Managing On-air Ad Inventory in Broadcast Television

This is the author's accepted manuscript. The original publication is available at http://dx.doi.org/10.1080/07408170802323026.Motivated by the experiences of the National Broadcasting Company (NBC), we present an analytical model for managing on-air ad inventory in broadcast television. The ad inventory in this industry is priced based on rating points or the number of viewers that watch a commercial. The rating points during a broadcast year are sold through two distinct processes: the Upfront, which occurs before the broadcast season, and the Scatter, which occurs during the broadcast season. A firm needs to allocate its total rating points inventory to these two markets before knowing either the performance rating of its shows or the Scatter market price, both of which are random. The networks offer ratings (performance) guarantees on the inventory that is sold in the Upfront market while such guarantees are seldom offered in the Scatter market. We propose an optimization model for the networks to manage their rating points inventory. Our model explicitly incorporates the performance uncertainty of the television shows as well as the revenue uncertainty of the Scatter market. We derive conditions for feasibility of the problem and characterize the optimal amount of rating points to sell in the Upfront market. Our model explains the current practice of selling around 60-80% of the total rating points for the season during the Upfront market and analyzes other common strategies used by the firms. In addition to providing key managerial insights, our work introduces quantitative methodologies to television networks in planning their Upfront markets

Multiobjective Financial Portfolio Design: A Hybrid Evolutionary Approach

Abstract—A principal challenge in modern computational finance is efficient portfolio design – portfolio optimization followed by decision-making. Optimization based on even the widely used Markowitz two-objective mean-variance approach becomes computationally challenging for real-life portfolios. Practical portfolio design introduces further complexity as it requires the optimization of multiple return and risk measures subject to a variety of risk and regulatory constraints. Further, some of these measures may be nonlinear and nonconvex, presenting a daunting challenge to conventional optimization approaches. We introduce a powerful hybrid multiobjective optimization approach that combines evolutionary computation with linear programming to simultaneously maximize these return measures, minimize these risk measures, and identify the efficient frontier of portfolios that satisfy all constraints. We also present a novel interactive graphical decision-making method that allows the decision-maker to quickly down-select to a small subset of efficient portfolios. The approach has been tested on real-life portfolios with hundreds to thousands of assets, and is currently being used for investment decisionmaking in industry. Index Terms—Evolutionary algorithms, linear programming, multiobjective decision-making, Pareto sorting, target objectives, portfolio optimization. I