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| assortment optimization machine learning: The Elements of Joint Learning and Optimization in Operations Management Xi Chen, Stefanus Jasin, Cong Shi, 2022-09-20 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. |
| assortment optimization machine learning: Introduction to Algorithmic Marketing Ilya Katsov, 2017-12 A comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. The book covers the main areas of marketing that require programmatic micro-decisioning - targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization. |
| assortment optimization machine learning: The New Science of Retailing Marshall Fisher, Ananth Raman, 2010-06-22 Retailers today are drowning in data but lacking in insight. They have so much information at their disposal that they struggle with both how to sort through it, and how to add science to their decision-making process without blunting the art that they correctly believe is a key ingredient of their success. This book reveals how retailers can use data to manage everything from strategic assortment planning, inventory management, and markdowns to improve store-level execution. This data-driven approach to the retail supply chain leads to far greater and faster inventory turns, far fewer and lower discounted goods and services, and better profit margins. The authors also tease out the personnel issues and the organizational implications of this approach. |
| assortment optimization machine learning: Research Handbook on Inventory Management Jing-Sheng J. Song, 2023-08-14 This comprehensive Handbook provides an overview of state-of-the-art research on quantitative models for inventory management. Despite over half a century’s progress, inventory management remains a challenge, as evidenced by the recent Covid-19 pandemic. With an expanse of world-renowned inventory scholars from major international research universities, this Handbook explores key areas including mathematical modelling, the interplay of inventory decisions and other business decisions and the unique challenges posed to multiple industries. |
| assortment optimization machine learning: Combinatorial Optimization and Applications Ding-Zhu Du, Donglei Du, Chenchen Wu, Dachuan Xu, 2021-12-10 This book constitutes the refereed proceedings of the 15th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2021, which took place in Tianjin, China, during December 17-19, 2021. The 55 papers presented in this volume were carefully reviewed and selected from 122 submissions. They deal with combinatorial optimization and its applications in general, focusing on algorithms design, theoretical and experimental analysis, and applied research of general algorithmic interest. |
| assortment optimization machine learning: Essential PySpark for Scalable Data Analytics Sreeram Nudurupati, 2021-10-29 Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book. |
| assortment optimization machine learning: Artificial Intelligence and Machine Learning in the Travel Industry Ben Vinod, 2023-05-26 Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice. |
| assortment optimization machine learning: Computational Intelligence in Machine Learning Vinit Kumar Gunjan, |
| assortment optimization machine learning: All Hands on Tech Thomas H. Davenport, Ian Barkin, 2024-09-18 Supercharge your organization's capacity for innovation The greatest untapped asset in an enterprise today is the ingenuity of its people. Dive into a future of work where technology empowers everyone to be a creator and builder with All Hands on Tech: The Citizen Revolution in Business Technology. This pivotal book offers a comprehensive look into the role of citizen developers—business domain experts who are driving IT-enabled innovation using technology previously reserved for professional technologists. Through case studies of citizens and citizen-enabled enterprises, the authors demonstrate how emerging technology bestows unprecedented power on these individuals and unprecedented value on the organizations that channel their efforts. They outline a transformative approach to citizen development that not only enhances companies' innovative capacity via the empowerment of domain experts, but also minimizes risk and liberates IT departments to pursue more strategic initiatives. All Hands on Tech describes a revolution in work—powered by technology becoming more human and humans becoming more comfortable with technology. This convergence provides a clear pathway for enterprises to leverage the on-the-ground experience and insight of all employees. The authors provide diverse examples of companies that have aligned the work of their citizen developers with wider organizational goals across citizen data science, automation, and development projects. These examples demonstrate why and how to commit to the citizen revolution in your organization. In the book, you'll: Discover the untapped potential of citizen developers to revolutionize business operations with technology democratization Find a practical framework for integrating citizen development into a broader corporate digital and data strategy, while controlling risk Explore a forward-thinking approach to redefining the roles of all hands in an enterprise, empowering them to turn ideas into applications, automations, and analytical/AI models For business leaders, executives, managers, and IT professionals looking to harness the full potential of their front-line employees and redefine the landscape of IT work, All Hands on Tech is a must-have resource. For business domain specialists and those eager to turn ideas into action, the citizen revolution democratizes information technology and empowers you to lead your organization towards a more innovative and efficient future. For subject matter experts, domain specialists, and those eager to put their ideas to work while also future-proofing their careers with invaluable skills, the citizen revolution ushers in an entirely new way of working. |
| assortment optimization machine learning: Planning and Reporting in BI-supported Controlling Dietmar Schön, 2023-07-24 Planning and reporting solutions in many companies still suffer from poor data quality, are insufficiently integrated and are often time and cost intensive. This practice-oriented book shows step by step how things can be done differently. It systematically shows how modern planning and reporting systems in BI-supported controlling can be set up with the use of data warehouse and big data technology and usefully supplemented with AI-supported features. For the 4th edition, the book has been comprehensively updated. The extensive controlling cockpit example has been expanded. It now contains suggestions for the areas of corporate management (operational and strategic controlling), sales, production, purchasing and project management. In addition, the latest developments in BI-supported controlling with the support of traditional and explorative BI are highlighted, including data mining, predictive analytics, artificial intelligence, RPA, chatbots, data discovery, data visualization, app technology, self-service BI and cloud computing. Further innovations concern the topics of data quality and data modeling. The final chapter is Mobile BI, which deals with the expansion of powerful mobile analysis and planning solutions with the help of tablets, mobile phones and other mobile devices. |
| assortment optimization machine learning: Essentials of Business Analytics Bhimasankaram Pochiraju, Sridhar Seshadri, 2019-07-10 This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters. The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text. Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business solution. The companion website provides examples, data sets and sample code for each chapter. |
| assortment optimization machine learning: Applied Analytics through Case Studies Using SAS and R Deepti Gupta, 2018-08-03 Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling. |
| assortment optimization machine learning: Handbook on Augmenting Telehealth Services Sonali Vyas, Sunil Gupta, Monit Kapoor, Samiya Khan, 2024-01-26 Handbook on Augmenting Telehealth Services: Using Artificial Intelligence provides knowledge of AI-empowered telehealth systems for efficient healthcare services. The handbook discusses novel innovations in telehealth using AI techniques and also focuses on emerging tools and techniques in smart health systems. The book highlights important topics such as remote diagnosis of patients and presents e-health data management showcasing smart methods that can be used to improvise healthcare support and services. The handbook also shines a light on future trends in AI-enabled telehealth systems. Features Provides knowledge of AI-empowered telehealth systems for efficient healthcare services Discusses novel innovations in telehealth using AI techniques Covers emerging tools and techniques in smart health systems Highlights remote diagnosis of patients Focuses on e-health data management and showcases smart methods used to improvise healthcare support and services Shines a light on future trends in AI-enabled telehealth systems Every individual (patients, doctors, healthcare staff, etc.) is currently getting adapted to this new evolution of healthcare. This handbook is a must-read for students, researchers, academicians, and industry professionals working in the field of artificial intelligence and its uses in the healthcare sector. |
| assortment optimization machine learning: Technology Optimization and Change Management for Successful Digital Supply Chains Sabri, Ehap, 2019-03-01 Companies across different industries are launching technology-enabled (digital) business transformation programs to improve their strategic, tactical, and operational supply chain processes. The greatest challenges that they are facing include the lack of preparation and knowledge of the digital transformation life cycle and poorly addressing or neglecting the “people-related” aspects of them. Therefore, improvement initiatives have been short-lived or incomplete, and expected business benefits have not been achieved or materialized. Technology Optimization and Change Management for Successful Digital Supply Chains is a pivotal reference source that provides vital research on the application of digital business transformation programs to improve strategic, tactical, and operational supply chain processes. While highlighting topics such as maturity models, predictive analysis, and communication planning, this publication explores the limited literature in the field of digital supply chain optimization and business transformation, and complements it with practical and proven tactics from the industry. This book is ideally designed for program managers, engineers, students, and practitioners seeking current research on the field’s latest best practices on digital supply chain enablement. |
| assortment optimization machine learning: Basic Concept of Merchandise Mrs. S. Nazira Begum, Dr. A. Vennila, Mrs. M. Jayanthi, 2024-02-29 Mrs. S. Nazira Begum, Assistant Professor, Department of Commerce PA, KG College of Arts & Science, Coimbatore,Tamil Nadu, India. Dr. A. Vennila, Assistant Professor, Department of Commerce PA, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India. Mrs. M. Jayanthi, Assistant Professor, Department of Commerce PA, KG College of Arts and Science, Coimbatore, Tamil Nadu, India. |
| assortment optimization machine learning: Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) Mostafa Ezziyyani, 2019-03-06 This book includes the outcomes of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD-2018), held in Tangier, Morocco on July 12–14, 2018. Presenting the latest research in the field of computing sciences and information technology, it discusses new challenges and provides valuable insights into the field, the goal being to stimulate debate, and to promote closer interaction and interdisciplinary collaboration between researchers and practitioners. Though chiefly intended for researchers and practitioners in advanced information technology management and networking, the book will also be of interest to those engaged in emerging fields such as data science and analytics, big data, internet of things, smart networked systems, artificial intelligence, expert systems and cloud computing. |
| assortment optimization machine learning: Wearable and Wireless Systems for Healthcare II Robert LeMoyne, Timothy Mastroianni, Donald Whiting, Nestor Tomycz, 2019-02-20 This book provides a far-sighted perspective on the role of wearable and wireless systems for movement disorder evaluation, such as Parkinson’s disease and Essential tremor. These observations are brought together in the application of quantified feedback for deep brain stimulation systems using the wireless accelerometer and gyroscope of a smartphone to determine tuning efficacy. The perspective of the book ranges from the pioneering application of these devices, such as the smartphone, for quantifying Parkinson’s disease and Essential tremor characteristics, to the current state of the art. Dr. LeMoyne has published multiple first-of-their-kind applications using smartphones to quantify movement disorder, with associated extrapolation to portable media devices. |
| assortment optimization machine learning: Algorithmic Game Theory Guido Schäfer, |
| assortment optimization machine learning: Web and Big Data. APWeb-WAIM 2021 International Workshops Yunjun Gao, An Liu, Xiaohui Tao, Junying Chen, 2021-12-03 This book constitutes revised selected papers from the workshops of the 5th Asia-Pacific Web and Web-Age Information Management International Joint Conference on Web and Big Data, APWeb-WAIM 2021: The Fourth International Workshop on Knowledge Graph Management and Applications, KGMA 2021, The Third International Workshop on Semi-structured Big Data Management and Application, SemiBDMA 2021, and The Second International Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2021, held in Guangzhou, China, in August 2021. Due to the COVID-19 pandemic the conference was held online. The 11 papers were thoroughly reviewed and selected from the 28 submissions and present recent research on the theory, design, and implementation of data management systems. |
| assortment optimization machine learning: Metaheuristic and Machine Learning Optimization Strategies for Complex Systems R., Thanigaivelan, M., Suchithra, S., Kaliappan, Mothilal, T., 2024-07-17 In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more. |
| assortment optimization machine learning: Quantum Machine Learning S Karthikeyan, M Akila, D. Sumathi, T Poongodi, 2024-10-28 This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies. In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering. |
| assortment optimization machine learning: Innovative Technology at the Interface of Finance and Operations Volodymyr Babich, John R. Birge, Gilles Hilary, 2022-01-01 This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study. |
| assortment optimization machine learning: Computational Science – ICCS 2021 Maciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot, 2021-06-11 The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Effective Solution of Ill-posed Inverse Problems with Stabilized Forward Solver” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| assortment optimization machine learning: Predictive Analytics Dursun Delen, 2020-12-15 Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen's holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies—including lessons from failed projects. It's all designed to help you gain a practical understanding you can apply for profit. * Leverage knowledge extracted via data mining to make smarter decisions * Use standardized processes and workflows to make more trustworthy predictions * Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting) * Understand predictive algorithms drawn from traditional statistics and advanced machine learning * Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection |
| assortment optimization machine learning: Combinatorial Optimization and Applications Weili Wu, Jianxiong Guo, 2024-01-09 The two-volume set LNCS 14461 and LNCS 14462 constitutes the refereed proceedings of the 17th International Conference on Combinatorial Optimization and Applications, COCOA 2023, held in Hawaii, HI, USA, during December 15–17, 2023. The 73 full papers included in the proceedings were carefully reviewed and selected from 117 submissions. They were organized in topical sections as follows: Part I: Optimization in graphs; scheduling; set-related optimization; applied optimization and algorithm; Graph planer and others; Part II: Modeling and algorithms; complexity and approximation; combinatorics and computing; optimization and algorithms; extreme graph and others; machine learning, blockchain and others. |
| assortment optimization machine learning: Applications of Computational Intelligence in Multi-Disciplinary Research Ahmed A. Elngar, Rajdeep Chowdhury, Mohamed Elhoseny, Valentina Emilia Balas, 2022-02-14 Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of computational intelligence to a wide spectrum of research cases. The book covers the main approaches used in computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing. - Provides an overview of the key methods of computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods - Includes case studies and real-world examples of computational intelligence applied in a variety of research topics, including bioinformatics, biomedical engineering, big data analytics, information security, signal processing, machine learning, nanotechnology, and optimization techniques - Presents a thorough technical explanation on how computational intelligence is applied that is suitable for a wide range of multidisciplinary and interdisciplinary research |
| assortment optimization machine learning: Data Analytics Dr. Hariharan R, Dr. Sudha E, Dr Vedapradha R, |
| assortment optimization machine learning: Mobile Ad Hoc Networks Jonathan Loo, Jaime Lloret Mauri, Jesús Hamilton Ortiz, 2016-04-19 Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, this book examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Leading researchers, industry professionals, and academics provide an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. |
| assortment optimization machine learning: AI Algorithms and ChatGPT for Student Engagement in Online Learning Bansal, Rohit, Chakir, Aziza, Hafaz Ngah, Abdul, Rabby, Fazla, Jain, Ajay, 2024-05-28 The shift to virtual education has presented numerous challenges, including maintaining student focus and participation. Traditional methods of instruction often need to catch up in capturing the attention of digital learners, leading to disengagement and reduced learning outcomes. However, there is a solution at hand. AI Algorithms and ChatGPT for Student Engagement in Online Learning offers a comprehensive approach to leveraging artificial intelligence (AI) algorithms and ChatGPT to enhance student engagement in digital classrooms. This book addresses the pressing need for innovative strategies to keep students actively involved in their online learning journey. By harnessing the power of AI algorithms, educators can personalize learning paths to suit individual student needs, ensuring that content is relevant and engaging. Additionally, ChatGPT serves as a virtual assistant, providing students with instant feedback and support, fostering a sense of connection to the learning process. |
| assortment optimization machine learning: Revenue Management and Pricing Analytics Guillermo Gallego, Huseyin Topaloglu, 2019-08-14 “There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it. Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK. |
| assortment optimization machine learning: Digital Era and Fuzzy Applications in Management and Economy Martha del Pilar Rodríguez García, Klender Aimer Cortez Alejandro, José M. Merigó, Antonio Terceño-Gómez, Maria Teresa Sorrosal Forradellas, Janusz Kacprzyk, 2022-03-31 This book aims to contribute to the discussion about the implications of fuzzy logic, neural networks, digital era, and other intelligent techniques on organizations. This book will be very useful for academic researchers and postgraduate students aiming to introduce themselves to the field of quantitative techniques for overcoming uncertain environments and developing models to make decisions. Developments in other theories and socioeconomic and computational changes have shed light on the importance of fuzzy applications in social sciences. The treatment of uncertainty in the economic and business analysis is fundamental and requires instruments compatible with the uncertain environment of economics and business, because most of the traditional models have been overtaken by this reality when trying to make decisions with uncertain information. In the face of information technology, digitization, and uncertainty, organizations confront new opportunities and challenges. In order to take advantage of these opportunities and overcome current and future challenges, it is needed to understand the evolution of these phenomenon. |
| assortment optimization machine learning: Production Factor Mathematics Martin Grötschel, Klaus Lucas, Volker Mehrmann, 2010-08-05 Mathematics as a production factor or driving force for innovation? Those, who want to know and understand why mathematics is deeply involved in the design of products, the layout of production processes and supply chains will find this book an indispensable and rich source. Describing the interplay between mathematical and engineering sciences the book focusses on questions like How can mathematics improve to the improvement of technological processes and products? What is happening already? Where are the deficits? What can we expect for the future? 19 articles written by mixed teams of authors of engineering, industry and mathematics offer a fascinating insight of the interaction between mathematics and engineering. |
| assortment optimization machine learning: Operations in an Omnichannel World Santiago Gallino, Antonio Moreno, 2019-10-15 The world of retailing has changed dramatically in the past decade. Sales originating at online channels have been steadily increasing, and even for sales transacted at brick-and-mortar channels, a much larger fraction of sales is affected by online channels in different touch points during the customer journey. Shopper behavior and expectations have been evolving along with the growth of digital channels, challenging retailers to redesign their fulfillment and execution processes, to better serve their customers. This edited book examines the challenges and opportunities arising from the shift towards omni- channel retail. We examine these issues through the lenses of operations management, emphasizing the supply chain transformations associated with fulfilling an omni-channel demand. The book is divided into three parts. In the first part, “Omni-channel business models”, we present four studies that explore how retailers are adjusting their fundamental business models to the new omni-channel landscape. The second part, “Data-driven decisions in an omni-channel world”, includes five chapters that study the evolving data opportunities enabled by omni-channel retail and present specific examples of data-driven analyses. Finally, in the third part, “Case studies in Omni-channel retailing”, we include four studies that provide a deep dive into how specific industries, companies and markets are navigating the omni-channel world. Ultimately, this book introduces the reader to the fundamentals of operations in an omni-channel context and highlights the different innovative research ideas on the topic using a variety of methodologies. |
| assortment optimization machine learning: Learning and Decision-Making from Rank Data Lirong Xia, 2019-02-06 The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required. |
| assortment optimization machine learning: The Art and Science of Demand and Supply Chain Planning in Today's Complex Global Economy Paul Myerson, 2023-02-24 The demand and supply chain planning process for manufacturers, distributors, and retailers has evolved over the years. It has gone from a disjointed, unconnected, slow, inaccurate, fairly manual set of processes to an integrated, timely process enabled by the use and coordination of highly trained people, lean, agile processes, and cutting-edge technology. To make this set of processes work effectively, one has to fully understand and appreciate that there is an art and science aspect to the process which can take years of education and experience to fully understand. Essentially, this book will offer the reader a chance to fully understand the interconnected set of processes in a best-practice application. Furthermore, examples and cases will be used to illustrate its practical application in today’s complex global supply chain. In addition, readers will understand and be able to apply and articulate the concepts, tools, and techniques used in the efficient supply of goods and services in today’s changing global economy. It will help them to learn how businesses, through their supply chain, work both internally and with their trading partners – both upstream and downstream – to build strong relationships and integrate demand and supply planning activities across the supply chain to deliver customer value efficiently and effectively. They will learn about the tools and technologies enabling integration, and the critical drivers and key metrics of supply chain performance. |
| assortment optimization machine learning: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications Com?a, Ioan-Sorin, Trestian, Ramona, 2019-01-25 The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks. |
| assortment optimization machine learning: Metaheuristics El-Ghazali Talbi, 2009-05-27 A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics. |
| assortment optimization machine learning: Information Processing and Management of Uncertainty in Knowledge-Based Systems Davide Ciucci, Inés Couso, Jesús Medina, Dominik Ślęzak, Davide Petturiti, Bernadette Bouchon-Meunier, Ronald R. Yager, 2022-07-04 This two-volume set (CCIS 1601-1602) constitutes the proceedings of the 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2021, held in Milan, Italy, in July 2022. The 124 papers were carefully reviewed and selected from 188 submissions. The papers are organized in topical sections as follows: aggregation theory beyond the unit interval; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy mathematical analysis and its applications; generalized sets and operators; information fusion techniques based on aggregation functions, pre-aggregation functions, and their generalizations; interval uncertainty; knowledge acquisition, representation and reasoning; logical structures of opposition and logical syllogisms; mathematical fuzzy logics; theoretical and applied aspects of imprecise probabilities; data science and machine learning; decision making modeling and applications; e-health; fuzzy methods in data mining and knowledge discovery; soft computing and artificia intelligence techniques in image processing; soft methods in statistics and data analysis; uncertainty, heterogeneity, reliability and explainability in AI; weak and cautious supervised learning. |
| assortment optimization machine learning: Machine Learning: End-to-End guide for Java developers Richard M. Reese, Jennifer L. Reese, Bostjan Kaluza, Dr. Uday Kamath, Krishna Choppella, 2017-10-05 Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming About This Book Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-cases Who This Book Is For This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. What You Will Learn Understand key data analysis techniques centered around machine learning Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more In Detail Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning. The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books: Java for Data Science Machine Learning in Java Mastering Java Machine Learning On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence. Style and approach This comprehensive course proceeds from being a tutorial to a practical guide, providing an introduction to machine learning and different machine learning techniques, exploring machine learning with Java libraries, and demonstrating real-world machine learning use cases using the Java platform. |
| assortment optimization machine learning: Artificial Intelligence for Smart Healthcare Parul Agarwal, Kavita Khanna, Ahmed A Elngar, Ahmed J. Obaid, Zdzislaw Polkowski, 2023-06-09 This book provides information on interdependencies of medicine and telecommunications engineering and how the two must rely on each other to effectively function in this era. The book discusses new techniques for medical service improvisation such as clear-cut views on medical technologies. The authors provide chapters on communication essentiality in healthcare, processing of medical amenities using medical images, the importance of data and information technology in medicine, and machine learning and artificial intelligence in healthcare. Authors include researchers, academics, and professionals in the field. |
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