scholarly journals A Re-Solving Heuristic with Uniformly Bounded Loss for Network Revenue Management

2020 ◽  
Vol 66 (7) ◽  
pp. 2993-3009 ◽  
Author(s):  
Pornpawee Bumpensanti ◽  
He Wang
Keyword(s):  
Revenue Management ◽  
Time Horizon ◽  
Original Problem ◽  
Limited Resources ◽  
Management Problem ◽  
Square Root ◽  
Network Revenue Management ◽  
Customer Acceptance ◽  
Uniformly Bounded ◽  
Network Revenue

We consider a canonical quantity-based network revenue management problem where a firm accepts or rejects incoming customer requests irrevocably in order to maximize expected revenue given limited resources. Because of the curse of dimensionality, the exact solution to this problem by dynamic programming is intractable when the number of resources is large. We study a family of re-solving heuristics that periodically re-optimize an approximation to the original problem known as the deterministic linear program (DLP), where random customer arrivals are replaced by their expectations. We find that, in general, frequently re-solving the DLP produces the same order of revenue loss as one would get without re-solving, which scales as the square root of the time horizon length and resource capacities. By re-solving the DLP at a few selected points in time and applying thresholds to the customer acceptance probabilities, we design a new re-solving heuristic with revenue loss that is uniformly bounded by a constant that is independent of the time horizon and resource capacities. This paper was accepted by Kalyan Talluri, revenue management and market analytics.

scholarly journals Online Network Revenue Management Using Thompson Sampling

2018 ◽  
Vol 66 (6) ◽  
pp. 1586-1602 ◽  
Author(s):  
Kris Johnson Ferreira ◽  
David Simchi-Levi ◽  
He Wang
Keyword(s):  
Revenue Management ◽  
Dynamic Pricing ◽  
Domain Knowledge ◽  
Poor Performance ◽  
Management Problem ◽  
Network Revenue Management ◽  
Thompson Sampling ◽  
Main Challenge ◽  
Inventory Constraints ◽  
Network Revenue

Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide attention, as Internet companies have successfully implemented it for online ad display. In “Online network revenue management using Thompson sampling,” K. Ferreira, D. Simchi-Levi, and H. Wang propose using Thompson sampling for a revenue management problem where the demand function is unknown. A main challenge to adopt Thompson sampling for revenue management is that the original method does not incorporate inventory constraints. However, the authors show that Thompson sampling can be naturally combined with a linear program formulation to include inventory constraints. The result is a dynamic pricing algorithm that incorporates domain knowledge and has strong theoretical performance guarantees as well as promising numerical performance results. Interestingly, the authors demonstrate that Thompson sampling achieves poor performance when it does not take into account domain knowledge. Finally, the proposed dynamic pricing algorithm is highly flexible and is applicable in a range of industries, from airlines and internet advertising all the way to online retailing.

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