Download or read book Optimal Bayesian Demand Learning Over Short Horizons written by Jue Wang and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigate the optimal Bayesian dynamic pricing and demand learning policy over short selling horizons, where the pricing decisions are time-sensitive. The seller fine-tunes the price near an incumbent price in order to maximize the total revenue. The existing literature focuses on policies that are asymptotically optimal, i.e., near optimal when the selling horizons are sufficiently long, but little is known about the optimal Bayesian policies, especially over short horizons. We formulate the problem as a finite-horizon stochastic dynamic program and identify a connection between the optimality equations and the generalized Weierstrass transform (GWT). We fully characterize the structure of the Bayesian optimal policy for the linear Gaussian demand model and prove that the optimal policy adjusts the myopic price away from the incumbent price. A notable exception occurs when the two prices coincide and the precision of the posterior belief exceeds a threshold, in which case it is optimal to forgo learning and use a fixed-price policy. Exploiting the structural results makes it possible to compute the optimal policy efficiently on an ordinary computer.

Download or read book Operationalizing Dynamic Pricing Models written by Steffen Christ and published by Springer Science & Business Media. This book was released on 2011-04-02 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.

Download or read book Retail Supply Chain Management written by Narendra Agrawal and published by Springer. This book was released on 2015-04-20 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition focuses on three crucial areas of retail supply chain management: (1) empirical studies of retail supply chain practices, (2) assortment and inventory planning and (3) integrating price optimization into retail supply chain decisions. The book has been fully updated, expanding on the distinguishing features of the original, while offering three new chapters on recent topics which reflect areas of great interest and relevance to the academic and professional communities alike - inventory management in the presence of data inaccuracies, retail workforce management, and fast fashion retail strategies. The innovations, lessons for practice, and new technological solutions for managing retail supply chains are important not just in retailing, but offer crucial insights and strategies for the ultimate effective management of supply chains in other industries as well. The retail industry has emerged as a fascinating choice for researchers in the field of supply chain management. It presents a vast array of stimulating challenges that have long provided the context of much of the research in the area of operations research and inventory management. However, in recent years, advances in computing capabilities and information technologies, hyper-competition in the retail industry, emergence of multiple retail formats and distribution channels, an ever increasing trend towards a globally dispersed retail network, and a better understanding of the importance of collaboration in the extended supply chain have led to a surge in academic research on topics in retail supply chain management. Many supply chain innovations (e.g., vendor managed inventory) were first conceived and successfully validated in this industry, and have since been adopted in others. Conversely, many retailers have been quick to adopt cutting edge practices that first originated in other industries. Retail Supply Chain Management: Quantitative Models and Empirical Studies, 2nd Ed. is an attempt to summarize the state of the art in this research, as well as offer a perspective on what new applications may lie ahead.

Download or read book Capacity Reservation for Capital intensive Technologies written by Stefan Spinler and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Arguably the central problem in operations research and management science (OR/MS) addressed by e-Business is better coordination of supply and de mand, including price discovery and reduction of transactions costs of buyer seller interactions. In capital-intensive industries like chemicals and steel, the out-of-pocket costs of excess capacity and the opportunity costs of underuti lized capacity have been important factors driving the growth of exchanges for improving demand and supply coordination through e-Business platforms. Stefan Spinler addresses in his dissertation one of the most interesting aspects of this evolution for OR/MS, the parallel development of long-term and short-term markets for capacity and output, accompanied by a range of exotic options and forwards as the basic mechanisms supporting transac tions. This is a fascinating research topic because it builds on the powerful framework of real options, while connecting directly to key operations deci sions (capacity planning, staffing, maintenance, and so forth) of the plants and technologies whose output is the focus of contracts. From the perspec tive of practice, the use of these contracting mechanisms, as facilitated by a new breed of B2B exchanges, represents an opportunity for further improving supply chain performance and capital asset productivity.

Download or read book Optimal Bayesian Mechanisms written by Eric Maskin and published by . This book was released on 1984 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Applications with Practical Measured Results in Electronics Industries written by Mong-Fong Horng and published by MDPI. This book was released on 2020-05-22 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

Download or read book Risk Management written by Michael Frenkel and published by Springer Science & Business Media. This book was released on 2005-12-06 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with all aspects of risk management that have undergone significant innovation in recent years, this book aims at being a reference work in its field. Different to other books on the topic, it addresses the challenges and opportunities facing the different risk management types in banks, insurance companies, and the corporate sector. Due to the rising volatility in the financial markets as well as political and operational risks affecting the business sector in general, capital adequacy rules are equally important for non-financial companies. For the banking sector, the book emphasizes the modifications implied by the Basel II proposal. The volume has been written for academics as well as practitioners, in particular finance specialists. It is unique in bringing together such a wide array of experts and correspondingly offers a complete coverage of recent developments in risk management.

Download or read book Management Science written by and published by . This book was released on 2005 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues for Feb. 1965-Aug. 1967 include Bulletin of the Institute of Management Sciences.

Download or read book Beyond Databases Architectures and Structures written by Stanisław Kozielski and published by Springer. This book was released on 2015-04-28 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2015, held in Ustroń, Poland, in May 2015. This book consists of 53 carefully revised selected papers that are assigned to 8 thematic groups: database architectures and performance; data integration, storage and data warehousing; ontologies and semantic web; artificial intelligence, data mining and knowledge discovery; image analysis and multimedia mining; spatial data analysis; database systems development; application of database systems.

Download or read book Optimization Learning and Control for Interdependent Complex Networks written by M. Hadi Amini and published by Springer Nature. This book was released on 2020-02-22 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

Download or read book Dynamic Optimization and Learning written by Alexandre X. Carvalhi and published by . This book was released on 2005 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model formulation; A formal analysis of the trade-off between optimization and learning; The general multiperiod problem; Monte Carlo simulation; Random prices and estimation bias.

Download or read book Dynamic Pricing with Demand Model Uncertainty written by Mr. Nuri Bora Keskin and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Pricing decisions often involve a tradeoff between learning about customer behavior to increase long-term revenues, and earning short-term revenues. In this thesis we examine that tradeoff. Whenever a firm is not certain about how its customers will respond to price changes, there is an opportunity to use price as a tool for learning about a demand curve. Most firms try to solve the tradeoff between learning and earning by managing these two goals separately. A common practice is to first estimate the parameters of the demand curve, and then choose the optimal price, assuming the parameter estimates are accurate. In this thesis we show that this conventional approach is far from being optimal, running the risk of incomplete learning--a negative statistical outcome in which the decision maker stops learning prematurely. We also propose several remedies to avoid the incomplete learning problem, and guard against poor performance. In Chapter 1, we model a learn-and-earn problem using a theoretical framework in which a seller has a prior belief about the demand curve for its product, and updates his belief upon observing customer responses to successive sales attempts. We assume that the seller's prior is a binary distribution, i.e. one of two demand curves is known to apply, although our analysis can be extended to any finite prior. In this setting, we first analyze the myopic Bayesian policy (MBP), which is a stylized representative of the estimate-and-then-optimize policies described above. Our analysis makes three contributions to the literature: first, we show that under the MBP the seller's beliefs can get stuck at a confounding value, leading to poor revenue performance. This result elucidates incomplete learning as a consequence of myopic pricing. Our second contribution is the development of a constrained variant of the MBP as a way to tweak the MBP in the binary-prior setting. By forbidding prices that are not sufficiently informative, constrained MBP (CMBP) avoids the incomplete learning problem entirely, and moreover, its expected performance gap relative to a clairvoyant who iv knows the underlying demand curve is bounded by a constant independent of the sales horizon. Finally, we generalize the CMBP family to obtain more flexible pricing policies that are suitable in case the seller has an arbitrary prior on model parameters. The incomplete learning result and the pricing policies we design have a practical significance. Because firms have no means to check whether they are suffering from incomplete learning, the myopic policies used in practice need to be modified with some kind of forced price experimentation, and our policies provide guidelines on how price experimentation can be employed to prevent incomplete learning. In Chapter 2, we consider several research questions: for example, when a seller has been charging an incumbent price for a very long time, how can he make use of the information contained in that incumbent price? Or, when a seller offers multiple products with substitutable demand, can he safely employ an independent price experimentation strategy for each product? More importantly, what if the particular pricing policies in literature are not feasible in a given business setting? To handles such cases, can we derive general principles that identify the essential ingredient of successful price experimentation policies? We address these questions using a fairly general dynamic pricing model, where a monopolist sells a set of products over a given time horizon. The expected demand for products is given by a linear curve, the parameters of which are not known by the seller. The seller's goal is to learn the parameters of the demand curve as he keeps trying to earn revenues. This chapter makes four main contributions to the learning-and-earning literature. First, we formulate an incumbent-price problem, where the seller starts out knowing one point on its demand curve, and show that the value of information contained in the incumbent price is substantial. Second, unlike previous studies that focus on a particular form of price experimentation, we derive general sufficient conditions for accumulating information in a near-optimal manner. We believe that practitioners can use these conditions as guidelines to design successful pricing policies in various settings. Third, we develop a unifying theme to obtain performance bounds in operations management problems with model uncertainty. We employ (i) the concept of Fisher information to derive natural lower bounds on regret, and (ii) martingale theory to analyze the estimation errors and generate well-performing policies. Finally, we analyze the pricing of multiple products with substitutable demand. Our analysis shows that multi-product pricing is not a straightforward repetition of single-product pricing. Learning in a high dimensional price space essentially requires sufficient "variation" in the directions of successive price vectors, which brings forth the idea of orthogonal pricing. In Chapter 3, we extend our analysis to the case where information can become obsolete. The particular dynamic pricing problem we consider includes a seller who tries to simultaneously learn about a time-varying demand curve, and earn sales revenues. We conduct a simulation study to evaluate the revenue performance of several pricing policies in this setting. Our results suggest that policies designed for static demand settings do not perform well in time-varying demand settings. Moreover, if the demand environment is not very noisy and the changes are not very frequent, a simple modification of the estimate-and-then-optimize approach, which is based on a moving time window, performs reasonably well in changing demand environments.

Download or read book Web and Internet Economics written by Michal Feldman and published by Springer Nature. This book was released on 2022-01-20 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th International Conference on Web and Internet Economics, WINE 2021, which was held online during December 14-17, 2021. The conference was originally planned to take place in Potsdam, Germany, but changed to a virtual event due to the COVID-19 pandemic. The 41 full papers presented in this volume were carefully reviewed and selected from 146 submissions. They were organized in topical sections as follows: mechanism design and pricing; matching, markets and equilibria; learning, fairness, privacy and behavioral models; social choice and cryptocurrencies.

Download or read book INFORMS Annual Meeting written by Institute for Operations Research and the Management Sciences. National Meeting and published by . This book was released on 2006 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Smart Grid and Enabling Technologies written by Shady S. Refaat and published by John Wiley & Sons. This book was released on 2021-07-27 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: SMART GRID AND ENABLING TECHNOLOGIES Discover foundational topics in smart grid technology as well as an exploration of the current and future state of the industry As the relationship between fossil fuel use and climate change becomes ever clearer, the search is on for reliable, renewable and less harmful sources of energy. Sometimes called the “electronet” or the “energy Internet,” smart grids promise to integrate renewable energy, information, and communication technologies with the existing electrical grid and deliver electricity more efficiently and reliably. Smart Grid and Enabling Technologies delivers a complete vision of smart grid technology and applications, including foundational and fundamental technologies, the technology that enables smart grids, the current state of the industry, and future trends in smart energy. The book offers readers thorough discussions of modern smart grid technology, including advanced metering infrastructure, net zero energy buildings, and communication, data management, and networks in smart grids. The accomplished authors also discuss critical challenges and barriers facing the smart grid industry as well as trends likely to be of importance in its future development. Readers will also benefit from the inclusion of: A thorough introduction to smart grid architecture, including traditional grids, the fundamentals of electric power, definitions and classifications of smart grids, and the components of smart grid technology An exploration of the opportunities and challenges posed by renewable energy integration Practical discussions of power electronics in the smart grid, including power electronics converters for distributed generation, flexible alternating current transmission systems, and high voltage direct current transmission systems An analysis of distributed generation Perfect for scientists, researchers, engineers, graduate students, and senior undergraduate students studying and working with electrical power systems and communication systems. Smart Grid and Enabling Technologies will also earn a place in the libraries of economists, government planners and regulators, policy makers, and energy stakeholders working in the smart grid field.

Download or read book Optimal Learning written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2013-07-09 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.

Download or read book Reinforcement Learning and Optimal Control written by Dimitri P. Bertsekas and published by . This book was released on 2020 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: