The Elements Of Joint Learning And Optimization In Operations Management

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.

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++).

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies.

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.

Systems Thinking, Third Edition combines systems theory and interactive design to provide an operational methodology for defining problems and designing solutions in an environment increasingly characterized by chaos and complexity. This new edition has been updated to include all new chapters on self-organizing systems as well as holistic, operational, and design thinking. The book covers recent crises in financial systems and job markets, the housing bubble, and environment, assessing their impact on systems thinking. A companion website is available at interactdesign.com. This volume is ideal for senior executives as well as for chief information/operating officers and other executives charged with systems management and process improvement. It may also be a helpful resource for IT/MBA students and academics. Four NEW chapters on self-organizing systems, holistic thinking, operational thinking, and design thinking Covers the recent crises in financial systems and job markets globally, the housing bubble, and the environment, assessing their impact on systems thinking Companion website to accompany the book is available at interactdesign.com

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.