Scheduling the Australian football league

Generating a schedule for a professional sports league is an extremely demanding task. Good schedules have many benefits for the league, such as higher attendance and TV viewership, lower costs, and increased fairness. The Australian Football League is particularly interesting because of an unusual competition format integrating a single round robin tournament with additional games. Furthermore, several teams have multiple home venues and some venues are shared by multiple teams. This paper presents a 3-phase process to schedule the Australian Football League. The resulting solution outperforms the official schedule with respect to minimizing and balancing travel distance and breaks, while satisfying more requirements

Scheduling sport tournaments using constraint logic programming

We tackle the problem of scheduling the matches of a round robin tournament for a sport league. We formally define the problem, state its computational complexity, and present a solution algorithm using a two-step approach. The first step is the creation of a tournament pattern and is based on known graph-theoretic results. The second one is a constraint-based depth-first branch and bound procedure that assigns actual teams to numbers in the pattern. The procedure is implemented using the finite domain library of the constraint logic programming language eclipse. Experimental results show that, in practical cases, the optimal solution can be found in reasonable time, despite the fact that the problem is NP-complete

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast

Handling fairness issues in time-relaxed tournaments with availability constraints

Sports timetables determine who will play against whom, where, and on which time slot. In contrast to time-constrained sports timetables, time-relaxed timetables utilize (many) more time slots than there are games per team. This offers time-relaxed timetables additional flexibility to take into account venue availability constraints, stating that a team can only play at home when its venue is available, and player availability constraints stating that a team can only play when its players are available. Despite their flexibility, time-relaxed timetables have the drawback that the rest period between teams’ consecutive games can vary considerably, and the difference in the number of games played at any point in the season can become large. Besides, it can be important to timetable home and away games alternately. In this paper, we first establish the computational complexity of time-relaxed timetabling with availability constraints. Naturally, when one also incorporates fairness objectives on top of availability, the problem becomes even more challenging. We present two heuristics that can handle these fairness objectives. First, we propose an adaptive large neighborhood method that repeatedly destroys and repairs a timetable. Second, we propose a memetic algorithm that makes use of local search to schedule or reschedule all home games of a team. For numerous artificial and real-life instances, these heuristics generate high-quality timetables using considerably less computational resources compared to integer programming models solved using a state-of-the-art solver

Particle Swarm Algorithm for Improved Handling of the Mirrored Traveling Tournament Problem

In this study, we used a particle swarm optimization (PSO) algorithm to address a variation of the non-deterministic polynomial-time NP-hard traveling tournament problem, which determines the optimal schedule for a double round-robin tournament, for an even number of teams, to minimize the number of trips taken. Our proposed algorithm iteratively explored the search space with a swarm of particles to find near-optimal solutions. We also developed three techniques for updating the particle velocity to move towards optimal points, which randomly select and replace row and column parameters to find candidate positions close to an optimal solution. To further optimize the solution, we calculated the particle cost function, an important consideration within the problem conditions, for team revenues, fans, and media. We compared our computation results with two well-known meta-Heuristics: a genetics algorithm utilizing a swapping method and a Greedy Randomized Adaptive Search Procedure Iterated Local Search algorithm heuristic on a set of 20 teams. Ultimately, the PSO algorithm generated solutions that were comparable, and often superior, to the existing well-known solutions. Our results indicate that our proposed algorithm could aid in reducing the overall budget expenditures of international sports league organizations, which could enable significant monetary savings and increase profit margins

Towards prevention of sportsmen burnout : Formal analysis of sub-optimal tournament scheduling

Funding Statement: The authors are grateful to the Deanship of Scientific Research at King Saud University, Saudi Arabia for funding this work through the Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing.Peer reviewedPublisher PD

An instance data repository for the round-robin sports timetabling problem

The sports timetabling problem is a combinatorial optimization problem that consists of creating a timetable that defines against whom, when and where teams play games. This is a complex matter, since real-life sports timetabling applications are typically highly constrained. The vast amount and variety of constraints and the lack of generally accepted benchmark problem instances make that timetable algorithms proposed in the literature are often tested on just one or two specific seasons of the competition under consideration. This is problematic since only a few algorithmic insights are gained. To mitigate this issue, this article provides a problem instance repository containing over 40 different types of instances covering artificial and real-life problem instances. The construction of such a repository is not trivial, since there are dozens of constraints that need to be expressed in a standardized format. For this, our repository relies on RobinX, an XML-supported classification framework. The resulting repository provides a (non-exhaustive) overview of most real-life sports timetabling applications published over the last five decades. For every problem, a short description highlights the most distinguishing characteristics of the problem. The repository is publicly available and will be continuously updated as new instances or better solutions become available

Balancing the Game: Comparative Analysis of Single Heuristics and Adaptive Heuristic Approaches for Sports Scheduling Problem

Sport timetabling problems are Combinatorial Optimization problems which involve the creation of schedules that determine when and where teams compete against each other. One specific type of sports scheduling, the double round-robin (2RR) tournament, mandates that each team faces every other team twice, once at their home venue and once at the opponent’s. Despite the relatively small number of teams involved, the sheer volume of potential scheduling combinations has spurred researchers to employ various techniques to find efficient solutions for sports scheduling problems. In this thesis, we present a comparative analysis of single and adaptive heuristics designed to efficiently solve sports scheduling problems. Specifically, our focus is on constructing time-constrained double round-robin tournaments involving 16 to 20 teams, while adhering to hard constraints and minimizing penalties for soft constraints violations. The computational results demonstrate that our adaptive heuristic approach not only successfully finds feasible solutions for the majority of instances but also outperforms the single heuristics examined in this study.Master's Thesis in InformaticsINF399MAMN-INFMAMN-PRO