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Team EMIS

Team EMIS. Ashton Nixon Chris Noon Ross Sewell Trevor Weichmann. Failed Project Attempt . American Airlines Project Wanted our help to reduce the time in some of the behind the scenes changeovers

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Team EMIS

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  1. Team EMIS Ashton Nixon Chris Noon Ross Sewell Trevor Weichmann

  2. Failed Project Attempt  • American Airlines Project • Wanted our help to reduce the time in some of the behind the scenes changeovers • In the end, they did not see the value in our services, so we had to look elsewhere for our project… • This debacle left our group in a tight spot with less than a month to learn a language and implement it.

  3. Presentation Overview • Background • The Problem • Current Situation • Analysis and Goals • Technical Description of Model • Analysis and Managerial Interpretation • Conclusion

  4. Background on EMIS Department • Exciting year • Out of 12 inductees of the Robert Stewart Hyer Society, 3 of them were EMIS majors • Professor Asmus named the SEJC Outstanding EMIS Undergraduate Faculty • Professor Barr received the Graduate Student Council Outstanding EMIS Graduate Faculty Award • Professor Siems was awarded the Outstanding Executive Faculty Award

  5. Background on EMIS Department • Academic responsibilities • Undergraduate and graduate studies • Several overlap classes • EMIS 5300’s and EMIS 7300’s • 2 entry-level classes, EMIS 1305 and 1307 • GEC requirement • Certain Professors teach classes in different levels • Some Professors hold other jobs

  6. Current Situation • Current Schedule is done by hand • Mary Alys Lillard • Professor • Adviser • Tedious • Time-Consuming • Currently only 9.5 full-time professors

  7. Management Summary • To make a model to more efficiently schedule the classes for the undergraduate and graduate level Engineering Management degrees • Use OPL software to program constraints and preferences in order to output several potential schedules

  8. Analysis and Goals • Create a Model of the scheduling process in OPL • Use our hard-coded model to get several output schedules • Use our program as a basis and shell for the future of scheduling classes

  9. Data Gathering • Information received from clients • Time preferences for professors • Historical data • Lists of EMIS courses • Current number of course and lab sections • Time constraints

  10. Our Model • Constraint Programming • OPL • Course Constraints • Professors teaching multiple courses • Time preferences of professors • No sections of same course scheduled at same time • Lab Constraints • Lab sections offered at different times • Labs only scheduled based on two available lab rooms

  11. Technical Description of the Model • Constraint programming • Using a matrix which is sized by the courses and the times • Boolean variables • 1 means Assigned class and 0 means not assigned class • Kristina Cannon • Helped with her previous knowledge of OPL

  12. Technical Description of the Model • Used teacher prefs to make constraints • // Constraints so that same class sections are not scheduled at the same time • forall(c in Courses) sum(t in CourseTimes)(AC[c,t]) <> 0; • forall(t in CourseTimes) {AC[EMIS1305a,t] + AC[EMIS1305b,t] + AC[EMIS1305c,t] <= 1}; • forall(t in CourseTimes) {AC[EMIS1307a,t] + AC[EMIS1307b,t] + AC[EMIS1307c,t] + AC[EMIS1307d,t] <= 1}; • //Kennington Constraint • forall(t in CourseTimes) {AC[EMIS1360a,t] + AC[EMIS1360b,t] <= 1}; • forall(t in CourseTimes) {AC[EMIS3308a,t] + AC[EMIS3308b,t] <= 1}; • forall(t in CourseTimes) {AC[EMIS3309a,t] + AC[EMIS3309b,t] <= 1}; • //Lillard constraint • forall(n in Lillard) {AC[EMIS3308a,n] + AC[EMIS3308b,n] = 0};

  13. Analysis and Managerial Interpretation • Prints results as output • There are many results as of now • With time more constraints will be added • Also the programmed can be modified to take in an Excel file with the teachers’ preferences and output to a separate Excel file for long term storage.

  14. Output of Program

  15. Conclusion • More work needs to be done • To date, our program outputs thousands of different possible solutions • The program is hard-coded with the information about course times, teacher availabilities, and teacher preferences • Future code should be able to read from and write to an excel file, making it easier to input and view the information

  16. Conclusion (continued) • More work needs to be done • We were not able to incorporate classroom space into the model • The same type of style that we used to schedule lab rooms should be done with classrooms • Code should be modified to have a while loop to output the top fifty schedules

  17. Conclusion (continued) • We have broken the initial thought barrier with the OPL software • Code will serve as the springboard to the final solution • Proud to present our model to our clients with the hope that they will be able to see the future potential this program has in the decrease of required hours of manual labor in order to produce a final class schedule

  18. Questions Anyone? • Thanks for listening!!! -Ashton Nixon -Chris Noon -Ross Sewell -Trevor Weichmann

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