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Adventures in Sports Scheduling

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OR in Sports or Adventures in Sports Scheduling 2008 Region 1 IIE Student Regional Conference Michael Trick Tepper School,Carnegie Mellon A Story The secret weapon A bit more  [Company X] develops enterprise software solutions in the enterprise asset management, change management, and ITIL Service Management markets To be continued… Why Sports Scheduling???   Big Business!  US National TV pays $500 million / year for baseball  College basketball conferences get up to $30 million  Manchester United privatized for about £800 million and gets more than £15 million in sponsorship per year No rights holder wants to pay those sums and then get a “bad” schedule. Huge variety of problem types Small instances are difficult  Strong break between easy/hard (for all algorithms) Practical interest in instances at the easy/hard interface A lot more fun to talk about than machine shop scheduling     General Trends Plus    Faster computers Better algorithms (Better data) “We solve linear programs 5,000,000 times faster than we did in 1996” Bob Bixby, ILOG The Schedule      (now) 30 teams, 2 leagues Teams spread all over US and a bit of Canada Each team plays 162 games over 182 day season (packed!) Generally play 2 series of games per week (a series is 2-4 games against a single team) What makes a good schedule? 2004 Tigers schedule Keys to Schedule Quality Two primary drivers of schedule quality: DISTANCE FLOW Key aspects Distance not cost (primarily) wear and team: primarily cross time zone Flow ideal is 2 H, 2 A, 2 H, 2 A … three is OK, one is possible, 4 avoided Other Aspects Requirements half weekends home half summer weekends home Stadium unavailability Required open/finish No repeaters Requests/preferences Holiday requests Semi-repeaters Preferred summer matchups Preferred open/finish Conflicts in two-team cities The Process for 2005 Schedule      January 2004. Teams fill out questionaires (about 5 pages each) February-June 2004. Schedule creation, iteration with MLB office July 2004. Teams receive tentative schedule, provide feedback August 2004. Schedule given to players Association End of August 2004. Schedule fixed. Teams add start times. The competition   Multiple groups prepare schedules No formal decision process: the league chooses the one they like Hard?    Oh yes! There are more than 10225 schedules for a Double Round Robin schedule for 16 teams (alone) Ongoing research (lots of papers) shows difficult to solve some problems (to optimality) for even 8 team problems! – to be discussed Integer Programming and Constraint Programming    Direct approaches allow formulation of most or all aspects of MLB problem Good software available Break up into Round Robin Segments  Phases of schedule where subsets of teams play among themselves  Changes every year since 1996 1996 Schedule Double Round Robin among All teams Double Round Robin among All teams 2003 Hypothetical Schedule Double Round Robin among All Teams Single RR Among all teams DIV RR DIV RR DIV RR 2005-today Schedule  Primarily     Divisional Double Round Robin League Double Round Robin Divisional Double Round Robin Interleague Slots  Complicated by Division Size (4, 5 or 6) Why Break into Round Robins?    Good Things:  More knowledge of how to do Round Robin Scheduling  Resulting schedules are easy to explain  Naturally causes good separation between consecutive visits Bad Things:  Forces particular formats  Limits quality of schedule (except for separation)  First RRs solved enforce restrictions on following RRs. At the end, makes generating many formats much easier Main Ideas of Approach Decompose into smaller (almost solvable) pieces  Interleague first  Round robins  First series, then game assignment Main Ideas in Our Approach Variables that encapsulate higher-level structures.   Not x[i,j,t] = 1 if i at j in time t Better is 0-1 variable for “Pittsburgh plays at NY in time 2, then PHI in time 3, then ATL in time 4” (roadtrip)    Many more variables, but fewer constraints Much tighter relaxation Able to have many more idiosyncratic costs Main ideas in our method Large Scale Local Search  Don’t exchange pairs of games (impossible to get back to feasibility)  Take a schedule; relax ¼ of it; resolve; repeat for days on end Main ideas of our method New computational methods  Constraint programming  Integration between integer and constraint programming Result  Good schedules quick Further work on college basketball scheduling (ACC schedulers since 1998) Umpire scheduling   Try yourself?  Check out the Traveling Tournament Problem http://mat.tepper.cmu.edu/TOURN  Back to the story Things to remember     IE/OR skills important in service industries New methods/computer speed/data make our approach more important than ever Try it: http://mat.tepper.cmu.edu/TOURN Sports is a pretty good IE application

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