Operations research techniques for scheduling chile's second division soccer league

In this paper, we use operations research (OR) techniques to schedule the Second Division of the Chilean professional soccer league. The solution must satisfy a series of conditions requested by league officials. Because the teams generally travel long distances by bus, geographical restrictions are particularly important. We specify the scheduling problem and solve it using an integer linear programming (ILP) model that defines when and where each match is played, subject to constraints. For the most difficult instances, we formulate a second ILP model that generates home-away patterns and assigns them to the teams; we then run the model, which determines the match schedule. Chilean league officials have successfully used the models to schedule all five Second Division tournaments between 2007 and 2010, replacing the random scheduling methodology that they used previously. Since 2007, the two formulations have been adapted to various formats with which the Second Division has experimented; these include a quadruple round robin and a two-phase tournament with zonal and national phases. The application we present is one of a number of such projects that the authors and their colleagues developed over the past few years, and it represents an expansion of the use of OR techniques for managing tasks in Chilean soccer.Fil: Duran, Guillermo Alfredo. Universidad de Chile; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Guajardo, Mario. NHH Norwegian School of Economics; NoruegaFil: Wolf Yadlin, Rodrigo. Universidad de Chile; Chil

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

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

English Premier League scheduling using simulated annealing

This is the first known attempt at scheduling the English Premier League (EPL), which is a NP-hard problem, in the literature. In this research an initial schedule is created using a ‘polygon’ construction method, a method which originates in graph theory. Two distinct simulated annealing metaheuristic solving methodologies are then created to improve this initial schedule. One method is based on a temperature schedule, finite epoch length and reheats while the other is based on a gradually reducing temperature schedule and non-finite epoch length. These two methods were evaluated with respect to solution quality (total penalty), reliability (variation of solution quality over numerous trials) and speed. The official schedule used by the EPL organisers was used for comparison. It was found that the first method produced comparable results, while the second produced improved results. The second method was validated over three seasons and consistently performed well. The findings in this research can be used as the maiden real-world framework and benchmark for the unsolved EPL scheduling problem in the sports scheduling literature

A Statistical Investigation into Factors Affecting Results of One Day International Cricket Matches

The effect of playing “home” or “away” and many other factors, such as batting first or second, winning or losing the toss, have been hypothesised as influencing the outcome of major cricket matches. Anecdotally, it has often been noted that Subcontinental sides (India, Pakistan, Sri Lanka and Bangladesh) tend to perform much better on the Subcontinent than away from it, whilst England do better in Australia during cooler, damper Australian Summers than during hotter, drier ones. In this paper, focusing on results of men’s One Day International (ODI) matches involving England, we investigate the extent to which a number of factors – including playing home or away (or the continent of the venue), batting or fielding first, winning or losing the toss, the weather conditions during the game, the condition of the pitch, and the strength of each team’s top batting and bowling resources – influence the outcome of matches. By employing a variety of Statistical techniques, we find that the continent of the venue does appear to be a major factor affecting the result, but winning the toss does not. We then use the factors identified as significant in an attempt to build a Binary Logistic Regression Model that will estimate the probability of England winning at various stages of a game. Finally, we use this model to predict the results of some England ODI games not used in training the model

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Design and optimization of hybrid renewable energy systems for off-grid continuous operations

The mining industry accounts for a significant portion of the energy demand by the industrial sector. The rising demand for metals around the world, coupled with the depletion of readily accessible ore deposits, has led to mining operations moving to more remote locations with no grid supply of energy. As a result, the operations require transport of fuel over large distances, leading to a significant increase in the overall mining cost. Renewable energy is considered to be the most promising solution to the mining industry energy problem. This work investigates the possibility of operating remote mines on local generation from renewables. A survey of recent literature revealed that while a lot of research had been done on hybrid renewable energy systems design and sizing, little thought had been given to accounting for the stochastic nature of renewable resources in the sizing process. Previous works focused on the sizing of PV-wind-battery systems; other potential generation and storage technologies were largely ignored. The challenge of intermittency in the power output of renewable generation systems had also largely been ignored. This thesis extends the state of the art on hybrid systems sizing by developing models and methodologies to address these challenges. A novel hybrid energy system integrating thermal and electrical renewable generation options with multiple large scale energy storage options is considered in this thesis. Models are developed for the different components of the energy system, with dynamic models incorporated for the material and energy balances of the storage alternatives, leading to a system of nonlinear differential algebraic equations (DAEs). The temporal nature of the renewable resources is accounted for by considering multiple stochastic renewable input scenarios generated from probability distribution functions (PDFs) as inputs into the system model. A reliability measure to quantify the impact of weather-based variability, called the modified loss of power supply probability, is developed. A bi-criteria sizing methodology which allows for the stochastic nature of renewable resources to be accounted for is presented. The approach combines the time series approach to reliability evaluation with a stochastic simulation model. Two approaches for mitigating the impact of intermittency in power outputs of renewable generation technologies are also developed. The first approach is based on system redesign, while the second approach is based on the introduction of an instantaneous response storage option. Case studies were presented to demonstrate the various methodologies. The results show that climate-based variability can have a significant impact on the cost and performance of hybrid energy systems and should always be accounted for in the sizing process. Intermittency needs to be accounted for in some form at the design stage as it can have an impact on the choice of technologies. The integration of thermal and electrical power generation and storage options provide a way to reduce hybrid system costs. The methodologies developed in this thesis are applicable to any location and can easily be extended to incorporate other generation and storage alternatives. They provide the decision maker with necessary information for making preliminary sizing decisions

Bowdoin Orient v.138, no.1-25 (2008-2009)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1009/thumbnail.jp