Author: K. GaneshPublisher:
ISBN:
Category :
Languages : en
Pages : 128
Book Description
Data Analytics Techniques and Heuristics for Complex Supply Chain Network Problems
Author: K. GaneshSpecial Issue: Data Analytics Techniques and Heuristics for Complex Supply Chain Network Problems
Author: K. GaneshAdvances in Computational Logistics and Supply Chain Analytics
Author: Ibraheem AlharbiPublisher: Springer Nature
ISBN: 3031500369
Category :
Languages : en
Pages : 205
Book Description
Computational Intelligence in Logistics and Supply Chain Management
Author: Thomas HannePublisher: Springer
ISBN: 3319407228
Category : Business & Economics
Languages : en
Pages : 190
Book Description
This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving them. The first two chapters provide general introductions to logistics and supply chain management on the one hand, and to computational intelligence on the other hand. The subsequent chapters cover specific fields in logistics and supply chain management, work out the most relevant problems found in those fields, and discuss approaches for solving them. Chapter 3 discusses problems in the field of production and inventory management. Chapter 4 considers planning activities on a finer level of granularity which is usually denoted as scheduling. In chapter 5 problems in transportation planning such as different types of vehicle routing problems are considered. While chapters 3 to 5 rather discuss planning problems which appear on an operative level, chapter 6 discusses the strategic problem of designing a supply chain or network. The final chapter provides an overview of academic and commercial software and information systems for the discussed applications. There appears to be a gap between general textbooks on logistics and supply chain management and more specialized literature dealing with methods for computational intelligence, operations research, etc., for solving the complex operational problems in these fields. For readers, it is often difficult to proceed from introductory texts on logistics and supply chain management to the sophisticated literature which deals with the usage of advanced methods. This book fills this gap by providing state-of-the-art descriptions of the corresponding problems and suitable methods for solving them.
Big Data Analytics in Supply Chain Management
Author: Iman RahimiPublisher: CRC Press
ISBN: 1000326934
Category : Computers
Languages : en
Pages : 209
Book Description
In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.
Supply Chain Analytics
Author: Kurt Y. LiuPublisher: Springer Nature
ISBN: 3030922243
Category : Business & Economics
Languages : en
Pages : 388
Book Description
This innovative new core textbook, written by an experienced professor and practitioner in supply chain management, offers a business-focused overview of the applications of data analytics and machine learning to supply chain management. Accessible yet rigorous, this text introduces students to the relevant concepts and techniques needed for data analysis and decision making in modern supply chains and enables them to develop proficiency in a popular and powerful programming software. Suitable for use on upper-level undergraduate, postgraduate and MBA courses in supply chain management, it covers all of the major supply chain processes, including managing supply and demand, warehousing and inventory control, transportation and route optimization. Each chapter comes with practical real-world examples drawn from a range of business contexts, including Amazon and Starbucks, case study discussion questions, computer-assisted exercises and programming projects.
Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Author: Thomas, J. JoshuaPublisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355
Book Description
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Supply Chain Analytics
Author: Hayden Van Der PostPublisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Reactive Publishing Own your future with Supply Chain Analytics Dive into the world of data-driven transformation with "Supply Chain Analytics," the essential guide for professionals seeking to revolutionize their supply chain processes using the power of Python. This cutting-edge book is meticulously crafted for supply chain managers, analysts, consultants, and IT professionals who are eager to elevate their skills and stay ahead in the rapidly evolving field of supply chain management. Main Points: 1. **Comprehensive Content: ** From basic concepts to advanced analytical techniques, this book provides a thorough grounding in supply chain theory paired with practical Python applications. It serves as both a reference and a how-to guide for analyzing and improving supply chain functions. 2. **Real-World Applications: ** Illustrated with case studies and real-world examples, "Supply Chain Analytics" shows how Python can solve complex supply chain problems. Readers will learn to develop powerful models that can lead to substantial cost reductions and efficiency improvements. 3. **Hands-On Learning: ** Engage in practical exercises and interactive content that encourage hands-on learning. Each chapter includes step-by-step tutorials that guide readers through the intricacies of implementing Python programming to solve actual supply chain challenges. 4. **Latest Trends and Techniques: ** Stay current with the most recent advancements in supply chain analytics. The book covers emerging trends and how Python is used to leverage big data, predictive analytics, AI, and machine learning to optimize supply chain operations. 5. **Expert Knowledge: ** Authored by experienced professionals in both Python programming and supply chain management, the book offers expert insights that combine theoretical knowledge with practical expertise. 6. **Skill Enhancement: ** Readers will enhance their technical proficiency in Python while simultaneously gaining a deep understanding of supply chain analytics, making them invaluable assets to any organization. 7. **Network of Professionals: ** Gain access to a community of like-minded individuals and professionals who are focused on harnessing analytics to transform the supply chain industry. Target Audience: - **Supply Chain Professionals: ** Managers and executives aiming to integrate more data-driven approaches into their operations. - **Data Analysts and Scientists: ** Those who want to specialize in supply chain analytics and apply their technical skills in Python within this domain. - **IT Professionals in Supply Chain: ** IT experts who need to understand the analytic requirements of supply chains to implement effective solutions. - **Academics and Students: ** Lecturers and students in supply chain management, operations research, or data science disciplines who require a practical and current understanding of how Python can be applied in supply chains. - **Consultants and Strategists: ** Industry consultants seeking to offer the latest analytical techniques to advise firms on supply chain optimization. Embrace the power of Python for a transformative journey through the supply chain universe with "Supply Chain Analytics," and emerge as a forward-thinking, data-driven supply chain professional poised to make an impactful change. Unlock the potential of Python in supply chain-your journey to becoming a data-savvy supply chain leader starts here.
Heuristics in Analytics
Author: Carlos Andre Reis PinheiroPublisher: John Wiley & Sons
ISBN: 1118347609
Category : Business & Economics
Languages : en
Pages : 256
Book Description
Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.
Sustainable Logistics Systems Using AI-based Meta-Heuristics Approaches
Author: Jiuping XuPublisher: Taylor & Francis
ISBN: 1003830692
Category : Business & Economics
Languages : en
Pages : 186
Book Description
This book introduces and analyses recent trends and studies of sustainable logistics systems using AI-based meta-heuristics approaches, including AI-based meta-heuristics applied to supply chain network models, integrated multi-criteria decision-making approaches for green supply chain management, uncertain supply chain models etc. It emphasizes both theory and practice, providing methodological and theoretical basis as well as case references for sustainable logistics systems using AI based meta-heuristics. Most of multi-national enterprises today face the challenge of sustainable development for their logistics systems trying to meet or exceed customer expectations. Sustainable development attracts both researchers and industrial practitioners who are focused on the design and implementation of logistics system. AI-based meta-heuristics approaches has emerged as a capable method for quickly providing optimal or near-optimal solutions for the problems that exact optimization cannot solve. Recent advances in various AI-based meta-heuristics approaches can resolve various and complex logistics and supply chain problem types. This book mainly encompasses the most popular and frequently employed AI-based meta-heuristics approaches such as genetic algorithm, variable neighborhood search, multi-objective heuristic search and the hybrid of these approaches. The chapters in this book were originally published in the International Journal of Management Science and Engineering Management.