Through the clouds : urban analytics for smart cities

Data has been collected since mankind, but in the recent years the technical innovations enable us to collect exponentially growing amounts of data through the use of sensors, smart devices and other sources. In her lecture Nanda will explore the role of Big Data in urban environments. She will give an introduction to the world of Big Data and Smart Cities, and an assessment of the role that data analytics plays in the current state of the digital transformation in our cities. Examples are given in the field of energy and mobility

Understanding the role of marketing communications in direct marketing

The standard RFM models used by direct marketers include behavioral variables, but ignore the role of marketing communications. In addition, RFM models allow customer responsiveness to vary across different customers, but not across diiferent time periods. Hence, the authors first extend RFM models by incorporating the effects of marketing communications and temporal heterogeneity. Then, using direct-marketing data from a Dutch charity organization, they calibrate the proposed model, and find that it better explains customer behavior because it includes information on both the past behavior and marketing communications. More specifically, they show that direct mail communication builds goodwill, which, in turn, enhances customer's likelihood to buy. However, cumulative exposure to direct mail creates irritation, and erodes goodwill. The two opposite effects induce a cyclic pattern of goodwill formation, which repeats over four quarters. Next, the authors find that, when they control for these communications effects, the standard result - customer's likelihood to buy increases as shopping frequency increases - reverses. That is, in contrast to the extant literature, customers who donate frequently are less likely to donate in the near future. These findings are not only stable over time, but also replicate across two large data sets. Finally, the authors discuss the need for implementing pulsing strategy to mitigate irritation, and the possibility of practicing one-to-one marketing by using information on customer responsiveness, which can be estimated for each customer via the proposed model

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature

A local search heuristic for unrelated parallel machine scheduling with efficient neighborhood search

The parallel machine scheduling problem with unrelated machines is studied where the objective is to minimize the maximum makespan. In this paper, new local search algorithms are proposed where the neighborhood search of a solution uses the 'efficiency' of the machines for each job. It is shown that this method yields better solutions and shorter running times than the more general local search heuristics

Determining the direct mailing frequency with dynamic stochastic programming

Both in business to business and in consumer markets direct mailings are an important means of communication with individual customers. This paper studies the mailing frequency problem that addresses the issue of how often to send a mailing to an individual customer in order to establish a profitable long-term relationship rather than targeting profitable groups of customers at every new mailing instance. The mailing frequency is optimized using long-term objectives but restricts the decisions to the number of mailings to send to the individual over consecutive finite planning periods. A stochastic dynamic programming model that is formulated for this problem is easy to solved for many applications in direct mailing. A particular implementation of the model will provide the direct mailer with controls to stimulate desired response behavior of their customers. The model is calibrated for a large non-profit organization and shows that very large improvements can be achieved by approaching the mailing strategy with the mailing frequency problem, both in the number of mailing to send and in the profits resulting from the responses

Estimting parameters of a microsimulation model for breast cancer screening using the score function method

In developing decision-making models for the evaluation of medical procedures, the model parameters can be estimated by fitting the model to data observed in trial studies. For complex models that are implemented by discrete event simulation (microsimulation) of individual life histories, the Score Function (SF) method can potentially be an appropriate approach for such estimation exercises. We test this approach for a microsimulation model of screening for cancer that is fitted to data from the HIP randomized trial for early detection of breast cancer. Comparison of the parameter values estimated by the SF method and the analytical solution shows that method performs well on this simple model. The precision of the estimated parameter values depends (as expected) on the size of the simulation number of life histories), and on the number of parameters estimated. Using analytical representations for parts of the microsimulation model can increase the precision in the estimation of the remaining parameters. Compared to the Nelder and Mead Simplex method which is often used in stochastic simulation because of its ease of implementation, the SF method is clearly more efficient (ratio computer time: precision of estimates). The additional analytical investment needed to implement the method in an (existing) simulation model may well be worth the effort

Airline revenue management: an overview of OR techniques 1982-2001

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature

Stochastic programming for multiple-leg network revenue management

Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced probabilistic models. Recently suggested models put even more effort into demand forecasting. We will show that the dynamic booking process rather than the demand forecasts needs to be addressed. In particular the nesting strategies applied in booking control will counter-effect the profitability of advanced estimation of booking limits and bid-prices

Models and techniques for hotel revenue management using a rolling horizon.

This paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays

Media planning by optimizing contact frequencies

In this paper we study a model to estimate the probability that a target group of an advertising campaign is reached by a commercial message a given number of times. This contact frequency distribution is known to be computationally difficult to calculate because of dependence between the viewing probabilities of advertisements. Our model calculates good estimates of contact frequencies in a very short time based on data that is often available. A media planning model that optimizes effective reach as a function of contact frequencies demonstrates the usefulness of the model. Several local search procedures such as taboo search, simulated annealing and genetic algorithms are applied to find a good media schedule. The results show that local search methods are flexible, fast and accurate in finding media schedules for media planning models based on contact frequencies. The contact frequency model is a potentially useful new tool for media planners