Including Limited Partners in the Diversity Jurisdiction Analysis

This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29–30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition

Modelling tuberculosis in areas of high HIV prevalence

We describe a discrete event simulation model of tuberculosis (TB) and HIV disease, parameterized to describe the dual epidemics in Harare, Zimbabwe. TB and HIV are the leading causes of death from infectious disease among adults worldwide and the number of TB cases has risen significantly since the start of the HIV epidemic, particularly in Sub-Saharan Africa, where the HIV epidemic is most severe. There is a need to devise new strategies for TB control in countries with a high prevalence of HIV. This model has been designed to investigate strategies for reducing TB transmission by more efficient TB case detection. The model structure and its validation are discussed

Classification analysis for simulation of machine breakdowns

Machine failure is often an important factor in throughput of manufacturing systems. To simplify the inputs to the simulation model for complex machining and assembly lines, we have derived the Arrows classification method to group similar machines, where one model can be used to describe the breakdown times for all of the machines in the group and breakdown times of machines can be represented by finite mixture model distributions. The Two-Sample Cram´er-von Mises statistic is used to measure the similarity of two sets of data. We evaluate the classification procedure by comparing the throughput of a simulation model when run with mixture models fitted to individual machine breakdown times; mixture models fitted to group breakdown times; and raw data. Details of the methods and results of the grouping processes will be presented, and will be demonstrated using an example

Prior and candidate models in the Bayesian analysis of finite mixtures

This paper discusses the problem of fitting mixture models to input data. When an input stream is an amalgam of data from different sources then such mixture models must be used if the true nature of the data is to be properly represented. A key problem is then to identify the different components of such a mixture, and in particular to determine how many components there are. This is known to be a non-regular/non-standard problem in the statistical sense and is technically notoriously difficult to handle properly using classical inferential methods. We discuss a Bayesian approach and show that there is a theoretical basis why this approach might overcome the problem. We describe the Bayesian approach explicitly and give examples showing its application

Comparison of simulation output series using bootstrapping

We describe a method for comparing stochastic outputs of simulation models. The method is distribution-free and allows the comparison of sets of data with different numbers of data points. This makes it ideal for performing comparisons between simulation output and the real output of the system being modelled, when often there are many more data points available from the output of the simulation model than present in the real data. We calculate the two-sample Cramer-von Mises goodness-of-fit statistic between the two sets of data, using bootstrapping to find the distribution of the statistic, and so the probability that the two sets of data were drawn from the same distribution

Revenue management for perishable products using simulation

We describe a methodology to find the expected number of sales for a given stock of a perishable product, and hence the optimal pricing strategy, over a given finite selling period. The methodology uses a stochastic simulation model that provides confidence ranges on the numbers of sales over the selling period. These ranges are designed to provide warnings to users of unusual buying behaviour. An updating procedure is also described that allows us to quickly update the price structure during the selling period as information about cumulative sales becomes available. We present two examples showing how the optimal price structure is updated during the selling period based on the latest sales data

Maximizing revenue in the airline industry under one-way pricing

The paper describes a methodology that has been implemented in a major British airline to find the optimal price to charge for airline tickets under one-way pricing. An analytical model has been developed to describe the buying behaviour of customers for flights over the selling period. Using this model and a standard analytical method for constrained optimization, we can find an expression for the optimal price structure for a flight. The expected number of bookings made on each day of the selling period and in each fare class given these prices can then be easily calculated. A simulation model is used to find the confidence ranges on the numbers of bookings and these ranges can be used to regulate the sale of tickets. A procedure to update the price structure based on the remaining capacity has also been developed