Dynamic pricing

About: Dynamic pricing is a research topic. Over the lifetime, 4144 publications have been published within this topic receiving 91390 citations. The topic is also known as: surge pricing & demand pricing.

The Future of e-Markets: Multidimensional Market Mechanisms

Abstract: 1. Electronic commerce and electronic marketplaces 1.1. Market-based coordination 1.2. Fixed versus dynamic pricing 1.3. Advanced auction design for electronic markets 1.4. The structure of this book 2. Internet marketplaces - a technical perspective 2.1. The role of electronic brokers 2.2. Electronic brokerage services on the internet 2.3. Software architectures of electronic commerce applications 2.4 Services of an electronic broker 2.5. OFFER - a sample implementation 2.6. Technical challenges and directions 3. The difficulty of setting prices 3.1. Market structures 3.2. Setting optimal prices 3.3. Towards dynamic pricing 4. Methods for the analysis and design of electronic markets 4.1. Equilibrium theory 4.2. Game theory 4.3. Mechanism design theory 4.4. Experimental economics 4.5. Computational economics and simulation 5. Automated negotiations - a survey of state-of-the-art practices 5.1. A roadmap to negotiation situations in electronic commerce 5.2. One-on-one bargaining 5.3. Multilateral negotiations 5.4. A critical review of game-theoretical auction models 5.5. Experimental analysis of standard auction mechanisms 5.6. Towards new frontiers - multi-unit auctions 5.7. Analysis of online auctions 5.8. Summary 6. Experimental analysis of multi-attribute auctions 6.1. Multi-attribute procurement negotiations 6.2. Description of the analyzed mechanisms 6.3. Research questions 6.4. Trading financial derivatives - a sample scenario 6.5. Implementation of an electronic brokerage service 6.6. Results of a laboratory experiment 7. Economic models of multi-attribute auctions 7.1. Previous game-theoretical analysis 7.2. Comparison of conventional and multi-attribute auctions 7.3. Multi-unit extensions 7.4. Summary 8. Conclusions and perspectives 8.1. Applicability and prerequisites 8.2. The role of decision support Appendix: utility theory and decision analysis Techniques Basic ideas of utility theory Multi-objective decision analysis References Index.

Joint resource allocation and base-station assignment for the downlink in CDMA networks

Abstract: In this paper, we jointly consider the resource allocation and base-station assignment problems for the downlink in CDMA networks that could carry heterogeneous data services. We first study a joint power and rate allocation problem that attempts to maximize the expected throughput of the system. This problem is inherently difficult because it is in fact a nonconvex optimization problem. To solve this problem, we develop a distributed algorithm based on dynamic pricing. This algorithm provides a power and rate allocation that is asymptotically optimal in the number of mobiles. We also study the effect of various factors on the development of efficient resource allocation strategies. Finally, using the outcome of the power and rate allocation algorithm, we develop a pricing-based base-station assignment algorithm that results in an overall joint resource allocation and base-station assignment. In this algorithm, a base-station is assigned to each mobile taking into account the congestion level of the base-station as well as the transmission environment of the mobile.

Dynamic pricing system and method for complex energy securities

Abstract: A dynamic pricing system for complex energy securities, comprising a communications interface executing on a network-connected server and adapted to receive information from a plurality of iNodes, an event database coupled to the communications interface and adapted to receive events from a plurality of iNodes via the communications interface, a pricing server coupled to the communications interface, and a statistics server coupled to the event database and the pricing server, is disclosed. According to the invention, the pricing server, on receiving a request to establish a price for an energy security, requests at least one statistical indicia of risk from the statistics server, the statistical indicia of risk being computed by the statistics server based on a plurality of historical data obtained from the event database, and the pricing server computes a price for the security based at least in part on the statistical indicia of risk.

Supply Chain Engineering

Abstract: Supply Chain Engineering considers how modern production and operations management (POM) techniques can respond to the pressures of the competitive global marketplace by integrating all activities in the supply chain, adding flexibility to the system, and drastically reducing production cost. Several POM challenges are answered through a comprehensive analysis of concepts and models that assist the selection of outsourcing strategies and dynamic pricing policies. The ramifications of these topics are discussed from local to global perspectives. Supply Chain Engineering also presents • inventory control policies, • radio frequency identification (RFID) technologies, • flexible and re-configurable manufacturing systems, • real-time assignment and scheduling methods, • new warehousing techniques. In addition, a significant part of the book is devoted to: lean manufacturing, line balancing (assembly lines, U-lines, and bucket brigades), and dynamic facilities layout approaches. Explanations are given using basic examples and detailed algorithms, while discarding complex and unnecessary theoretical minutiae. Moreover, all the examples have been carefully selected with a view to eventual industrial application. Supply Chain Engineering is written for students and professors in industrial and systems engineering, management science, operations management, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.

Blind Network Revenue Management

Abstract: We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time but does not know the underlying demand function that maps prices into instantaneous demand rate. We introduce a family of “blind” pricing policies that are designed to balance trade-offs between exploration demand learning and exploitation pricing to optimize revenues. We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori, and we prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.