scholarly journals Optimal Pricing and Service Provisioning Strategies in Cloud Systems: A Stackelberg Game Approach

2013 ◽  
Author(s):  
Valerio Di Valerio ◽  
Valeria Cardellini ◽  
Francesco Lo Presti
Keyword(s):  
Stackelberg Game ◽  
Optimal Pricing ◽  
Service Provisioning ◽  
Cloud Systems ◽  
Provisioning Strategies

scholarly journals Optimal Pricing for Service Provisioning in the Secondary Spectrum Market

2018 ◽  
Vol 232 ◽  
pp. 04068
Author(s):  
Fan Gu ◽  
Xianwei Li ◽  
Liang Zhao ◽  
Haiyang Zhang ◽  
Xiaoying Yang
Keyword(s):  
Spectrum Sharing ◽  
Solution Method ◽  
Stackelberg Game ◽  
Optimal Pricing ◽  
Service Provisioning ◽  
Network Access ◽  
Secondary Users ◽  
Second Stage ◽  
Access Services ◽  
Spectrum Market

In this paper, we investigate the problem of spectrum sharing in a secondary spectrum market where one secondary operator provisions network access services to a number of secondary users (SUs) by leasing spectrum from spectrum holder. For the system model under consideration, the spectrum allocated to the secondary operator can be shared by SUs. We model the interaction between the secondary operator and SUs as a two-stage Stackelberg game, where the secondary operator network price decisions in the first stage, and SUs make their spectrum demands decisions in the second stage. We use the backward induction method to solve this game. The numerical results show that the proposed solution method can capture the main factors of the secondary spectrum market, and provide a promising framework for the design of future secondary CR systems.

Data-driven online service supply chain: a demand-side and supply-side perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Li ◽  
Shaojun Ma ◽  
Xu Han ◽  
Chundong Zheng ◽  
Di Wang
Keyword(s):  
Influencing Factors ◽  
Stackelberg Game ◽  
Data Driven ◽  
Optimal Pricing ◽  
Supply Side ◽  
Pricing Strategies ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Online Service ◽  
Content Type

PurposeBig data analytics (BDA) and machine learning (ML) can be used to identify the influencing factors of online service supply chains (OSSCs) and can help in the formulation of optimal pricing strategies. This paper analyzes the influencing factors of customer online shopping from the demand-side perspective and formulates optimal pricing strategies from the supply-side perspective.Design/methodology/approachThis paper uses ML and the Stackelberg game approach to discuss OSSC management. ML's feature selection algorithm is used to identify the important influencing factors of 12,330 customers' online shopping intention data using four different classifiers. The Stackelberg game approach is used to analyze the pricing strategies of integrators and suppliers in OSSCs.FindingsFirst, the feature selection algorithm can improve the efficiency of optimization in big data samples of OSSCs. Second, the level of visualization and the quality of information (page value) will affect the purchase behavior of customers. Finally, the relationship between the optimal pricing and the level of visualization is obtained through the Stackelberg game approach.Practical implicationsThis paper reveals the phenomenon of “mystery customers,” and the results of this paper can provide insights and suggestions regarding the decision-making behavior of integrators and suppliers in OSSC management.Originality/valueConsidering customer behavior intention, this paper uses a data-driven method to explore the influencing factors and pricing strategies of OSSCs. The empirical results enrich the existing OSSC management research, proposing that the level of product visualization and information quality plays an important role in OSSCs.

scholarly journals A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6625
Author(s):  
Yang Wang ◽  
Yuankun Lin ◽  
Lingyu Chen ◽  
Jianghong Shi
Keyword(s):  
Traffic Safety ◽  
Point Processes ◽  
Stackelberg Game ◽  
Content Delivery ◽  
Transportation Systems ◽  
Stackelberg Equilibrium ◽  
Pricing Strategy ◽  
Optimal Pricing ◽  
Incentive Scheme ◽  
Optimal Pricing Strategy

As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery.

scholarly journals Competing Reverse Channels’ Performance with Sustainable Recycle Innovation Input

2020 ◽  
Vol 10 (16) ◽  
pp. 5429 ◽  
Author(s):  
Ran Liu ◽  
Bisheng Du ◽  
Wenwen Yuan ◽  
Guiping Li
Keyword(s):  
Hybrid Model ◽  
Closed Loop ◽  
Stackelberg Game ◽  
The Other ◽  
Third Party ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Basic Model ◽  
Innovation Barriers ◽  
The Impact

Increasing attention to sustainable development issues and recycling are forcing the recyclers to use different incentives to capture more market share. Recycling innovation input is one of the effective topics in reverse competitive chains. Because of the importance of this issue, firstly, a basic closed-loop supply chain (CLSC) system is discussed that includes an integrated manufacturer and a third-party collector. Then the impact of the integration with the innovation input into third-party product collectors is considered. Eventually, two models are constructed. The first model is a basic model that includes an integrated manufacturer and one third-party collector with innovation investment. The other model is the hybrid model that includes an integrated manufacturer and two third-party collectors with and without innovation input. Stackelberg game models are used to study the optimal pricing strategies for all three models and players’ attitudes toward different scenarios. Finally, numerical analysis is presented. Our findings are generated on the following three aspects. The collector’s recycling choice, recycling innovation input, and influence on recyclers and manufacturers. It is found that the manufacturer will always choose to recycle and prefers the hybrid recycling market, which depends on the rate of collection and the compensation from production-collecting. Moreover, the results reveal that the highest return rate of recyclers occurred under the hybrid model. However, the recyclers may not be able to invest the sustainable recycle innovation input under the exorbitant innovation barriers.

scholarly journals Pricing Decisions of Two Complementary Products in a Fuzzy Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Li Wang ◽  
Jing Zhao ◽  
Jie Wei
Keyword(s):  
Closed Form ◽  
Stackelberg Game ◽  
Reference Model ◽  
Optimal Pricing ◽  
Pricing Model ◽  
Complementary Products ◽  
Nash Game ◽  
Retail Pricing ◽  
Pricing Decisions ◽  
Fuzzy Environment

Pricing decisions of two complementary products in a fuzzy environment are considered in this paper. The purpose of this paper is to analyze the changes of the optimal retail pricing of two complementary products under two different decentralized decision scenarios (e.g., Nash game case and Stackelberg game case). As a reference model, the centralized pricing model is also established. The closed-form optimal pricing decisions of the two complementary products are obtained in the above three decision scenarios. Some interesting management insights into how pricing decisions vary with decision scenarios are given.

scholarly journals Generalized Nash Equilibria for the Service Provisioning Problem in Multi-Cloud Systems

2017 ◽  
Vol 10 (3) ◽  
pp. 381-395 ◽  
Author(s):  
Danilo Ardagna ◽  
Michele Ciavotta ◽  
Mauro Passacantando
Keyword(s):  
Nash Equilibria ◽  
Service Provisioning ◽  
Cloud Systems ◽  
Generalized Nash Equilibria ◽  
Multi Cloud

scholarly journals Optimal pricing strategies based on time delay in multi-service networks with priority

2005 ◽  
Vol 18 (3) ◽  
pp. 547-558 ◽  
Author(s):  
Jin Fude ◽  
Jing Yuanwei ◽  
Zhou Jianhua ◽  
Khosrow Sohraby ◽  
Georgi Dimirovski
Keyword(s):  
Game Theory ◽  
Time Delay ◽  
Stackelberg Game ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Service Networks ◽  
Incentive Strategy ◽  
Service Priority

The problem of pricing equilibrium of multi-service priority-based net- work is studied by using incentive strategy in Stackelberg game theory. First some concepts in game theory were introduced. Then, the existing results on two-user two-level Nash problem was introduced briefly. A new one-leader two-user two-level incentive Stackblberg strategy is presented by employing the time delay in the strategy.

scholarly journals Research on Pricing and Service Level Strategies of Dual Channel Reverse Supply Chain Considering Consumer Preference in Multi-Regional Situations

2020 ◽  
Vol 17 (23) ◽  
pp. 9143
Author(s):  
Yao Kang ◽  
Juhong Chen ◽  
Di Wu
Keyword(s):  
Stackelberg Game ◽  
Consumer Preference ◽  
Service Level ◽  
Third Party ◽  
Optimal Pricing ◽  
Reverse Supply Chain ◽  
Service Levels ◽  
Dual Channel ◽  
Transfer Prices ◽  
High Level

Facing the increasingly serious waste electrical and electronic equipment (WEEE) recycling problem, recycling enterprises actively introduce online recycling channels, build dual channel reverse supply chains (DRSC), and use high-level recycling price and service levels to enhance consumers’ recycling enthusiasm and recycling amount. Nevertheless, in China, where the imbalance of regional development is widespread, the recycling center, third-party recycler (TPR), and third-party platform (TPP) are faced with the choices of pricing and service level when facing multi-regional consumers. This paper mainly answers the following questions: (1) When the recycling center and TPP introduce online recycling channels in multi-regional situations, how should they set online recycling price, transfer price, and service level? (2) When consumer preference for online channels changes in a certain region, how should recycling enterprises adjust their optimal pricing and service level decisions for different regions to maximize their own profits? How do the profits of recycling enterprises change? In order to solve the above problems, in this paper, we propose three pricing and service level decision models for the recycling center with online channels, namely, keeping prices unchanged, unifying all prices, and maximizing its own profits. By using the Stackelberg game to solve the model, we get the optimal pricing, service level decisions, as well as the maximum profits of the recycling center, TPP, and TPR when consumer preference changes. By analyzing the results of the model, we find that the change of consumer preference for online channels in a certain region will affect the decision and profits of multi-regional recycling enterprises. Specifically, consumer preference for online channels in a certain region will not only lead to an increase in the profits of the recycling center and TPP and a decrease in the profit of local TPRs, but also an increase in the profit of TPRs in other regions. In addition, at the beginning of introducing online channels, the recycling center can adopt two strategies to avoid conflicts among channels: keeping offline transfer prices unchanged and unifying all transfer prices, but the former promotes its economic profits more significantly.

Optimal pricing model of car-sharing: market pricing or platform pricing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ling Liang ◽  
Lin Tian ◽  
Jiaping Xie ◽  
Jianhong Xu ◽  
Weisi Zhang
Keyword(s):  
Stackelberg Game ◽  
Revenue Sharing ◽  
Mature Stage ◽  
Optimal Pricing ◽  
Car Sharing ◽  
Market Pricing ◽  
Content Type ◽  
Pricing Mechanism ◽  
Revenue Sharing Contract ◽  
Automobile Manufacturer

PurposeThe car-sharing market has entered the mature stage, and consumers' demand shows a diversified increasing trend. This paper considers two modes of operation and two pricing strategies, which are business-to-consumer and consumer-to-consumer modes, market pricing and platform pricing. Under these conditions, the platform's revenue-sharing ratio will be different. The purpose of this paper is to explore this research question, and seeks an optimal pricing mechanism that can achieve a win–win situation between platform and automobile manufacturer in the two market modes.Design/methodology/approachThe authors design different profit functions for platform under the two contexts. Of course, the platform's function is constrained to the manufacturer's function. By introducing a revenue-sharing contract a Stackelberg game model dominated by the platform is established and the equilibrium solutions under the two pricing models are derived.FindingsThe study found that even if only market pricing is executed, the scale of the car-sharing market will continue to expand. As the car-sharing market becomes more saturated, platform pricing is better for the automobile manufacturer; in most cases, the platform prefers platform pricing, but when the number of private cars is relatively small, if the cost of car operation and maintenance for the automobile manufacturer is lower or the revenue-sharing ratio of private cars is high, then market pricing will be more favorable to the platform.Practical implicationsWith the cross-border integration of car service platforms and the automobile manufacturing industry, the key to achieving win–win cooperation and sustainable development in the car-sharing market will converge on the question of how to design a suitable pricing mechanism and revenue-sharing method.Originality/valueAuthors have determined how a car-sharing platform achieves a win–win order pricing strategy with the manufacturer and private car owners, respectively. And authors combined the supply chain revenue-sharing contract with the car-sharing market to explore the application of the revenue-sharing contract in the sharing economy.

scholarly journals Game-Theoretic Resource Pricing and Provisioning Strategies in Cloud Systems

2020 ◽  
Vol 13 (1) ◽  
pp. 86-98 ◽  
Author(s):  
Valeria Cardellini ◽  
Valerio Di Valerio ◽  
Francesco Lo Presti
Keyword(s):  
Cloud Systems ◽  
Resource Pricing ◽  
Provisioning Strategies ◽  
Game Theoretic
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