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Stochastic programming in supply chain risk managementresilience, viability, and cybersecurity /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic programming in supply chain risk managementby Tadeusz Sawik.
Reminder of title:	resilience, viability, and cybersecurity /
Author:	Sawik, Tadeusz.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xxix, 350 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Supply chain management.
Online resource:	https://doi.org/10.1007/978-3-031-57927-1
ISBN:	9783031579271$q(electronic bk.)

Stochastic programming in supply chain risk managementresilience, viability, and cybersecurity /
Sawik, Tadeusz.

Stochastic programming in supply chain risk managementresilience, viability, and cybersecurity /[electronic resource] :by Tadeusz Sawik. - Cham :Springer Nature Switzerland :2024. - xxix, 350 p. :ill. (chiefly col.), digital ;24 cm. - International series in operations research & management science,v. 3592214-7934 ;. - International series in operations research & management science ;v. 140..

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.

ISBN: 9783031579271$q(electronic bk.)

Standard No.: 10.1007/978-3-031-57927-1doiSubjects--Topical Terms:

969063
Supply chain management.

LC Class. No.: HD38.6

Dewey Class. No.: 658.500285

Stochastic programming in supply chain risk managementresilience, viability, and cybersecurity /
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505 0 $a 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.
520 $a 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.
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