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To assess which partnerships were most critical during the recovery planning process following Hurricanes Maria and Irma. We discuss the roles and impact of different types of partners, barriers and facilitators to partnerships and lessons in collaboration during the development of the economic and disaster recovery plan for Puerto Rico.

The Homeland Security Operational Analysis Center (HSOAC) was tasked with assisting the Puerto Rican government with an assessment of damages from Hurricanes Maria and Irma and the development of the Recovery Plan. During the process, a small team compiled and coded a database of meetings with non-HSOAC partners. The team was divided into sector teams that mirrored FEMA’s Recovery Support Functions. Each sector completed two surveys identifying high impact partners and their roles and contributions, as well as barriers and facilitators to partnerships.

A total of 1,382 engagements were recorded across all sectors over seven months. The most frequently identified high impact partners were federal and Puerto Rican governmental organizations partners. NGOs and nonprofits were noted as key partners in obtaining community perspective. Sector teams cited a lack of trust and difficulty identifying partners as barriers to partner engagement. Given the expedited nature of disaster response, establishing partnerships before disasters occur may help facilitate community input. Early networking, increased transparency and defining roles and responsibilities may increase trust and effectiveness among partnerships.

To our knowledge, this is one of the few studies that quantifies and illustrates the partnerships formed and their contributions during recovery planning, and lessons learned.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

Severe disruptions to maritime supply chains, including port closures, congestion and shortages in shipping capacity, have occurred during the COVID-19 pandemic. This paper’s purpose is to explore flexibility-based countermeasures that enable actors in maritime supply chains to mitigate the effects of disruptions with different characteristics.

Semi-structured interviews were conducted with shipping lines, shippers, forwarders and ports. Data on the COVID-19 pandemic's effects and countermeasures were collected and compared with data regarding the 2016–2017 Gothenburg port conflict.

Spatial, capacity, service and temporal flexibility emerged as the primary countermeasures, whilst important characteristics of disruptions were geographical spread, duration, uncertainty, criticality, the element of surprise and intensity. Spatial flexibility was exercised in both disruptions by switching to alternative ports. During the COVID-19 pandemic, ensuring capacity flexibility included first removing and then adding vessels. Shipping lines exercising service flexibility prioritised certain cargo, which made the spot market uncertain and reduced flexibility for forwarders, importers and exporters that changed carriers or traffic modes. Experience with disruptions meant less surprise and better preparation for spatial flexibility.

Understanding how actors in maritime supply chains exercise flexibility-based countermeasures amid disruptions with different characteristics can support preparedness for coming disruptions.

Comparing flexibility-based measures in a pandemic versus port conflict provides insights into the important characteristics of disruptions and the relevance of mitigation strategies. The resilience of maritime supply chains, although underexamined compared with manufacturing supply chains, is essential for maintaining global supply chain flows.

The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.

Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.

Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.

The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.

To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

This research analyzes the alignment of story framing between Samarco and news media following the dam disaster in Minas Gerais in November 2015. Drawing on framing theory as the underlying impetus, the study seeks to answer five major questions: RQ1: How did Samarco frame the mining tragedy in the aftermath of the dam collapse? RQ2: How did the news media frame the mining tragedy in the aftermath of the dam collapse? RQ3: Did the frames presented by Samarco and news media coincide? RQ4: Did the frames presented by Samarco and news media contradict? RQ5: What can be observed about the information flow and interaction between news media and the general public on social media? From a methods perspective, the study uses comparative textual analysis and NodeXL social network visualization to analyze the discourse around Samarco and information flow on social media in the aftermath of the tragedy. The results show that, while some social media content served as a forum for expressions of empathy toward survivors, social media content on Twitter mostly delivered a one-sided and positive view of the firm’s actions.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

The reality of emergency preparedness for older adults has not been quite satisfactory in many regions. This paper aimed to study how to improve emergency preparedness for older adults in the viewpoint of emergency culture.

A systematic literature review was used to contrast internationally voiced emergency preparedness (i.e. the principles of emergency preparedness) with culturally adapted emergency preparedness (e.g. values, games, arts, outdoor activities, etc.) via international organizations in both developing and developed countries. For data flow, the preferred reporting items for systematic reviews and meta-analyses was also used in a supplementary way.

An encompassing tenet states that all stakeholders need to divert the internationally voiced emergency preparedness into culturally adapted emergency preparedness while addressing the effects of culture, self-preparedness, integration, diversity, tailored intervention, etc.

This paper has probed into the frame of emergency preparedness for older adults more comprehensively than the previous literature. In doing so, the aspect of emergency culture has been much supported.