Table A1 Summary of literature...

Table A1

Summary of literature classification

No	Categories	Publications
1	Disruption management strategies employed by players in the maritime industry
1.1	Managing disruptions from the port level
a	Using mathematical models
	1. Risk of port failures using assessment framework for internal factors	Hsieh (2014), Hsieh et al. (2014)
	2. Stochastic measures of resilience for port operators	Pant et al. (2014)
	3. Causes of seaport disruption using Bayesian networks	John et al. (2016)
	4. Estimated the total economic consequences of a disruption by a modified demand- and supply-driven I-O model	Rose et al. (2018)
	5. Loss estimating framework for risk management of port stakeholders	Cao and Lam (2018, 2019)
b	Using management frameworks
	1. Management frameworks to lessen the risks, losses for port operators and related stakeholders	Lam (2012)
	2. Proposed solution frameworks according to the nature of each group of disruption’s causes	Loh and Thai (2015), Loh et al. (2017a), Lam and Su (2015)
	3. Conceptual framework for port operators to evaluate ports strategize against the risks associated with weather disruption events	Gharehgozli et al. (2017)
	4. Integrated framework for port stakeholders mapping scenario-based preferences in risk analysis	Almutairi et al. (2019)
	5. Framework analysing the eﬀects on hinterland logistics resulting from the port conﬂict	Gonzalez-Aregall and Bergqvist (2019)
	6. Trade-off space for vessel-move sequencing decisions to shift from decentralised to centralised decision-making temporarily during the disruption	Amodeo and Francis (2021)
c	Using technical support systems
	1. A DSS to analyse security risks for port operators and seafarers	John et al. (2018)
	2. Vessel tracking data	Verschuura et al. (2020)
	3. Port’s DSS together with the digital twinning-based resilience analysis	Zhou et al. (2021)
	4. A microscopic traffic simulation model (VISSIM) based hybrid multimodal	Dhanak et al. (2021)
1.2	Managing disruptions from the transport system level
a	Using mathematical models
	1. A three optimisation model to manage operations in intermodal logistics networks	Chen et al. (2016)
	2. An I-O model to measure disruptive scenarios following transport sectors impacted	Thekdi and Santos (2016)
	3. A mathematical cost model (stochastic mixed-integer program) to calculate the costs and penalties	Uddin and Huynh (2016)
	4. A network game theory to investigate the strategic investment of players	Chen et al. (2018)
	5. A multilayer network of sea routes and land routes	Alderson et al. (2020)
b	Using management frameworks
	1. A framework to address disruption vulnerability in the maritime transportation system	Berle et al. (2011)
	2. A decision process of the port operators, shippers, and carriers	Rousset and Ducruet (2020)
	3. Temporal and spatial sequences of the supply and demand shocks of COVID-19 on container ports and the container shipping industry	Notteboom et al. (2021)
c	Using technical support systems	Farhadi et al. (2016)
	1. Use nationwide automatic identification system data (AIS)
1.3	Managing disruptions from the supply chain level
a	Using mathematical models
	1. Optimising mitigation strategies using Markovian-based methodology	Gurning and Cahoon (2011), Gurning et al. (2013)
	2. Potential cost impacts of temporary port-of-entry closures	Lewis et al. (2013)
	3. Optimising maritime supply chain performance using a comprehensive analytic approach	Zavitsas et al. (2018)
	4. The effects of lateral transshipment and expedited shipping on supply chain performance using a simulation-based optimisation approach	Avci (2019)
	5. Factors causing port-centric supply chain disruptions using fuzzy comprehensive evaluation	Loh et al. (2017b)
	6. The interdependency between the port's disruption and its supply chain performance using Bayesian network	Hossain et al. (2020)
1.4	Managing disruptions from the regional level
a	Using mathematical models
	1. The loss of ports and the economy using I-O model	Rose and Wei (2013)
	2. The economic consequences of and resilience to seaport disruptions adapting TERM multi-regional CGE model	Wei et al. (2020)
	3. Economic loss estimation of the industry clusters	Zhang and Lam (2016), Lewis et al. (2013)
b	Using technical support systems
	1. SCADA infrastructures and cyber-physical systems	Kalogeraki et al. (2018)
1.5	Managing disruptions using other approaches
a	Developing a maritime disruptions database and forecast systems
	1. Database of maritime disruptions that have affected the UK from 1950 to 2014	Adam et al. (2016)
	2. Integrated tools for the safe management and risk assessment of seaports	Repetto et al. (2017)
	3. Cyclone risk map for seaports	Lam et al. (2017)
b	Managing disruptions from the safety and security approaches
	1. The enablers and performance outcomes of resilience capability	Yang and Hsu (2018)
	2. The essential causes and relevant insights for the safety in the water transport system	Wang et al. (2019)
c	Managing disruptions from the regulation approach
	1. Risks leading to port closure	Eskijian (2006)
	2. The impacts of policy interventions on industry actors’ preparedness	Kwesi-Buor et al. (2019)
2	The link between disruption management strategies employed by players in the maritime industry and their resilience performance	Loh and Thai (2016)

No	Categories	Publications
1	Disruption management strategies employed by players in the maritime industry
1.1	Managing disruptions from the port level
a	Using mathematical models
	1. Risk of port failures using assessment framework for internal factors	Hsieh (2014), Hsieh et al. (2014)
	2. Stochastic measures of resilience for port operators	Pant et al. (2014)
	3. Causes of seaport disruption using Bayesian networks	John et al. (2016)
	4. Estimated the total economic consequences of a disruption by a modified demand- and supply-driven I-O model	Rose et al. (2018)
	5. Loss estimating framework for risk management of port stakeholders	Cao and Lam (2018, 2019)
b	Using management frameworks
	1. Management frameworks to lessen the risks, losses for port operators and related stakeholders	Lam (2012)
	2. Proposed solution frameworks according to the nature of each group of disruption’s causes	Loh and Thai (2015), Loh et al. (2017a), Lam and Su (2015)
	3. Conceptual framework for port operators to evaluate ports strategize against the risks associated with weather disruption events	Gharehgozli et al. (2017)
	4. Integrated framework for port stakeholders mapping scenario-based preferences in risk analysis	Almutairi et al. (2019)
	5. Framework analysing the eﬀects on hinterland logistics resulting from the port conﬂict	Gonzalez-Aregall and Bergqvist (2019)
	6. Trade-off space for vessel-move sequencing decisions to shift from decentralised to centralised decision-making temporarily during the disruption	Amodeo and Francis (2021)
c	Using technical support systems
	1. A DSS to analyse security risks for port operators and seafarers	John et al. (2018)
	2. Vessel tracking data	Verschuura et al. (2020)
	3. Port’s DSS together with the digital twinning-based resilience analysis	Zhou et al. (2021)
	4. A microscopic traffic simulation model (VISSIM) based hybrid multimodal	Dhanak et al. (2021)
1.2	Managing disruptions from the transport system level
a	Using mathematical models
	1. A three optimisation model to manage operations in intermodal logistics networks	Chen et al. (2016)
	2. An I-O model to measure disruptive scenarios following transport sectors impacted	Thekdi and Santos (2016)
	3. A mathematical cost model (stochastic mixed-integer program) to calculate the costs and penalties	Uddin and Huynh (2016)
	4. A network game theory to investigate the strategic investment of players	Chen et al. (2018)
	5. A multilayer network of sea routes and land routes	Alderson et al. (2020)
b	Using management frameworks
	1. A framework to address disruption vulnerability in the maritime transportation system	Berle et al. (2011)
	2. A decision process of the port operators, shippers, and carriers	Rousset and Ducruet (2020)
	3. Temporal and spatial sequences of the supply and demand shocks of COVID-19 on container ports and the container shipping industry	Notteboom et al. (2021)
c	Using technical support systems	Farhadi et al. (2016)
	1. Use nationwide automatic identification system data (AIS)
1.3	Managing disruptions from the supply chain level
a	Using mathematical models
	1. Optimising mitigation strategies using Markovian-based methodology	Gurning and Cahoon (2011), Gurning et al. (2013)
	2. Potential cost impacts of temporary port-of-entry closures	Lewis et al. (2013)
	3. Optimising maritime supply chain performance using a comprehensive analytic approach	Zavitsas et al. (2018)
	4. The effects of lateral transshipment and expedited shipping on supply chain performance using a simulation-based optimisation approach	Avci (2019)
	5. Factors causing port-centric supply chain disruptions using fuzzy comprehensive evaluation	Loh et al. (2017b)
	6. The interdependency between the port's disruption and its supply chain performance using Bayesian network	Hossain et al. (2020)
1.4	Managing disruptions from the regional level
a	Using mathematical models
	1. The loss of ports and the economy using I-O model	Rose and Wei (2013)
	2. The economic consequences of and resilience to seaport disruptions adapting TERM multi-regional CGE model	Wei et al. (2020)
	3. Economic loss estimation of the industry clusters	Zhang and Lam (2016), Lewis et al. (2013)
b	Using technical support systems
	1. SCADA infrastructures and cyber-physical systems	Kalogeraki et al. (2018)
1.5	Managing disruptions using other approaches
a	Developing a maritime disruptions database and forecast systems
	1. Database of maritime disruptions that have affected the UK from 1950 to 2014	Adam et al. (2016)
	2. Integrated tools for the safe management and risk assessment of seaports	Repetto et al. (2017)
	3. Cyclone risk map for seaports	Lam et al. (2017)
b	Managing disruptions from the safety and security approaches
	1. The enablers and performance outcomes of resilience capability	Yang and Hsu (2018)
	2. The essential causes and relevant insights for the safety in the water transport system	Wang et al. (2019)
c	Managing disruptions from the regulation approach
	1. Risks leading to port closure	Eskijian (2006)
	2. The impacts of policy interventions on industry actors’ preparedness	Kwesi-Buor et al. (2019)
2	The link between disruption management strategies employed by players in the maritime industry and their resilience performance	Loh and Thai (2016)

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