A Request-level Guaranteed Delivery Advertising Planning

Zhang, Hong; Zhang, Lan; Xu, Lan; Ma, Xiaoyang; Wu, Zhengtao; Tang, Cong; Xu, Wei; Yang, Yiguo

doi:10.1145/3394486.3403348

Cited by 14 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al (2023) studied the online allocation problem with two-side resource constraints, which can be seen as a novel way to achieve resource-consumption balancing. They also provided several real-life examples, such as the online orders assignment in ecommerce platforms and online advertising platforms (Zhang et al, 2020), where it is required to guarantee a certain amount of resource-consumption. Since our work is most related to (Balseiro et al, 2021), we have thoroughly discussed the technical differences in the introduction section above and we will present the comparison more concretely in Section 3.…”

Section: Fairness and Resource-consumption Balancing In Operationsmentioning

confidence: 99%

Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing

Chen,

Lyu,

Wang

et al. 2024

Production and Operations Management

View full text Add to dashboard Cite

In addition to maximizing the total revenue, decision-makers in lots of industries would like to guarantee balanced consumption across different resources. For instance, in the retailing industry, ensuring a balanced consumption of resources from different suppliers enhances fairness and helps maintain a healthy channel relationship; in the cloud computing industry, resource-consumption balance helps increase customer satisfaction and reduce operational costs. Motivated by these practical needs, this paper studies the price-based network revenue management (NRM) problem with both demand learning and fair resource-consumption balancing. We introduce the regularized revenue, that is, the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal. We propose a primal-dual-type online policy with the upper-confidence-bound demand learning method to maximize the regularized revenue. We adopt several innovative techniques to make our algorithm a unified and computationally efficient framework for the continuous price set and a wide class of balancing regularizers. Our algorithm achieves a worst-case regret of [Formula: see text], where [Formula: see text] denotes the number of products and [Formula: see text] denotes the number of time periods. Numerical experiments in a few NRM examples demonstrate the effectiveness of our algorithm in simultaneously achieving revenue maximization and fair resource-consumption balancing.

show abstract

Section: Fairness and Resource-consumption Balancing In Operationsmentioning

confidence: 99%

Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing

Chen,

Lyu,

Wang

et al. 2024

Production and Operations Management

View full text Add to dashboard Cite

show abstract

“…We have two constraints (4) and (5) represent for (2). Constraint (6) represents the total number of ads displayed on day j at location k does not exceed the number of visitors on day j at the corresponding location k. For the set places T[r], we have the set of constraints as ( 7), ( 8) and (9). Constraints (10) show that if campaign Ci is not display on day j or at place k, the number of advertisements of campaign i run on day j or at place k is 0.…”

Section: Modelmentioning

confidence: 99%

“…According to structure of linear programming with complicating constraints, for each independent set of places t (t=1,…, r) is a sub-problem t with the set of constraint similar as (4), ( 5), (6) for the set of place T[t]:…”

Section: Modelmentioning

confidence: 99%

“…Likewise with most types of web based publicizing (see [3]), one of the focal inquiries that emerges with regards to online graphical presentation promoting is that of resource allocation, i.e., deciding how to allocate supply/inventory (client visits) to request (advertiser campaigns) in order to upgrade for different publisher and advertiser destinations. However, even formulating the inventory allocation problem for online graphical display advertising is quite challenging (see [4][5][6]).…”

Section: Introduction *mentioning

confidence: 99%

See 1 more Smart Citation

Solving Resource Allocation Problem in Wifi Network by Dantzig-Wolfe Decomposition Algorithm

2024

JST: SSAD

View full text Add to dashboard Cite

Online advertising and advancements is a recent trend in marketing technology, in this context we consider a new form of contract which allows advertisers to specify in the Wifi system. Based on the structure of the system, we have to organize and manage resource allocation such that the guaranteed display is satisfied. We introduce a new mathematical model and develop an optimization framework that aims to optimize “fairness” of allocation each campaign over its targeted location. Because of large scale problem, the Dantzig-Wolfe decomposition is proposed for solving it. Dantzig-Wolfe decomposition is a technique for dealing with large scale linear programming and modified to solve linear integer programming, nonlinear programming. Especially, it is used mostly in linear programming when its size is very large, and its structure is appropriate. The technique has been successfully applied in a variety of contexts. In this paper, we introduce a new model of a resource allocation problem in Wifi network and represent Dantzig-Wolfe decomposition for solving this problem by dividing the number of advertisement impressions when users access the Wifi network. The numerical simulation shows the efficiency of our proposed method.

show abstract

“…Gross Merchandise Volume(GMV) for ad providers and publisher's revenue. We generally formulate this widely used guaranteed delivery advertising model as an online resource allocation problem with two-sided constraints (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Online Allocation Problem with Two-sided Resource Constraints

Zhang¹,

Ye²,

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

Motivated by many interesting real-world applications in logistics and online advertising, we consider an online allocation problem subject to lower and upper resource constraints, where the requests arrive sequentially, sampled i.i.d. from an unknown distribution, and we need to promptly make a decision given limited resources and lower bounds requirements. First, with knowledge of the measure of feasibility, i.e., α, we propose a new algorithm that obtains 1 − O( ǫ α−ǫ )competitive for the offline problems that know the entire requests ahead of time. Inspired by the previous study (Devanur et al., 2019), this algorithm adopts an innovative technique to dynamically update a threshold price vector for making decisions. Moreover, an optimization method to estimate the optimal measure of feasibility is proposed with theoretical guarantee at the end of this paper. Based on this method, if we tolerate slight violation of the lower bounds constraints with parameter η, the proposed algorithm is naturally extended to the settings without strong feasible assumption, which cover the significantly unexplored infeasible scenarios.

show abstract

A Request-level Guaranteed Delivery Advertising Planning

Cited by 14 publications

References 9 publications

Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing

Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing

Solving Resource Allocation Problem in Wifi Network by Dantzig-Wolfe Decomposition Algorithm

Online Allocation Problem with Two-sided Resource Constraints

Contact Info

Product

Resources

About