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

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

A New Method for Defining Parameters to SETAR(2,k_1,k_2)-models

This paper describes a new numerical method for defining the threshold and delay parameters of k-order self-exciting threshold autoregressive (SETAR) models. The idea of the method is to divide a time series into ascending and descending parts. This division can especially be exploited in building prediction models: it is a considerably easier task to predict the ascending and descending parts separately than to try to predict both of them at the same time. Another issue of this paper is to build the prediction model with only the most significant predictor variables. This is achieved with the help of evolutionary optimizing. In the first step we generate a number of models (networks) with different predictor variables, then we train each of the networks and the fittest ones are used for reproduction. In the beginning we also set the possible number of predictor variables to some constant. In the next step we reduce the number of predictor variables by leaving out the ones not chosen ..

Forecasting Sunspot Numbers with Neural Networks

¾This paper presents a feedforward neural network approach to sunspot forecasting. The sunspot series were analyzed with feedforward neural networks, formalized based on statistical models. The statistical models were used as comparison models along with recurrent neural networks. The feedforward networks had 2¾4 inputs (depending on the number of predictor variables), one hidden layer with 20 or 30 neurons and one neuron on the output layer. The networks were trained using the backpropagation algorithm. As a result, I found that feedforward neural networks are much better forecasters than recurrent neural networks and statistical models. Keywords¾Neural networks, Time series analysis, Forecasting, Prediction, Statistical models, Sunspots, Autoregressive models. 1. INTRODUCTION Sunspots are blotches on the sun, about 1000K colder than the surroundings. Many phenomena, such as the northern lights, are evidently related to them. Sunspots were observed with the naked eye until Galileo in ..

Optimizing the unlimited shift generation problem

Good rosters have many benefits for an organization, such as lower costs, more effective utilization of resources and fairer workloads. This paper introduces the unlimited shift generation problem. The problem is to construct a set of shifts such that the staff demand at each timeslot is covered by a suitable number of employees. A set of real-world instances derived from the actual problems solved for various companies is presented, along with our results. This research has contributed to better systems for our industry partners

Optimizing the unlimited shift generation problem

Good rosters have many benefits for an organization, such as lower costs, more effective utilization of resources and fairer workloads. This paper introduces the unlimited shift generation problem. The problem is to construct a set of shifts such that the staff demand at each timeslot is covered by a suitable number of employees. A set of real-world instances derived from the actual problems solved for various companies is presented, along with our results. This research has contributed to better systems for our industry partners