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Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.
Presents computational intelligence methods for addressing supply chain issues, emphasizing techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.
Supply Chain Optimization captures the latest results in a segment of current research activity in supply chain management. This research area focuses on applying optimization techniques to supply chain management problems. The research papers that make up the volume provide a snapshot of state-of-the-art optimization methods within the field. This book presents rigorous modelling approaches for supply chain operations problems with a goal of improving supply chain performance (or the performance of some segment thereof). It contains high-quality works from leading researchers in the field whose expertise fits within this scope. The book provides a diverse blend of research topics and novel modelling and solution approaches for difficult classes of supply chain operations, planning, and design problems.
In order to experience significant improvement in business processes, successful organizations must launch, implement, and maintain effective transformation programs. Such programs enable companies to fully maximize benefits and avoid potential failures. Optimization of Supply Chain Management in Contemporary Organizations discusses best practices and methods in transformation initiatives that improve the overall functionality and success of supply chain processes. Focusing on performance measurement, change management, and strategy development, this book is an essential reference source for executives, managers, advanced-level students, and professionals working in the field of business transformations and supply chain development.
Introduction and basic building blocks. Adding costs to two echelon supply chains. Advanced modeling and expanding to multiple echelons. How to get industrial streng results. Case study wrap up.
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.
The book provides an introduction to logistics and supply chain management and the application of evolutionary computation, focusing on specific fields related to supply chain issues, from strategic sourcing decisions, and production planning and control to inventory to logistics and its application using evolutionary / heuristics techniques. Bridging the gap between management research, decision-making and computer analysis, this interdisciplinary book features state-of-the-art descriptions of the corresponding problems and advanced methods for solving them.
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.
This volume collects recent results in supply chain optimisation. It presents new approaches and methods based on operations research, artificial intelligence and advanced computing techniques for design of production systems, supply and inventory management, production planning and scheduling, location, transportation and logistics, and simulation in supply flow optimisation. The text presents a wide spectrum of optimisation problems taking into account supply chain paradigms, which are pivotal to improving productivity.
Intended for an audience of graduate students, executive MBA students, and mid-to upper level government and corporate managers, Design, Analysis and Optimization of Supply Chains: A System Dynamic Approach examines the complexity of the types of organizations that comprise a modern supply chain, the problems that arise as a result of this complexity, and the solutions and analytical approaches available to managers that can help resolve these real world problems and dilemmas. The modern enterprise, be it a large corporation or a government agency, has two key dimensions of complexity: static and dynamic. The static complexity refers to the remarkable number of companies and agencies that enable delivery of the product or service. A static "snapshot" of this end-to-end enterprise would reveal hundreds if not thousands of companies involved in the supply network and many additional firms involved in the distribution and delivery to customers. Planning, communication, coordination and execution of this large system network is fundamentally challenging just because of the sheer size. This large, extended network represents the static complexity. The dynamic complexity arises from the difficulty of managing the performance of this extended enterprise over time. This requires having the appropriate metrics to track performance over time, the management skills to develop strategies, the ability to collect and monitor the correct data for true visibility, and the recognition and understanding of the long lags between actions and results. Design, Analysis and Optimization of Supply Chains: A System Dynamic Approach incorporates real-world examples and cases, representing actual complex enterprise systems including firms involved and with long lead times, to illustrate the multi-faceted activities occurring within a modern supply chain and the challenges they pose to managers. Simulation and optimization techniques are introduced and used to develop strategies for improved performance.