Dynamic Revenue Management for Online Display Advertising. G. Roels, K. Fridgeirsdottir. Journal of Revenue and Pricing Management. 8(5): 452-466. November 2009.
In this paper, we propose a dynamic optimisation model to maximise a web publisher's online display advertising revenues. Our model dynamically selects which advertising requests to accept and dynamically delivers the promised advertising impressions to viewers so as to maximise revenue, accounting for uncertainty in advertising requests and website traffic. After characterising the structural properties of our model, we propose a Certainty Equivalent Control heuristic and then show with a real case study that our optimisation-based method outperforms common practices. These results highlight the importance of accounting for the opportunity cost of capacity allocation in advertisement contract negotiation for globally maximising online publishers' revenues.
Contracting for Collaborative Services. G. Roels, U.S. Karmarkar, S. Carr. Management Science. 56(5): 849-863. May 2010.
In this paper, we analyze the contracting issues that arise in collaborative services, such as consulting, financial planning, and information technology outsourcing. In particular, we investigate how the choice of contract type--among fixed-fee, time-and-materials, and performance-based contracts--is driven by the service environment characteristics. We find that fixed-fee contracts contingent on performance are preferred when the service output is more sensitive to the vendor's effort, that time-and-materials contracts are optimal when the output is more sensitive to the buyer's effort, and that performance-based contracts dominate when the output is equally sensitive to both the buyer's and the vendor's inputs. We also discuss how the performance of these contracts is affected with output uncertainty, process improvement opportunities, and the involvement of multiple buyers and vendors in the joint-production process. Our model highlights the trade-offs underlying the choice of contracts in a collaborative service environment and identifies service process design changes that improve contract efficiency.
Robust Controls for Network Revenue Management. G. Perakis, G. Roels. Manufacturing & Service Operations Management. 12(1): 56-76. Winter 2010.
Revenue management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is, however, often difficult to characterize, especially in new or nonstationary markets. In this paper, we develop robust formulations for the capacity allocation problem in revenue management using the maximin and the minimax regret criteria under general polyhedral uncertainty sets. Our approach encompasses the following open-loop controls: partitioned booking limits, nested booking limits, displacement-adjusted virtual nesting, and fixed bid prices. In specific problem instances, we show that a booking policy of the type of displacement-adjusted virtual nesting is robust, both from maximin and minimax regret perspectives. Our numerical analysis reveals that the minimax regret controls perform very well on average, despite their worst-case focus, and outperform the traditional controls when demand is correlated or censored. In particular, on real large-scale problem sets, the minimax regret approach outperforms by up to 2% the traditional heuristics. The maximin controls are more conservative but have the merit of being associated with a minimum revenue guarantee. Our models are scalable to solve practical problems because they combine efficient (exact or heuristic) solution methods with very modest data requirements.
Competing for Shelf Space. V. Martínez-de-Albéniz, G. Roels. Production and Operations Management. 20(1): 32-46. January/February 2011.
In recent years, the competition for shelf space has intensified, as more products now compete for a retail space that has remained roughly constant. In this paper, we analyze the dynamics of this competition in a multi-supplier retail point. Assuming that sales are shelf space dependent, we consider a retailer that optimizes its shelf space allocation among different products based on their sales level and profit margins. In this context, product manufacturers set their wholesale prices so as to obtain larger shelf space allocations but at the same time keep margins as high as possible. We analyze the equilibrium situation in the supply chain, and find that generally the retailer's and the suppliers' incentives are misaligned, resulting in suboptimal retail prices and shelf space allocations. We however find that the inefficiencies induced by suboptimal shelf space allocation decisions are small relative to those induced by suboptimal pricing decisions.
The Price of Anarchy in Supply Chains: Quantifying the Efficiency of Price-Only Contracts. G. Perakis, G. Roels. Management Science. 53(8): 1249-1268. August 2007.
In this paper, we quantify the efficiency of decentralized supply chains that use price-only contracts. With a price-only contract, a buyer and a seller agree only on a constant transaction price, without specifying the amount that will be transferred. It is well known that these contracts do not provide incentives to the parties to coordinate their inventory/capacity decisions. We measure efficiency with the Price of Anarchy, defined as the largest ratio of profits between the integrated supply chain (that is, fully coordinated) and the decentralized supply chain. We characterize the efficiency of various supply chain configurations: push or pull inventory positioning, two or more stages, serial or assembly systems, single or multiple competing suppliers, and single or multiple competing retailers.
Regret in the Newsvendor Model with Partial Information. G. Perakis, G. Roels. Operations Research. 56(1): 188-203. January-February 2008.
Traditional stochastic inventory models assume full knowledge of the demand probability distribution. However, in practice, it is often difficult to completely characterize the demand distribution, especially in fast-changing markets. In this paper, we study the newsvendor problem with partial information about the demand distribution (e.g., mean, variance, symmetry, unimodality). In particular, we derive the order quantities that minimize the newsvendor's maximum regret of not acting optimally. Most of our solutions are tractable, which makes them attractive for practical application. Our analysis also generates insights into the choice of the demand distribution as an input to the newsvendor model. In particular, the distributions that maximize the entropy perform well under the regret criterion. Our approach can be extended to a variety of problems that require a robust but not conservative solution.
An Analytical Model for Traffic Delays and the Dynamic User Equilibrium Problem. G. Perakis, G. Roels. Operations Research. 54(6): 1151-1171. November-December 2006.
In urban transportation planning, it has become critical (1) to determine the travel time of a traveler and how it is affected by congestion, and (2) to understand how traffic distributes in a transportation network. In the first part of this paper, we derive an analytical function of travel time, based on the theory of kinematic waves. This travel-time function integrates the traffic dynamics as well as the effects of shocks. Numerical examples demonstrate the quality of the analytical function, in comparison with simulated travel times. In the second part of this paper, we incorporate the travel-time model within a dynamic user equilibrium (DUE) setting. We prove that the travel-time function is continuous and strictly monotone if the flow varies smoothly. We illustrate how the model applies to solve a large network assignment problem through a numerical example.
The Price of Information: Inventory Management with Limited Information about Demand. G. Roels, G. Perakis. Manufacturing & Service Operations Management. 8(1): 102-104. Winter 2006. (Extended Abstracts Winner, MSOM Student Paper Competition)
Highlights the study "The Price of Information: Inventory Management With Limited Information About Demand." Explanation on the Maximin Criterion and the Minimax Regret Criterion; Findings of the study; Recommendations of the study.