Throughout my education and professional career I have gained experience and interest in various areas of Operational Research.

For the last three and a half years, I have been working as an operational researcher developing probability models of slot machine games for the gambling industry. These models are necessary to calculate the probability distribution of the prizes and adjust the long-term percentage return and also, albeit less importantly, to control the hit rate and the volatility of the game. The "percentage return" is the percentage of the collected money the game will return to the players in the long term.

The models of the games are also necessary to perform risk analysis. Although the law of large numbers assures that the percentage return will approach the theoretical percentage return in the long term, the casino operators must know and understand that in the short term there is a risk that the amount of money awarded in prizes by the games exceeds the amount of money paid by the players. Understanding and managing the risk is especially important when large jackpots are involved.

Modelling these games requires the use of Operational Research techniques, Probability and Stochastic Processes. For those games in which the player must make choices, the win distribution must be calculated according to the optimal strategy. These models are developed with a spreadsheet, when possible, but more often programming languages such as C++, Java or Visual Basic must be used. In every case, it is convenient to develop a Monte Carlo simulation model along the mathematical model to double-check the results.

The work that I have carried out during this time has provided material for several papers. The first of these, ‘Hitting the jackpot with OR’, has been published in The Operational Research Society’s OR Insight magazine (vol. 19 issue 3). This paper is concerned with analysing the role of mathematics, and Operational Research in particular, in the slot machine industry. It provides an overview of some of the more popular game elements with details of corresponding modelling solutions.

‘In search of new slot game features: A stochastic dynamic programming analysis of Battleships’ is the second paper that has resulted from my work. This paper has been submitted to the Journal of the Operational Research Society and is currently being reviewed by referees. It discusses how the new slot technologies can realise more player interaction and more sophisticated games and, therefore, it argues that the role of the mathematician or operational researcher will become more important. The paper illustrates this possibility by suggesting a version of ‘Battleships’ could be a feature of a slot game and provides a modelling solution based on Stochastic Dynamic Programming.

The third paper, ‘Markov chain applications in the Slot Machine industry’, discusses applications of Markov chains in different situations within the slot machine industry. It analyses different games, illustrates the type of problems that one might encounter in practice and provides suggestions for developing implementations of the models exploiting the particular characteristics of the Markov chain applications in this industry. The examples provided are from real-life games modelled by me while working at Barcrest Games that have already been released and are commercially available. This paper has also been submitted to the JORS and is currently being reviewed by referees.

‘Bitz & Pizzas: optimal stopping of a slot machine bonus game’ discusses slot games which, like the previous, can be modelled as Markov chains but because the player can stop the game at any time, the percentage return must be calculated according to the expected value of the optimal stopping strategy. The paper is the case study of the Bitz & Pizzas american game. This paper has also been submitted to the JORS.

Finally, ‘To gamble or not to gamble: determining optimal playing strategies for Hi-Lo’ discusses those games in which the player must make choices. Under these circumstances, the percentage return must be calculated according to the optimal strategy. This is calculated using techniques such as stochastic dynamic programming in Markov processes with finite time horizons, where the sequential decisions that make up the optimal strategy must be determined. The paper presents a case-study to illustrate this point. This paper has also been submitted to the JORS.

Previously, I developed a prototype distributed component-based simulation architecture in Java as part of the research undertaken for my PhD in Management Science. The thesis tackles the issue of how to deal with simulation models that are becoming increasingly complex and large. Existing simulation software does not seem to cope well with these models. This thesis argues that future simulation software should have the features of modularity, reuse, hierarchical structuring, scaling, portability, interoperability, distributed execution, capability to execute over the Internet and/or the web and ease of use of VIMS graphical environments. The objective of this thesis is to demonstrate that it is possible and useful to apply component technologies to simulation approaches and support the previous features. I obtained my Ph.D. degree in November 2002. My academic qualifications also include the equivalent of a First Class BSc (Hons) in Mathematics, with a major in Probability and Statistics. While I was carrying out the research for my Ph.D., I also occasionally worked as a laboratory assistant and marked some student work.

In the future, I would enjoy carrying out research in any topic related to Operational Research and Stochastic Processes, especially in optimisation, as I have enjoyed working successfully in these fields in my current job. Nevertheless, I would also enjoy doing research on other topics of Operational Research. I am also looking forward to taking part on projects, should the occasion arise. I would especially like to have the opportunity to extend my experience to other industries.

Finally, I can confidently teach any of the subjects I have used in my professional career: stochastic processes, optimisation, modelling and simulation, mathematical programming, probability, Markovian decision processes, deterministic and probabilistic models in operations research and also, although not really OR, introduction to programming (Java or C++).