Stochastic Programming In Supply Chain Risk Management

This book offers a novel multi-portfolio approach and stochastic programming formulations for modeling and solving contemporary supply chain risk management problems. The focus of the book is on supply chain resilience under propagated disruptions, supply chain viability under severe crises, and supply chain cybersecurity under direct and indirect cyber risks. The content is illustrated with numerous computational examples, some of which are modeled on real-world supply chains subject to severe multi-regional or global crises, such as pandemics. In the computational examples, the proposed stochastic programming models are solved using an advanced algebraic modeling language AMPL and GUROBI solver. The book seamlessly continues the journey begun in the author’s previously published book “Supply Chain Disruption Management: Using Stochastic Mixed Integer Programming.” It equips readers with the knowledge, tools, and managerial insights needed to effectively model and address modern supply chain risk management challenges. As such, the book is designed for practitioners and researchers who are interested in supply chain risk management. Master’s and Ph.D. students in disciplines like supply chain management, operations research, industrial engineering, applied mathematics, and computer science will also find the book a valuable resource.

This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address riskneutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on integrated disruption mitigation and recovery decision-making and innovative, computationally efficient multi-portfolio approach to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on realworld supply chain disruption management problems, illustrate the material presented and provide managerial insights. Many propositions formulated in the book lead to a deep understanding of the properties of developed stochastic mixed integer programs and optimal solutions. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into six main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply and demand portfolios and production and inventory scheduling, Part V deals with selection of resilient supply portfolio in multitier supply chain networks; and Part VI addresses selection of cybersecurity safequards portfolio for disruption management of information flows in supply chains.

In this book, theory of large scale optimization is introduced with case studies of real-world problems and applications of structured mathematical modeling. The large scale optimization methods are represented by various theories such as Benders’ decomposition, logic-based Benders’ decomposition, Lagrangian relaxation, Dantzig –Wolfe decomposition, multi-tree decomposition, Van Roy’ cross decomposition and parallel decomposition for mathematical programs such as mixed integer nonlinear programming and stochastic programming. Case studies of large scale optimization in supply chain management, smart manufacturing, and Industry 4.0 are investigated with efficient implementation for real-time solutions. The features of case studies cover a wide range of fields including the Internet of things, advanced transportation systems, energy management, supply chain networks, service systems, operations management, risk management, and financial and sales management. Instructors, graduate students, researchers, and practitioners, would benefit from this book finding the applicability of large scale optimization in asynchronous parallel optimization, real-time distributed network, and optimizing the knowledge-based expert system for convex and non-convex problems.

This book covers central issues in mitigating supply chain risks from various perspectives. Today’s supply chains are vulnerable to disruptions that can have a significant impact on firms, business and performance. The aim of supply chain risk management is to identify the potential sources of risks and implement appropriate actions in order to mitigate supply chain disruptions. In this regard, the book presents a wealth of methods, strategies and analyses that are essential for mitigating supply chain risks. As a comprehensive collection of the latest research and cutting-edge developments in supply chain risk and its mitigation, the book is structured into four main parts, addressing supply chain risk strategies and developments; supply chain risk management review; supply chain sustainability and resilience; and supply chain analysis and risk management applications. The contributing authors are leading academic researchers and practitioners, who combine findings and research results with a practical and contemporary view on how companies can best manage supply chain risks and disruptions, as well as how to create resilient and sustainable supply chains. This book can be used as an essential resource for students and scholars who are interested in pursuing research or teaching courses on the rapidly growing field of supply chain management. It also offers an interesting and informative read for managers and practitioners who need to deepen their understanding of effective supply chain risk management.

Managing Supply Chain Risk and Vulnerability, a book that both practitioners and students can use to better understand and manage supply chain risk, presents topics on decision making related to supply chain risk. Leading academic researchers, as well as practitioners, have contributed chapters focusing on developing an overall understanding of risk and its relationship to supply chain performance; investigating the relationship between response time and disruption impact; assessing and prioritizing risks; and assessing supply chain resilience. Supply chain managers will find Managing Supply Chain Risk and Vulnerability a useful tool box for methods they can employ to better mitigate and manage supply chain risk. On the academic side, the book can be used to teach senior undergraduate students, as well as graduate-level students. Additionally, researchers may use the text as a reference in the area of supply chain risk and vulnerability.

This book offers an introduction to structural dynamics, ripple effect and resilience in supply chain disruption risk management for larger audiences. In the management section, without relying heavily on mathematical derivations, the book offers state-of-the-art concepts and methods to tackle supply chain disruption risks and designing resilient supply chains in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering background. In the technical section, the book constitutes structural dynamics control methods for supply chain management. Real-life problems are modelled and solved with the help of mathematical programming, discrete-event simulation, optimal control theory, and fuzzy logic. The book derives practical recommendations for management decision-making with disruption risk in the following areas: How to estimate the impact of possible disruptions on performance in the pro-active stage? How to generate efficient and effective stabilization and recovery policies? When does one failure trigger an adjacent set of failures? Which supply chain structures are particular sensitive to ripple effect? How to measure the disruption risks in the supply chain?

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.