04275nam a22006135i 4500 978-3-031-57927-1 DE-He213 20251030112328.0 cr nn 008mamaa 240624s2024 sz | s |||| 0|eng d 9783031579271 978-3-031-57927-1 10.1007/978-3-031-57927-1 doi HD38.5 KJMV8 bicssc BUS087000 bisacsh KJMV8 thema Sawik, Tadeusz. author. aut http://id.loc.gov/vocabulary/relators/aut 79209 Stochastic Programming in Supply Chain Risk Management [electronic resource] : Resilience, Viability, and Cybersecurity / by Tadeusz Sawik. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXIX, 350 p. 67 illus., 62 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda International Series in Operations Research & Management Science, 2214-7934 ; 359 Introduction -- Supply Chain Resilience: Supply Portfolio -- Supply Chain Resilience: Multi-Tier Supply Portfolio -- Supply Chain Resilience under Ripple Effect -- Supply Chain Viability: Risk-Neutral Decision-Making -- Supply Chain Viability: Risk-Averse Decision-Making -- Supply Chain Reshoring: Risk-Neutral vs Risk-Averse Decision-Making -- Supply Chain Cybersecurity: A Linear Optimization Model -- Supply Chain Cybersecurity: Direct and Indirect Cyber Risks -- Supply Chain Cybersecurity: Security Controls with Maximum Cybersecurity Value. 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. Business logistics. 61225 Operations research. 57943 Stochastic models. Financial risk management. 59046 Data protection. Mathematical optimization. 65522 Supply Chain Management. 66371 Operations Research and Decision Theory. 67143 Stochastic Modelling. 66662 Risk Management. 59047 Data and Information Security. 66492 Optimization. 65528 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031579264 Printed edition: 9783031579288 Printed edition: 9783031579295 International Series in Operations Research & Management Science, 2214-7934 ; 359 69480 https://doi.org/10.1007/978-3-031-57927-1 ZDB-2-BUM ZDB-2-SXBM 60969 60969