International Transactions in Operational Research最新文献

This study examines how personalized pricing affects customers’ showrooming behavior in the duopoly competition with horizontal product differentiation between traditional and online retailers. For the available personalized pricing strategies, we obtained the optimal decisions for both retailers in the scenarios with and without showrooming. Our results indicate that adopting personalized pricing under particular conditions enables each retailer to be more profitable, even in the presence of showrooming purchases. This finding explains why an increasing number of online and offline retailers have implemented personalized pricing based on technical means and consumer purchase records. It is also demonstrated that personalized pricing can counter showrooming behavior of consumers amidst the competition between both retailers, which provides a novel theoretical basis for dealing with cross-channel shopping in multichannel retail competition. We further clarify how retailers can avoid being trapped in a prisoner's dilemma when both sides have access to personalized pricing. Also, the adoption of personalized pricing can improve consumer surplus and social welfare.

The flourishing platform business model has been rapidly integrated into manufacturing industries, and value-added service (VAS) provided by the platforms has become a critical part of enhancing competitiveness. This study investigates the optimal two-period pricing decisions and VAS strategies of the two-sided platform for manufacturing. For both the supplier and manufacturer side, the platform decides the entry fees in each period and provides differentiated quality of basic services, as well as the VAS (if any). Moreover, the manufacturer's utility of accessing is influenced by the cross-network externality, which is related to the number of suppliers in each period. In the presence of the supplier's varying entry timing, three VAS strategies are available for the platform including: (1) VAS for suppliers in Period 1 (Model S1), (2) VAS for suppliers in Period 2 (Model S2), and (3) VAS for manufacturers in Period 1 or 2 (Model M). We establish a two-period game model under each VAS strategy. Then, the optimal platform's pricing decisions are derived, and the optimal performances in three VAS strategies are compared. Findings demonstrate that when the cross-network externality strength is large, the platform always prefers Model S1; only when both the cross-network externality strength and quality of the platform's basic service are comparatively low, the platform selects Model S2; otherwise, the platform chooses Model M. This study also extends to the scenario that the platform provides bilateral VAS in Period 1 (Model B1) or Period 2 (Model B2). Notably, the platform is not always willing to offer bilateral VAS, especially when the VAS cost coefficient is relatively high.

Innovation is one of the driving forces of economic development and social progress, and the crowdsourcing contest is a well-established mechanism for encouraging innovation. This paper examines two incentive schemes in two-stage innovation contests: feedback and elimination. Feedback enhances the efforts by revealing the competitive status, and elimination intensifies the competition by removing less-qualified participants. We build a game theoretical model to investigate how the organizer should design the feedback and elimination schemes and then analyze the equilibrium efforts and optimal contest design in four-solver contests. The results suggest that the optimal design depends on the combined effects of the reward, effort sensitivity, and cost coefficiency. Elimination and nonelimination contests can be optimal under different conditions. Furthermore, we extend the equilibrium analysis to competitions with $��n�>�4�$ contestants and investigate the optimal design with numerical studies. The most interesting result is that the elimination contest with feedback from the organizer is an ideal option for a budget-constrained enterprise that seeks an innovative solution from the public for a complex innovation project. Also, the optimal number of contestants in the second stage is not always two when feedback is combined with elimination.

This paper develops a game-theoretic model that examines the entry decisions of two competing platform owners with differentiated research and development (R&D) efficiency and the response strategy of an original equipment manufacturer (OEM) in selecting which platform to cooperate with. The OEM and platform owners compete on quality and price in the face of consumers with heterogeneous preferences across the dimensions of the device and platform. We analyze the interaction between the entry decisions of platform owners and the OEM's response strategy within two cooperation models. In the charging model, we find that in the absence of entry, the OEM prefers the high-efficiency platform. When only one platform owner enters, the OEM prefers the low-efficiency platform. If the differentiated R&D efficiency and the device differentiation cost are large, both platform owners will choose to enter and the OEM will cooperate with the low-efficiency platform. In the bundling model, when only one platform owner enters, only at a large cost of device differentiation will the OEM cooperate with the platform that enters the market to weaken market competition. In particular, when only the high-efficiency platform enters, it is interesting that there exists a large cost of device differentiation that makes the high-efficiency platform more favorable in the case when the OEM cooperates with the low-efficiency platform. Our study provides practical insights into platform entry and OEM response.

In this paper, we discuss discrete location problems where the objective is to locate a given number of facilities of different types in order to appropriately cover a given set of demand points. The coverage provided to each demand point is the result of the cooperation among the located facilities. The different types of facility refer to the coverage radius or quality of coverage that each type may provide. We present a non-linear formulation of the problem where the objective is to maximize the percentage of demand points that are appropriately covered. We then show how the model can be linearized based on a representation of probabilities through a network structure. To address large instances of the problem, we introduce a genetic algorithm (GA) that is based on the representation of the solution by two chromosomes. Computational experiments over a wide range of randomly generated problems indicate the GA clearly outperforms CPLEX, especially in larger instances.