Recovery Strategy Through Decomposition-Based Optimization

Chen Peng (), Hongfeng Wang (), Yi Yang () and Yong Zhang ()
Additional contact information
Chen Peng: Shanghai University, School of Mechatronic Engineering and Automation
Hongfeng Wang: Northeastern University
Yi Yang: Shanghai University
Yong Zhang: China University of Mining and Technology, School of Information and Control Engineering

Chapter Chapter 8 in Modeling and Resilience Recovery for Disrupted Supply Chain, 2026, pp 165-188 from Springer

Abstract: Abstract This chapter addresses SC resilience under long-term disruptions caused by the COVID-19 pandemic, where concurrent supply and production disruptions exhibit time-varying capacity reductions. A modified multi-portfolio approach integrating simulation and predictions is proposed to concurrently select primary and recovery supply and production portfolios. Time-dependent mixed integer programming models incorporating preparedness and recovery measures are developed, solved via a novel prediction-based decomposition optimisation method. Computational experiments on a real-world electronics SC demonstrate the effectiveness of the proposed approach in enhancing SC resilience and viability.

Keywords: Supply chain resilience; Disruption risks; Supply chain recovery; Time-dependent mixed integer programming (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-95-4901-6_8

Ordering information: This item can be ordered from
http://www.springer.com/9789819549016

DOI: 10.1007/978-981-95-4901-6_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().