Download or read book Online Learning and Optimization in Operations Management written by Rui Sun (Ph. D.) and published by . This book was released on 2020 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study in this thesis online learning and optimization problems in operations management where we need to make decisions in the face of incomplete information and operational constraints in a dynamic environment. We first consider an online matching problem where a central platform needs to match a number of limited resources to different groups of users that arrive sequentially over time. The platform does not know the reward of each matching option and must learn the true rewards from the matching results. We formulate the problem as a Markovian multi-armed bandit with budget constraints, and propose an innovative algorithm that is based on assembling the policies for each single arm. We prove the algorithm’s worst-case performance guarantee, and numerically show the algorithm’s robust performance compared to alternative heuristics. We next consider a revenue management problem with add-on discounts where a retailer offers discounts on selected supportive products (e.g. video games) to customers who have also purchased the core products (e.g. video game consoles). When the products’ demand functions are unknown, we propose a UCB-based learning algorithm that uses the an FPTAS optimization algorithm as a subroutine to determine the prices of different types of products. We show that the algorithm can converge to the optimal full-information pricing policy. We also conduct numerical experiments with real-world data to illustrate the performance of our algorithm and the advantage of using the add-on discount strategy in practice. We last consider a network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory. The retailer does not know the demand of different products, and must learn demand from the sales data. To optimize the pricing decisions, we propose an efficient algorithm that combines the Thompson sampling technique and the online gradient descent method with a primal-dual framework. In comparison to traditional algorithms that are based on frequently solving linear programs, our algorithm does not need to solve any linear program, and therefore, has the advantage in computational efficiency. We analyze the performance guarantee of our algorithm, and show the algorithm’s fast running time through numerical experiments.
Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Download or read book Dynamic Learning and Optimization for Operations Management Problems written by He Wang (Ph. D.) and published by . This book was released on 2016 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advances in information technology and the increased availability of data, new approaches that integrate learning and decision making have emerged in operations management. The learning-and-optimizing approaches can be used when the decision maker is faced with incomplete information in a dynamic environment. We first consider a network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory constraints. The retailer does not know the exact demand distribution at each price and must learn the distribution from sales data. We propose a dynamic learning and pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for similar settings. We next consider a dynamic pricing problem for a single product where the demand curve is not known a priori. Motivated by business constraints that prevent sellers from conducting extensive price experimentation, we assume a model where the seller is allowed to make a bounded number of price changes during the selling period. We propose a pricing policy that incurs the smallest possible regret up to a constant factor. In addition to the theoretical results, we describe an implementation at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings. Finally, we study a supply chain risk management problem. We propose a hybrid strategy that uses both process flexibility and inventory to mitigate risks. The interplay between process flexibility and inventory is modeled as a two-stage robust optimization problem: In the first stage, the firm allocates inventory, and in the second stage, after disruption strikes, the firm schedules its production using process flexibility to minimize demand shortage. By taking advantage of the structure of the second stage problem, we develop a delayed constraint generation algorithm that can efficiently solve the two-stage robust optimization problem. Our analysis of this model provides important insights regarding the impact of process flexibility on total inventory level and inventory allocation pattern.
Download or read book Stochastic Networks written by Frank Kelly and published by Cambridge University Press. This book was released on 2014-02-27 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: A compact, highly-motivated introduction to some of the stochastic models found useful in the study of communications networks.
Download or read book Optimization Models written by Giuseppe C. Calafiore and published by Cambridge University Press. This book was released on 2014-10-31 with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.
Download or read book Operations Research written by Knowledge Flow and published by Knowledge Flow. This book was released on 2016-01-19 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★★★★★LEARNING STARTS WITH VIEWING THE WORLD DIFFERENTLY.★★★★★ Knowledge flow — A mobile learning platform provides Apps and Books. Knowledge flow provides learning book of Operations Research. This book brings essential reference with detailed illustrations for operation research, whether students, teachers or professionals across the world. This book of operations research based on management and engineering operations research courses and this operation research book covers basic concepts such as optimization, game theory, networks, and transport operations. Contents: 1. Optimization 2. Game Theory 3. Queuing Systems 4. Discrete Event Simulation 5. Computer and Telecommunication Networks 6. Financial Engineering 7. Supply Chain Manage 8. Dynamic Programming and Control Problems 9. Transport Operations and Logistics 10. Service Operations Management
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 Optimization in Operations Research written by Ronald L. Rardin and published by Prentice Hall. This book was released on 2014-01-01 with total page 936 pages. Available in PDF, EPUB and Kindle. Book excerpt: For first courses in operations research, operations management Optimization in Operations Research, Second Edition covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. This dynamic text emphasizes the importance of modeling and problem formulation andhow to apply algorithms to real-world problems to arrive at optimal solutions. Use a program that presents a better teaching and learning experience-for you and your students. Prepare students for real-world problems: Students learn how to apply algorithms to problems that get them ready for their field. Use strong pedagogy tools to teach: Key concepts are easy to follow with the text's clear and continually reinforced learning path. Enjoy the text's flexibility: The text features varying amounts of coverage, so that instructors can choose how in-depth they want to go into different topics.
Download or read book Online Optimization written by Patrick Jaillet and published by Springer. This book was released on 2021-01-14 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of ONLINE OPTIMIZATION is to provide a systematic survey of the methodology. From the methodological survey, the book then covers a variety of applications of online optimization methods in the domain of Operations Research and Management Science. These applications include a range of problem types, which include the multiple scheduling complex transportation systems, optimizing financial decision problems in "real time", and complex production problems of all sorts (e.g., whether costs should be reduced or profits should be maximized or scarce resources should be used wisely, etc.). With online optimization the issue of incomplete data is an essential aspect of the scientific challenge. Hence, how well online algorithms can perform and how one can guarantee solution quality—even without knowing all data in advance—are the primary challenges of the online optimization methodology.
Download or read book Advanced Optimization and Operations Research written by Asoke Kumar Bhunia and published by Springer Nature. This book was released on 2020-01-09 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides students with fundamentals and advanced concepts in optimization and operations research. It gives an overview of the historical perspective of operations research and explains its principal characteristics, tools, and applications. The wide range of topics covered includes convex and concave functions, simplex methods, post optimality analysis of linear programming problems, constrained and unconstrained optimization, game theory, queueing theory, and related topics. The text also elaborates on project management, including the importance of critical path analysis, PERT and CPM techniques. This textbook is ideal for any discipline with one or more courses in optimization and operations research; it may also provide a solid reference for researchers and practitioners in operations research.
Download or read book Perturbations Optimization and Statistics written by Tamir Hazan and published by MIT Press. This book was released on 2017-09-22 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
Download or read book Learning and Optimization in Modern Retail written by Patricio Tomas Foncea Araneda and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The general topic of this thesis is the application of optimization and statistical inference methods to practical industry problems in the domains of supply chain, demand estimation, assortment optimization, and experimentation. We develop new methodologies that improve the practice of operations management for retailers and wholesalers, testing and implementing them in partnership with industry collaborators. We begin by tackling the problem faced by an online retailer that receives orders sequentially and must decide from which of its warehouses to ship each item in the order. Each warehouse has a limited inventory and the retailer must balance the trade-off between immediate cost of shipping with future inventory availability. We formulate it as an online optimization problem and propose a novel primal-dual algorithm with provable performance guarantees that is robust to the demand process and does not require any explicit forecast. We then turn our attention to the problem of learning customer preferences using aggregated demand from multiple products and stores. Although we rely on traditional choice model techniques, the novelty of our approach is the inclusion of a low-rank term in the utility model that aims to capture non-observable characteristics as latent features. This not only improves the overall fit of our demand estimation model, but also helps addressing endogeneity of our regressors without the need of instrumental variables. Once we have a demand model in place, we can use it to forecast customer behavior and make decisions that bring positive predicted outcomes. We develop algorithms that solve the assortment optimization problem when using complex choice models, and the problem of optimally allocating shelf space. These algorithms are efficient and scalable, and we show how our industry collaborator is currently implementing these in their business operations. Finally, we study the problem of measuring the effect of interventions in largescale retail. We design an experimentation platform for a large wholesaler that applies promotions at store level. We apply a generalized version of synthetic control to find treatment effects and show that these estimates are more reliable and accurate than the ones obtained with current methodologies used by industry practitioners.
Download or read book Online Learning and Online Convex Optimization written by Shai Shalev-Shwartz and published by Foundations & Trends. This book was released on 2012 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online Learning and Online Convex Optimization is a modern overview of online learning. Its aim is to provide the reader with a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms.
Download or read book Bite Sized Operations Management written by Mark S. Daskin and published by Springer Nature. This book was released on 2022-05-31 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is an introduction to Operations Management. Three themes are woven throughout the book: optimization or trying to do the best we can, managing tradeoffs between conflicting objectives, and dealing with uncertainty. After a brief introduction, the text reviews the fundamentals of probability including commonly used discrete and continuous distributions and functions of a random variable. The next major section, beginning in Chapter 7, examines optimization. The key fundamentals of optimization—inputs, decision variables, objective(s), and constraints—are introduced. Optimization is applied to linear regression, basic inventory modeling, and the newsvendor problem, which incorporates uncertain demand. Linear programming is then introduced. We show that the newsvendor problem can be cast as a network flow linear programming problem. Linear programming is then applied to the problem of redistributing empty rental vehicles (e.g., bicycles) at the end of a day and the problem of assigning students to seminars. Several chapters deal with location models as examples of both simple optimization problems and integer programming problems. The next major section focuses on queueing theory including single-and multi-server queues. This section also introduces a numerical method for solving for key performance metrics for a common class of queueing problems as well as simulation modeling. Finally, the text ends with a discussion of decision theory that again integrates notions of optimization, tradeoffs, and uncertainty analysis. The text is designed for anyone with a modest mathematical background. As such, it should be readily accessible to engineering students, economics, statistics, and mathematics majors, as well as many business students.
Download or read book Simulation Based Optimization written by Abhijit Gosavi and published by Springer. This book was released on 2014-10-30 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Download or read book Data Mining written by and published by BoD – Books on Demand. This book was released on 2022-03-30 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining.
Download or read book Mathematical Optimization Theory and Operations Research written by Yury Kochetov and published by Springer Nature. This book was released on 2020-09-13 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes refereed proceedings of the 19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020, held in Novosibirsk, Russia, in July 2020. Due to the COVID-19 pandemic the conference was held online. The 25 full papers and 8 short papers presented in this volume were carefully reviewed and selected from a total of 102 submissions. The papers in the volume are organised according to the following topical headings: combinatorial optimization; mathematical programming; global optimization; game theory and mathematical economics; heuristics and metaheuristics; machine learning and data analysis.
