Project Title: Monte Carlo Method to Schedule Sport Competitions Student: Manpreet Ruprai Course: BSc Hons Computer Science Abstract: For NP (non-deterministic polynomial problems) no simple algorithm yields optimal schedules in a limited amount of computer time, and Monte Carlo simulation is analysed as an alternative to create schedules. It is not always computationally feasible to analyse every possible move in the ancient game of 'Go', hence Monte Carlo Simulation is used. This fact also is true of many scheduling environments; it is not possible to valuate every possible schedule. Monte Carlo Simulation is a method which has been used for many applications, such as risk analysis, and is especially useful in examining systems with a great number of coupled degrees of freedom, such as liquids, disordered materials, strongly coupled solids, and cellular structures. Multiple groups consisting of a Round-Robin format constitute many major sporting events such as the cricket and football world cups. Lots of high profile sports such as football involve many issues regarding the subject of scheduling and there have been many different algorithms attempting to resolve scheduling problems, one such example is constraint programming. Rarely has a soft constraint provided the weight of the constraint as a distribution. Within this study the opportunity to devise how the schedule can be created using Monte Carlo Simulation and how by varying the number of iterations, it is possible produce different pseudorandom schedules. Another major fact observed is how a Monte Carlo risk analysis can be applied using ranges of a stadium capacity combined with the match rankings to assure the superior and most entertaining games are performed in the most suitable stadiums. By using the simulation, the distribution of stadium attendances can lead to fair, yet risk free stadium assignment. The Design of the 'Monte Carlo Tournament Scheduler' (MCTS) facilitates the introduction of Monte Carlo into the field of Automated Scheduling.