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 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 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 Approximate Dynamic Programming written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2007-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

Download or read book OPTIMIZATION AND OPERATIONS RESEARCH Volume I written by Ulrich Derigs and published by EOLSS Publications. This book was released on 2009-02-09 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization and Operations Research is a component of Encyclopedia of Mathematical Sciences in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. The Theme on Optimization and Operations Research is organized into six different topics which represent the main scientific areas of the theme: 1. Fundamentals of Operations Research; 2. Advanced Deterministic Operations Research; 3. Optimization in Infinite Dimensions; 4. Game Theory; 5. Stochastic Operations Research; 6. Decision Analysis, which are then expanded into multiple subtopics, each as a chapter. These four volumes are aimed at the following five major target audiences: University and College students Educators, Professional Practitioners, Research Personnel and Policy Analysts, Managers, and Decision Makers and NGOs.

Download or read book Machine Learning Under a Modern Optimization Lens written by Dimitris Bertsimas and published by . This book was released on 2019 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Online Data Driven Learning Approaches in Operations Management Problems written by Vivek Ramamurthy and published by . This book was released on 2012 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditionally, stochastic models in operations research use specific probabilistic assumptions to model random phenomena, and determine optimal policies or decisions on this basis. Often, these probabilistic assumptions are parametric, and entail estimation of parameters using very small samples of data. Many a times, the available information is not sufficient to postulate a model with any degree of certainty. Consequently, policies based on parametric assumptions in this case, are very sensitive to the particular assumptions made. One of the goals of this thesis is therefore the development of objective, adaptive, data-driven, learning approaches to objective functions, that make as few parametric assumptions as possible, and give rise to optimal policies that perform well for small samples, without compromising large sample performance. While this clearly seems a very difficult problem, it is one that is observed in nearly every operations management problem and is certainly the right problem to pursue. In this thesis, we develop novel learning approaches to specific problems in inventory control, call center staffing and dynamic assortment optimization. We test these approaches computationally, and provide strong evidence for the adoption of our general approach in tackling model uncertainty in operations management problems.

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 Operations Planning written by Joseph Geunes and published by CRC Press. This book was released on 2014-09-18 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: A reference for those working at the interface of operations planning and optimization modeling, Operations Planning: Mixed Integer Optimization Models blends essential theory and powerful approaches to practical operations planning problems. It presents a set of classical optimization models with widespread application in operations planning. The

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 Optimization for Learning and Control written by Anders Hansson and published by John Wiley & Sons. This book was released on 2023-06-20 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters’ level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters’ students in a coherent way. The focus is on basic algorithmic principles and trade-offs. Optimization for Learning and Control covers sample topics such as: Optimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization. First-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems. Stochastic optimization methods, augmented Lagrangian methods, interior-point methods, and conic optimization methods. Dynamic programming for solving optimal control problems and its generalization to reinforcement learning. How optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines. How calculus of variations is used in optimal control and for deriving the family of exponential distributions. Optimization for Learning and Control is an ideal resource on the subject for scientists and engineers learning about which optimization methods are useful for learning and control problems; the text will also appeal to industry professionals using machine learning for different practical applications.

Download or read book Operations Management a Quantitative Approach written by P. B. Mahapatra and published by PHI Learning Pvt. Ltd.. This book was released on 2010 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is specially designed to suit the latest syllabi of courses on Production/Operations Management offered by various universities to the undergraduate students of Mechanical Engineering, Production Engineering and Industrial Engineering as well as students of Master of Business Administration (MBA) specializing in Production and Operations Management stream. The book offers a balanced coverage of the fundamental principles of managing operations and the quantitative techniques used to support the functions of operations management. There are many worked-out examples in each chapter to enable students to comprehend the quantitative material of the book. The text is divided into two parts. Techniques of operations research such as linear programming, transportation assignment models, dynamic optimization and waiting line models are discussed in Part I. Some generic classes with functions for array and matrix manipulation, analysis of queuing models and evaluation of probability for some standard distributions have been defined and used throughout for writing programs for diverse managerial applications. Part II is devoted to a detailed discussion of management functions such as Product Design and Development, Forecasting, Capacity Analysis, Plant Layout, Assembly Line Balancing, Inventory Control, Materials Requirement Planning, Production Scheduling, Quality Control, Total Quality Management, Just in Time (JIT), Supply Chain Management, Maintenance Management and Six Sigma. Small computer programs have been given wherever required for solving practical problems. The functions developed in generic base classes have been used to take advantage of source code reusability offered by Object Oriented Programming (C++).

Download or read book Learning and Intelligent Optimization written by Clarisse Dhaenens and published by Springer. This book was released on 2015-06-18 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Learning and Optimization, LION 9, which was held in Lille, France, in January 2015. The 31 contributions presented were carefully reviewed and selected for inclusion in this book. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to algorithm selection and configuration, learning, fitness landscape, applications, dynamic optimization, multi-objective, max-clique problems, bayesian optimization and global optimization, data mining and - in a special session - also on dynamic optimization.

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 Anticipatory Optimization for Dynamic Decision Making written by Stephan Meisel and published by Springer Science & Business Media. This book was released on 2011-06-23 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of today’s online information systems rapidly increases the relevance of dynamic decision making within a large number of operational contexts. Whenever a sequence of interdependent decisions occurs, making a single decision raises the need for anticipation of its future impact on the entire decision process. Anticipatory support is needed for a broad variety of dynamic and stochastic decision problems from different operational contexts such as finance, energy management, manufacturing and transportation. Example problems include asset allocation, feed-in of electricity produced by wind power as well as scheduling and routing. All these problems entail a sequence of decisions contributing to an overall goal and taking place in the course of a certain period of time. Each of the decisions is derived by solution of an optimization problem. As a consequence a stochastic and dynamic decision problem resolves into a series of optimization problems to be formulated and solved by anticipation of the remaining decision process. However, actually solving a dynamic decision problem by means of approximate dynamic programming still is a major scientific challenge. Most of the work done so far is devoted to problems allowing for formulation of the underlying optimization problems as linear programs. Problem domains like scheduling and routing, where linear programming typically does not produce a significant benefit for problem solving, have not been considered so far. Therefore, the industry demand for dynamic scheduling and routing is still predominantly satisfied by purely heuristic approaches to anticipatory decision making. Although this may work well for certain dynamic decision problems, these approaches lack transferability of findings to other, related problems. This book has serves two major purposes: ‐ It provides a comprehensive and unique view of anticipatory optimization for dynamic decision making. It fully integrates Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on approximate dynamic programming. Moreover, the book identifies different degrees of anticipation, enabling an assessment of specific approaches to dynamic decision making. ‐ It shows for the first time how to successfully solve a dynamic vehicle routing problem by approximate dynamic programming. It elaborates on every building block required for this kind of approach to dynamic vehicle routing. Thereby the book has a pioneering character and is intended to provide a footing for the dynamic vehicle routing community.

Download or read book Advances in Stochastic Dynamic Programming for Operations Management written by Frank Schneider and published by Logos Verlag Berlin GmbH. This book was released on 2014 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many tasks in operations management require the solution of complex optimization problems. Problems in which decisions are taken sequentially over time can be modeled and solved by dynamic programming. Real-world dynamic programming problems, however, exhibit complexity that cannot be handled by conventional solution techniques. This complexity may stem from large state and solution spaces, huge sets of possible actions, non-convexities in the objective function, and uncertainty. In this book, three highly complex real-world problems from the domain of operations management are modeled and solved by newly developed solution techniques based on stochastic dynamic programming. First, the problem of optimally scheduling participating demand units in an energy transmission network is considered. These units are scheduled such that total cost of supplying demand for electric energy is minimized under uncertainty in demand and generation. Second, the integrated problem of investment in and optimal operations of a network of battery swap stations under uncertain demand and energy prices is modeled and solved. Third, the inventory control problem of a multi-channel retailer selling through independent sales channels is modeled and optimality conditions for replenishment policies of simple structure are proven. This book introduces efficient approximation techniques based on approximate dynamic programming (ADP) and extends existing proximal point algorithms to the stochastic case. The methods are applicable to a wide variety of dynamic programming problems of high dimension.

Download or read book Supply Chain Optimization Management and Integration Emerging Applications written by Wang, John and published by IGI Global. This book was released on 2010-11-30 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our rapidly changing world has forced business practitioners, in corporation with academic researchers, to respond quickly and develop effective solution methodologies and techniques to handle new challenges in supply chain systems. Supply Chain Optimization, Management and Integration: Emerging Applications presents readers with a rich collection of ideas from researchers who are bridging the gap between the latest in information technology and supply chain management. This book includes theoretical, analytical, and empirical research, comprehensive reviews of relevant research, and case studies of effective applications in the field of SCM. The use of new technologies, methods, and techniques are emphasized by those who have worked with supply chain management across the world for those in the field of information systems.