Computational Models for Scheduling in Online Advertising

Programmatic advertising is an actively developing industry and research area. Some of the research in this area concerns the development of optimal or approximately optimal contracts and policies between publishers, advertisers and intermediaries such as ad networks and ad exchanges. Both the development of contracts and the construction of policies governing their implementation are difficult challenges, and different models take different features of the problem into account. In programmatic advertising decisions are made in real time, and time is a scarce resource particularly for publishers who are concerned with content load times. Policies for advertisement placement must execute very quickly once content is requested; this requires policies to either be pre-computed and accessed as needed, or for the policy execution to be very efficient. We formulate a stochastic optimization problem for per publisher ad sequencing with binding latency constraints. Within our context an ad request lifecycle is modeled as a sequence of one by one solicitations (OBOS) subprocesses/lifecycle stages. From the viewpoint of a supply side platform (SSP) (an entity acting in proxy for a collection of publishers), the duration/span of a given lifecycle stage/subprocess is a stochastic variable. This stochasticity is due both to the stochasticity inherent in Internet delay times, and the lack of information regarding the decision processes of independent entities. In our work we model the problem facing the SSP, namely the problem of optimally or near-optimally choosing the next lifecycle stage of a given ad request lifecycle at any given time. We solve this problem to optimality (subject to the granularity of time) using a classic application of Richard Bellman's dynamic programming approach to the 0/1 Knapsack Problem. The DP approach does not scale to a large number of lifecycle stages/subprocesses so a sub-optimal approach is needed. We use our DP formulation to derive a focused real time dynamic programming (FRTDP) implementation, a heuristic method with optimality guarantees for solving our problem. We empirically evaluate (through simulation) the performance of our FRTDP implementation relative to both the DP implementation (for tractable instances) and to several alternative heuristics for intractable instances. Finally, we make the case that our work is usefully applicable to problems outside the domain of online advertising

A network option portfolio management framework for adaptive transportation planning

A real option portfolio management framework is proposed to make use of an adaptive network design problem developed using stochastic dynamic programming methodologies. The framework is extended from Smit's and Trigeorgis' option portfolio framework to incorporate network synergies. The adaptive planning framework is defined and tested on a case study with time series origin-destination demand data. Historically, OD time series data is costly to obtain, and there has not been much need for it because most transportation models use a single time-invariant estimate based on deterministic forecasting of demand. Despite the high cost and institutional barriers of obtaining abundant OD time series data, we illustrate how having higher fidelity data along with an adaptive planning framework can result in a number of improved management strategies. An insertion heuristic is adopted to run the lower bound adaptive network design problem for a coarse Iran network with 834 nodes, 1121 links, and 10 years of time series data for 71,795 OD pairs.Transportation planning Portfolio management Real options Adaptive network design Intercity truck flow

Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems

In many parts of the world, passengers traveling on underground metro systems do not enjoy uninterrupted Internet connectivity. This results in passenger frustration since during such trips the access of online social media services is a highly popular activity. Being the world's oldest underground metro system, London's underground is a typical transportation environment, where the Internet connectivity is often not available during journeys which predominantly take place underground along sub-surface and deep-level track lines. To alleviate the absence of continuous connectivity, we designed DeepOpp, a context-aware mobile system that facilitates offline access to online social media content. The DeepOpp operates efficiently due to its opportunistic approach: it executes content prefetching and caching operations when adequate urban 3G or WiFi signal is detected. The functionality of DeepOpp includes the crowdsourcing of measurements of signal characteristics (strength, bandwidth availability, and latency) which are subsequently used in predicting mobile network signal coverage and initiating data prefetching operations. During data prefetching, an optimization scheme selectively specifies the social media content to be cached based on current network conditions and device storage availability. We implemented DeepOpp as an Android application which we trialled during real trips on the London underground. Our evaluations show that the DeepOpp offers significant reduction when compared with existing approaches in terms of power usage and the volume of data downloaded. Even though we only tested DeepOpp in the London underground metro system, its feature set makes it readily applicable in similar underground metro systems (in cities like New York, Paris, and Shanghai) as well as in situations, where mobile device users suffer from significant connectivity interruptions