000 04275nam a22006135i 4500
001 978-3-031-57927-1
003 DE-He213
005 20251030112328.0
007 cr nn 008mamaa
008 240624s2024 sz | s |||| 0|eng d
020 _a9783031579271
_9978-3-031-57927-1
024 7 _a10.1007/978-3-031-57927-1
_2doi
050 4 _aHD38.5
072 7 _aKJMV8
_2bicssc
072 7 _aBUS087000
_2bisacsh
072 7 _aKJMV8
_2thema
100 1 _aSawik, Tadeusz.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979209
245 1 0 _aStochastic Programming in Supply Chain Risk Management
_h[electronic resource] :
_bResilience, Viability, and Cybersecurity /
_cby Tadeusz Sawik.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXIX, 350 p. 67 illus., 62 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInternational Series in Operations Research & Management Science,
_x2214-7934 ;
_v359
505 0 _aIntroduction -- 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 _aThis 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.
650 0 _aBusiness logistics.
_961225
650 0 _aOperations research.
_957943
650 0 _aStochastic models.
650 0 _aFinancial risk management.
_959046
650 0 _aData protection.
650 0 _aMathematical optimization.
_965522
650 1 4 _aSupply Chain Management.
_966371
650 2 4 _aOperations Research and Decision Theory.
_967143
650 2 4 _aStochastic Modelling.
_966662
650 2 4 _aRisk Management.
_959047
650 2 4 _aData and Information Security.
_966492
650 2 4 _aOptimization.
_965528
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031579264
776 0 8 _iPrinted edition:
_z9783031579288
776 0 8 _iPrinted edition:
_z9783031579295
830 0 _aInternational Series in Operations Research & Management Science,
_x2214-7934 ;
_v359
_969480
856 4 0 _uhttps://doi.org/10.1007/978-3-031-57927-1
912 _aZDB-2-BUM
912 _aZDB-2-SXBM
999 _c60969
_d60969