April 22, 2008 - Fair Isaac Corp announced that for the third consecutive season, the NFL used Optimal Planning Solutions's sports-scheduling solution and Fair Isaac's Xpress-MP optimization tool to deliver the NFL's playing schedule.


Optimal Planning Solutions customized its advanced sports-scheduling tools, which use Fair Isaac's Xpress-MP solver, to mathematically model and solve the NFL's extremely complex scheduling problems.


"There are a myriad of complex scheduling requirements and constraints that must be taken into account each season," said Michael North, NFL director of broadcast planning and scheduling. "We chose Optimal Planning Solutions and Fair Isaac because they not only understood our unique requirements but also created a tool that greatly improves the timeliness of our scheduling process and the overall quality of the schedules. The result is an outstanding playing schedule for our fans, teams, and broadcast partners."


Based on the NFL's objectives, Optimal Planning Solutions is using Xpress-MP to create a schedule that:

  • Accommodates stadium availability issues,
  • Minimizes the competitive impact of travel and/or days between games,
  • Delivers television schedules that allow the most fans to watch the biggest games, and
  • Enables the NFL to search and analyze countless potential schedules for those that balance the needs of the team and network partners, taking into account the many constraints that must be considered.

"Countless factors must be taken into account when scheduling the NFL season," said North. "This process was, until recently, mostly a manual and intensely complex process. With the implementation of the Optimal Planning Solutions software using Fair Isaac's Xpress-MP tool, we can evaluate infinitely more scheduling options than ever before."


Xpress-MP is a suite of mathematical modeling and optimization tools from Fair Isaac used to solve linear, integer, quadratic, non-linear and stochastic programming problems. The suite features algorithms to accurately solve the demanding problems.

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