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.

Optimal Pricing Strategies Based on Time Delay in Multi-Service Networks

2001 ◽  
Vol 34 (3) ◽  
pp. 105-110
Author(s):  
Fude Jin ◽  
Yuanwei Jing ◽  
Zhianhua Zhou ◽  
G.M. Dimirovske ◽  
K. Sohraby
Keyword(s):  
Time Delay ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Service Networks

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 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.

Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information

2020 ◽  
Vol 10 (5) ◽  
pp. 1557
Author(s):  
Weijia Feng ◽  
Xiaohui Li
Keyword(s):  
Resource Allocation ◽  
Incomplete Information ◽  
Stackelberg Game ◽  
Superior Performance ◽  
Channel Gain ◽  
Mobile Users ◽  
Pricing Strategies ◽  
Game Equilibrium ◽  
Allocation Process ◽  
Realistic Situation

Ultra-dense and highly heterogeneous network (HetNet) deployments make the allocation of limited wireless resources among ubiquitous Internet of Things (IoT) devices an unprecedented challenge in 5G and beyond (B5G) networks. The interactions among mobile users and HetNets remain to be analyzed, where mobile users choose optimal networks to access and the HetNets adopt proper methods for allocating their own network resource. Existing works always need complete information among mobile users and HetNets. However, it is not practical in a realistic situation where important individual information is protected and will not be public to others. This paper proposes a distributed pricing and resource allocation scheme based on a Stackelberg game with incomplete information. The proposed model proves to be more practical by solving the problem that important information of either mobile users or HetNets is difficult to acquire during the resource allocation process. Considering the unknowability of channel gain information, the follower game among users is modeled as an incomplete information game, and channel gain is regarded as the type of each player. Given the pricing strategies of networks, users will adjust their bandwidth requesting strategies to maximize their expected utility. While based on the sub-equilibrium obtained in the follower game, networks will correspondingly update their pricing strategies to be optimal. The existence and uniqueness of Bayesian Nash equilibrium is proved. A probabilistic prediction method realizes the feasibility of the incomplete information game, and a reverse deduction method is utilized to obtain the game equilibrium. Simulation results show the superior performance of the proposed method.

scholarly journals Pricing Strategies of Logistics Distribution Services for Perishable Commodities

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 186
Author(s):  
Tao Li ◽  
Yan Chen ◽  
Taoying Li
Keyword(s):  
Dynamic Pricing ◽  
Consumer Satisfaction ◽  
Service Providers ◽  
Time Constraints ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Pricing Model ◽  
Logistics Service ◽  
Distribution Services ◽  
Perishable Goods

The problem of pricing distribution services is challenging due to the loss in value of product during its distribution process. Four logistics service pricing strategies are constructed in this study, including fixed pricing model, fixed pricing model with time constraints, dynamic pricing model, and dynamic pricing model with time constraints in combination with factors, such as the distribution time, customer satisfaction, optimal pricing, etc. By analyzing the relationship between optimal pricing and key parameters (such as the value of the decay index, the satisfaction of consumers, dispatch time, and the storage cost of the commodity), it is found that the larger the value of the attenuation coefficient, the easier the perishable goods become spoilage, which leads to lower distribution prices and impacts consumer satisfaction. Moreover, the analysis of the average profit of the logistics service providers in these four pricing models shows that the average profit in the dynamic pricing model with time constraints is better. Finally, a numerical experiment is given to support the findings.

scholarly journals A Welfare Assessment of Revenue Management Systems

2020 ◽  
Vol 19 (1) ◽  
pp. 1-41
Author(s):  
Nicolas Dupuis ◽  
Marc Ivaldi ◽  
Jerome Pouyet
Keyword(s):  
Theoretical Model ◽  
Revenue Management ◽  
Consumer Preferences ◽  
Consumer Surplus ◽  
Capacity Constraints ◽  
Management Systems ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Welfare Impact ◽  
Welfare Assessment

AbstractWe study the welfare impact of revenue management, a practice which is widely spread in the transport industry, but whose impact on consumer surplus remains unclear. We develop a theoretical model of revenue management allowing for heterogeneity in product characteristics, capacity constraints, consumer preferences, and probabilities of arrival. We also introduce dynamic competition between revenue managers. We solve this model computationally and recover the optimal pricing strategies. We find that revenue management is generally welfare enhancing as it raises the number of sales.

scholarly journals Selling to strategic customers with cost uncertainty

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Guodaohou Song ◽  
Xiaofang Wang
Keyword(s):  
Production Cost ◽  
Learning Effect ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Strategic Customers ◽  
Cost Uncertainty ◽  
Intertemporal Pricing ◽  
Negative Comments ◽  
Second Period

AbstractProduction cost can be influenced by previous sales in an uncertain way. In reality, production cost may decrease in the number of initial buyers due to the learning effect, or increase in the number of initial buyers due to the quality-improving pressure from negative comments of unhappy users. Taking this uncertainty into account, this paper studies the optimal intertemporal pricing strategies of a firm when selling to strategic customers in two periods where production cost in the second period randomly changes with the number of buyers in the first period. Our results suggest how firms should adjust their optimal pricing strategies under different market circumstances.

scholarly journals Pricing Policies in a Retailer Stackelberg O2O Green Supply Chain

2020 ◽  
Vol 12 (8) ◽  
pp. 3236
Author(s):  
Gan Wan ◽  
Gang Kou ◽  
Tie Li ◽  
Feng Xiao ◽  
Yang Chen
Keyword(s):  
Supply Chain ◽  
Stackelberg Game ◽  
Good Combination ◽  
Green Supply Chain ◽  
Game Model ◽  
Mathematical Function ◽  
Pricing Strategies ◽  
Direct Channel ◽  
Retail Channel ◽  
Offline Channels

Due to the popularization of the concept of “new retailing”, we study a new commercial model named O2O (online-to-offline), which is a good combination model of a direct channel and a traditional retail channel. We analyze an O2O supply chain in which manufacturers are responsible for making green products and selling them through both online and offline channels. The retailer is responsible for all online and offline channels’ orders, and the manufacturer gives the retailer a fixed fee. We construct a mathematical function model and analyze the greenness and pricing strategies of centralized and decentralized settings through the retailer Stackelberg game model. Due to the effects of the double marginalization of supply chain members, we adopt a simple contract to coordinate the green supply chain. The paper’s contributions are that we obtain pricing and greening strategies by taking the cooperation of offline channels and online channels into consideration under the O2O green supply chain environment.

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