection

User Gestures

Update State

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Subject

name : String

Course

name : Stringsubject : SubjectblocksPerWeekdoubleBlocksPerWeekdepartments : List

Instructor

departments : ListmandatoryAvailability : WeeklyScheduledesiredAvailability : WeeklySchedulemaximumDailyClasses : int

Block DoubleBlock

Department

name : String

WeeklySchedule

days : ListperiodsPerDay

Day

name : Stringperiods : List

Period

day : DaytimePeriod

name : String

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Cohort

students : Listgrade : Grade

Student

firstName : StringlastName : Stringgrade : Grade

SchoolClass

course : Courseinstructors : Listcohorts : Liststudents : List

Grade

name : String

Course

name : Stringsubject : SubjectblocksPerWeekdoubleBlocksPerWeekdepartments : List

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Room

building : Stringfloor : Stringnumber : Stringcapacity : intavailability : WeeklySchedule

Block DoubleBlock

Period

day : DaytimePeriod

name : String

SchoolSchedule

name : StringclassSchedule : ClassSchedule

saveToFile()exportTeacherView()exportStudentView()importStudents()

ClassPeriod

schoolClasses : Listroom : Roomperiod : Periodfixed : booleanscheduled : boolean

ClassSchedule

classPeriods : List

findAvailableRooms(filter)findPeriods(filter)findClassPeriods(filter)

Created by Scheduling Algorithm

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Scheduling Proof of Concept: Project Goal

Presented by

Glen Winston

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Scheduling Proof of Concept: Project Goal

Scheduler Component is highest area of risk in technical design.

Risk Mitigation Plan

• Proof of concept• Two exclusive resources• Hand written algorithm fallback plan

Proof of Concept is complete, we learned

• We were able to create periods in a rules engine.

• We were able to fill periods in a rules engine.

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Scheduling Proof of Concept: Approach

Presented by

Steve Moran

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Scheduling Problem To Solve• Students, Teachers, & Subjects,• in 7 grades, subdivided into cohorts (groups),• into classrooms of various sizes & locations,• with 7 daily schedule blocks,• with a rotating class schedule,• with 81 initial constraints.

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Technology Alternatives• DROOLS – actively being prototyped

– “understandable” XML style rules in java

• JESS – capable, but licensing issues • CLIPS – “lisp style” rules implemented in C• JClips – directly runs CLIPS files in java

– testbed for existing CLIPS.clp example files

• Prolog – inefficient backward chaining• Brute force - inefficient backward chaining

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Rete-based Inference Engine• Declarative programming – “what is”• Forward chaining rules – “data driven”

• Fast in-memory network – “The only algorithm for implementing production rules

whose performance is demonstrably independent of the number of rules.”

• Rules can change without recompiling

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<parameter>

Domain

<condition>

Boolean

<consequence>

java

Rules Working MemoryFacts

Assert

Retract

Modify

Facts Rules

Ordered by Salience

Rete

AlgorithmCollections of Objects

Classes

Rooms

Blocks

In a nutshell, we want to schedule Classes into Rooms with Blocks

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RoomsRooms

Blocks

222

101 - 8AM

101

222

101

101 - 9AM101 - 10AM

222 - 8AM222 - 9AM

222 - 10AM

Before Rule Fires After Rule Fires

<parameter> Room </parameter>

<condition> none </condition>

<consequence>

assert(new Block(room.num,8))

assert(new Block(room.num, 9))

assert(new Block(room.num, 10))

</consequence>

Working

Memory

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room 101 has block at 8

room 101 has block at 9

room 101 has block at 10

room 222 has block at 8

room 222 has block at 9

room 222 has block at 10

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Rooms ClassBlocks

222101 - 8AM

101

Math

English101 - 9AM

101 - 10AM

222 - 8AM222 - 9AM

222 - 10AM

Working Memory

<parameter>

Room

Block

Class

</parameter>

<condition>

block.class = null

class.numStudents < room.capacity

class.isScheduled == false

</condition>

<consequence>

class.isScheduled = true

modify(class)

block.class = class.id

modify(block)

</consequence>

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Scheduling class: 5 in room: 101 at: 8

Scheduling class: 15 in room: 101 at: 9

Scheduling class: 6 in room: 101 at: 10

Scheduling class: 8 in room: 222 at: 8

Scheduling class: 16 in room: 222 at: 9

Scheduling class: 20 in room: 222 at: 10

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Scheduling Proof of Concept: Challenges

Presented by

Bob McKeever

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Constraint Programming Problem• Scheduling is an NP Complete Problem.• Requires polynomial time to solve. • Could be solved trivially by using a systemic

search. • Generate and test until a solution is found.

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Constraint Programming Solutions • Backtracking.• Backtracking with Forward Checking. • Backtracking with Forward Checking and

Heuristics. • Tic, Tac, Toe as an example

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Drools Negatives • Very little documentation.• Does not have all the same features as

CLIPS. (At present “not” is not supported.)• Can not directly convert from CLIPS code to

Drools code. • Small user community.• Just out of Beta.

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Drools Negatives (Con’t)• Team members have no experience with

Drools programming.• 3 Team members have no experience with

programming expert systems.

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Drools Positives • Handles the constraints well. Much better

than nested if statements. • Open source.• Seems to have a lot of “buzz”.• Did I mention it was free?

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Drools Positives (Con’t) • We are starting to get up to speed with it.

Now have some working examples.• May be able to post our work as an example

on their web site to have others carry on. May help on maintenance issues.

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Scheduling Proof of Concept: Drools Sample

Presented by

Bob McKeever

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<?xml version="1.0" encoding="UTF-8"?>

<!--

The definition of a RuleExecutionSet is not within the scope of the JSR 94.

The implementation given in this file is written for the reference

implementation. A rule engine vendor verifying their rule engine should

modify this file to their specific needs.

-->

<rule-set name="Scheduler"

xmlns="http://drools.org/rules"

xmlns:java="http://drools.org/semantics/java"

xmlns:xs="http://www.w3.org/2001/XMLSchema-instance"

xs:schemaLocation="http://drools.org/rules rules.xsd

http://drools.org/semantics/java java.xsd">

<java:import>java.util.*</java:import>

<java:import>org.drools.examples.schedule.model.Block</java:import>

<java:import>org.drools.examples.schedule.model.ClassInfo</java:import>

<java:import>org.drools.examples.schedule.model.ClassToSchedule</java:import>

<java:import>org.drools.examples.schedule.model.Room</java:import>

<java:import>org.drools.examples.schedule.model.RoomCourseRelation</java:import>

<java:import>org.drools.examples.schedule.model.RoomInfo</java:import>

<java:import>org.drools.examples.schedule.model.SchoolClass</java:import>

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<!--

Create the blocks

-->

<rule name="generate blocks" salience="40">

<parameter identifier="roomInfo">

<class>RoomInfo</class>

</parameter>

<java:consequence>

System.out.println("Making block " + roomInfo.number);

drools.assertObject(new Block(roomInfo.number, 8));

drools.assertObject(new Block(roomInfo.number, 9));

drools.assertObject(new Block(roomInfo.number, 10));

</java:consequence>

</rule>

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<!--

Schedule.

-->

<rule name="schedule" >

<parameter identifier="roomInfo"> <class>RoomInfo</class> </parameter>

<parameter identifier="block"> <class>Block</class> </parameter>

<parameter identifier="classInfo"> <class>ClassInfo</class> </parameter>

<java:condition> block.schoolClass == 0 </java:condition>

<java:condition> classInfo.numStudents &lt;= roomInfo.capacity </java:condition>

<java:condition> classInfo.isScheduled == false </java:condition>

<java:consequence>

classInfo.isScheduled = true;

drools.modifyObject(classInfo);

block.schoolClass = classInfo.id;

drools.modifyObject(block);

System.out.println("Scheduling class: " + block.schoolClass +

" in room: " + block.room + " at: " + block.time);

</java:consequence>

</rule>

</rule-set>

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Scheduling API

Presented by

Bob McKeever

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Scheduling API

Scheduler

View Controller

Model

javax.swingjavax.swing

Scheduler APIScheduler API

State ChangeState Query

Change Notification

View Selection

User Gestures

Update State

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Deployment Plan

Presented by

Bob McKeever

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Deployment Plan Goals

• Keep customer informed. • Get buy in from customer’s IT administrator. • Create Windows executable.• Provide physical program to the customer.• Provide documentation to the customer. • Give the program the Windows look and feel.

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Deployment Plan Actions• Use launch4j to create a Windows executable

with Splash screens and icons.• Meet with customer’s IT administrator.

Request computer that has been backed up. • Create set up program using Wise for install

and uninstall. Burn onto a CD.• Test!, Test!, Test!

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Deployment Plan Actions (Con’t)• Develop documentation and detailed

installation instructions.• Provide professional documentation and CD.• Meet at Customers Site for installation. • Provide a Jar file version on the Web site of a

generic scheduler. (One that does not have the customer’s logos on it and is not dependent on Windows to run.)

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Q & A

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Backward & Forward Chaining

Presented by

Steve Moran

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Two Approaches

• Backward Chaining– Imperative based systems – how to– Queries fact space for goal ‘truth’– Mechanism used in most most logic programming, i.e. Prolog

• Forward Chaining– Declarative based systems – what is– Triggered on fact space information– A data driven technique to reach inferences from a set of facts

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Backward vs. Forward Chaining• Backward-chaining means that no rules are fired upon assertion of new

knowledge. When an unknown piece of knowledge is detected all rules relevant to the knowledge in question are fired until the question is answered, if possible. Thus, backward chaining systems normally work from a goal state back to the original state.

• Forward-chaining implies that upon assertion of new knowledge, all relevant rules are fired exhaustively, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s).

• The branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus determine which method is most efficient.

Source: http://ai.eecs.umich.edu/cogarch0/index.html

51Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf

PatientDiagnosis

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Backward vs. Forward Chaining• Backward-chaining means that no rules are fired upon assertion of new

knowledge. When an unknown piece of knowledge is detected all rules relevant to the knowledge in question are fired until the question is answered, if possible. Thus, backward chaining systems normally work from a goal state back to the original state.

• Forward-chaining implies that upon assertion of new knowledge, all relevant rules are fired exhaustively, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s).

• The branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus determine which method is most efficient.

Source: http://ai.eecs.umich.edu/cogarch0/index.html

53Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf

54Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf

55Source: http://www.cise.ufl.edu/class/cap6685sp03/Lectures/ES-CH4b.pdf

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Backward vs. Forward Chaining• Backward-chaining means that no rules are fired upon assertion of new

knowledge. When an unknown piece of knowledge is detected all rules relevant to the knowledge in question are fired until the question is answered, if possible. Thus, backward chaining systems normally work from a goal state back to the original state.

• Forward-chaining implies that upon assertion of new knowledge, all relevant rules are fired exhaustively, effectively making all knowledge about the current state explicit within the state. Forward chaining may be regarded as progress from a known state (the original knowledge) towards a goal state(s).

• The branching factor (the number of considerations at each state) may vary between forward and backward chaining and thus determine which method is most efficient.

Source: http://ai.eecs.umich.edu/cogarch0/index.html