Historical data suggest that when a severe tropical storm or hurricane impacts a community, the vulnerable segment of the population suffers the most severe consequences. With an increased aging population, it is crucial to understand how vulnerability alters evacuation behavior. Emergent variables such as fear of COVID-19 require additional exploration. People afraid of COVID-19 exposure may refuse to evacuate, exposing themselves unnecessarily. Differentiation is critical to evacuation logistics since it is needed to determine what proportion would stay in a local shelter, public or other, rather than evacuating or staying in their home and guide the logistics resource allocation process. This research uses data from a web and phone survey conducted in the Hampton Roads area of U.S. Virginia, with 2,200 valid responses to analyze the influence of social and demographic vulnerability factors and risk perception on evacuation decisions. This research contributes to the existing literature by developing a multinomial order logit model based on vulnerability factors and intended evacuation decisions, including staying at home, looking for a shelter, or leaving the Hampton Roads area. Findings show that race and risk perception are the variables that influence the decision-making process the most. Fear of COVID-19 transmission is also associated with an increased likelihood of leaving homes during evacuation. The variations in findings from previous studies are discussed regarding their implications for logistics emergency managers.

The speed of recovery from supply chain disruption has been identified as the predominant factor in building a resilient supply chain. However, COVID-19 as an example of an evolving crisis may challenge this assumption. Infection risk concerns may influence production resumption decision-making because any incidents of infection may lead to further shutdowns of production lines and undermine firms' long-term cash flows. Sampling 244 production resumption announcements by Chinese manufacturers in the early COVID-19 crisis (February-March 2020), our analysis shows that, generally, investors react positively to production resumptions. However, investors perceived the earlier production resumptions were higher risk (indicated by declined stock price). Such concerns were exacerbated by more locally confirmed cases of COVID-19 but were less salient for manufacturers with high debts (liquidity pressure). This study calls for a reassessment of the current disruption management mindset in response to new evolving crises (e.g., COVID-19) and provides theoretical, practical, and policy implications for building resilient supply chains.

The COVID-19 pandemic led to a significant breakdown of the traditional retail sector resulting in an unprecedented surge in e-commerce demand for the delivery of essential goods. Consequently, the pandemic raised concerns pertaining to e-retailers' ability to maintain and efficiently restore level of service in the event of such low-probability high-severity market disruptions. Thus, considering e-retailers' role in the supply of essential goods, this study assesses the resilience of last-mile distribution operations under disruptions by integrating a Continuous Approximation (CA) based last-mile distribution model, the resilience triangle concept, and the Robustness, Redundancy, Resourcefulness, and Rapidity (R4) resilience framework. The proposed R4 Last Mile Distribution Resilience Triangle Framework is a novel performance-based qualitative-cum-quantitative domain-agnostic framework. Through a set of empirical analyses, this study highlights the opportunities and challenges of different distribution/outsourcing strategies to cope with disruption. In particular, the authors analyzed the use of an independent crowdsourced fleet (flexible service contingent on driver availability); the use of collection-point pickup (unconstrained downstream capacity contingent on customer willingness to self-collect); and integration with a logistics service provider (reliable service with high distribution costs). Overall, this work recommends the e-retailers to create a suitable platform to ensure reliable crowdsourced deliveries, position sufficient collection-points to ensure customer willingness to self-collect, and negotiate contracts with several logistics service providers to ensure adequate backup distribution.

The evolving COVID-19 epidemic pose significant threats and challenges to emergency response operations. This paper focuses on designing an emergency logistic network, including the deployment of emergency facilities and the allocation of supplies to satisfy the time-varying demands. A framework is proposed for the emergency logistic network design. We first present an improved short-term epidemic model to predict the evolutionary trajectory of the epidemic. Then, considering the uncertainty of the estimated demands, we construct a capacitated multi-period, multi-echelon facility deployment and resource allocation robust optimization model to improve the reliability of the decisions. To address the conservativeness of robust solutions during the evolution of the epidemic, an uncertainty budget adjustment strategy is proposed and integrated into the rolling horizon optimization approach. The results of the case study show that (i) the short-term prediction method has higher accuracy and the accuracy increases with the amount of observed data; (ii) considering the demand uncertainty, the proposed robust optimization model combined with uncertainty budget adjustment strategy can improve the performance of the emergency logistic network; (iii) the proposed solution method is more efficient than its benchmark, especially for large-scale cases. Moreover, some managerial insights related to the emergency logistics network design problem are presented.

Since the outbreak of COVID-19, its impacts have been envisaged in multi-dimensional ways, including global supply chains, international logistics, and transportation. Owing to a series of virus variants since 2020, several Belt and Road Initiative (BRI) projects have been halted along the Belt and Road (B&R), and their implementation progress has been affected. In addition, China, which initiated the BRI in 2013, has been facing challenges which are caused by its economic, socio-demographic, and international political structural changes. Moreover, growing conflicts and tensions in international trade and politics, such as the war between Russia and Ukraine, China-US trade conflicts, foreign companies' reshoring the production lines from China, and diplomatic disputes between China and Australia, have been interwoven with the impacts of COVID-19 on the supply chains, international trade, and transportation in the world. Having considered the above, this study explores a new research-driven approach to reignite the BRI momentum in the transitional period towards the post-COVID-19 era from a Chinese economic perspective. In doing so, this paper proposes nine research agendas, such as the optimization network of transportation and logistics distribution centers (LDCs) along the B&R, priority development and performance of LDCs, greening the B&R with green shipping corridors, revisiting port devolution continuum, humanitarian logistics in association with COVID-19, security and risk analysis of China's energy supply chains, and export of the 6th Generation Ports (6GP) model with smart ports to major container ports along the port supply chains. Each research agenda is addressed with its motivation, significance, and applicable and representative methods.

With the impact of the COVID-19 pandemic, global container freights have increased dramatically since the second half of 2020, which has significantly hampered the booking activities of fragmented transportation space for small and medium-sized import and export enterprises (SMIEEs). To provide SMIEEs with an effective tool for controlling shipping costs, we propose the design principles of index microinsurance under fragmented scenarios and design the container freight index microinsurance (CFIM) based on a comprehensive analysis of the term, compensation and share structures. We further establish the pricing model for the CFIM and selection procedure for product optimization, and illustrate the framework with a case study based on the data of the China Containerized Freight Index Europe Service, which demonstrates the good performance of the designed product even under extreme market conditions. The design principles proposed can shed light on the innovation of index microinsurance product that meets fragmented needs and the newly designed CFIM, along with the pricing and optimization procedure, provides practitioners with useful tools for cost control.

Omnichannel sales surge in the coronavirus pandemic. This paper establishes an analytical model to study when a firm can benefit from implementing the omnichannel strategy of buy-online and pick-up in-store (BOPS), where the market characteristics are captured by the two-dimensional heterogeneity of product valuation and online waiting cost. The increase in the store visiting cost will reduce BOPS consumers' willingness to pay, but it will also strengthen the encroachment of BOPS on traditional dual-channel. The results show that the firm can benefit from the BOPS strategy when the store visiting cost is relatively high. This well explains the rapid development of the omnichannel with BOPS because of a high store visiting cost during COVID-19. Furthermore, sharply contrasting to the traditional dual-channel sales in which a higher store visiting cost always hurts the firm, the profit under BOPS can be nonmonotonic in the store visiting cost and the firm can benefit from a higher store visiting cost. Specifically, the combination of cross-selling effect, and associated with the store visiting cost can result in a U-shaped or inverse U-shaped BOPS profit. In addition, introducing BOPS motivates the firm to either increase or decrease the optimal price, conditional on the store visiting cost. For consumers, online and offline consumers can also indirectly benefit from the BOPS strategy, though they may not enjoy the BOPS service.

This study quantifies the effects of health control measures at the airport on passenger behaviour related to business travel. A stated preference survey was conducted over potential air travellers in Hong Kong in the context of COVID-19 pandemic. Panel latent class models were estimated to understand passenger preference toward new travel requirements given the applicability of online meeting. Online meeting is applicable in cases where it is a good substitute of air travel and achieves the same outcomes of a trip, and inapplicable otherwise. Empirical results indicate that traveller subgroups are affected in different ways. When an online meeting is inapplicable, nearly 75% of the respondents prefer to travel for business and undertake health screenings. These passengers (identified as "captive" business travellers) perceive such measures necessary to lower health related risks during air travel. As such, they are willing to spend up to 21 to 38 min on the health control measures such as vaccination record requirements and test involving sample collection. When an online meeting is applicable, the share of "choice" business travellers is about 45%, among whom the attitudes towards health control measures become more averse. The average weighted willingness-to-pay for the time saved at health checkpoints increase significantly. The aviation industry thus faces a "double-hit" problem: operation costs will increase due to pandemic control measures, and the resultant inconvenience, extra time and costs further reduces travel demand. Unlike previous short pandemics, business travel is likely to suffer with an extended decline until the pandemic is fully controlled. These identified challenges call for financial and operational support to help the aviation industry reach a sustainable "new normal". The high value of time saved at check points also justifies investments that make the pandemic control and health measures efficient and smooth. Travellers' time spent on airport health control should be within 20 min to avoid substantial negative impacts on business travel demand.

Crisis-induced vaccine supply chain management has recently drawn attention to the importance of immediate responses to a crisis (e.g., the COVID-19 pandemic). This study develops a queuing model for a crisis-induced vaccine supply chain to ensure efficient coordination and distribution of different COVID-19 vaccine types to people with various levels of vulnerability. We define a utility function for queues to study the changes in arrival rates related to the inventory level of vaccines, the efficiency of vaccines, and a risk aversion coefficient for vaccinees. A multi-period queuing model considering congestion in the vaccination process is proposed to minimise two contradictory objectives: (i) the expected average wait time of vaccinees and (ii) the total investment in the holding and ordering of vaccines. To develop the bi-objective non-linear programming model, the goal attainment algorithm and the non-dominated sorting genetic algorithm (NSGA-II) are employed for small- to large-scale problems. Several solution repairs are also implemented in the classic NSGA-II algorithm to improve its efficiency. Four standard performance metrics are used to investigate the algorithm. The non-parametric Friedman and Wilcoxon signed-rank tests are applied on several numerical examples to ensure the privilege of the improved algorithm. The NSGA-II algorithm surveys an authentic case study in Australia, and several scenarios are created to provide insights for an efficient vaccination program.

In nowadays world, firms are encountered with many challenges that can jeopardize business continuity. Recently, the coronavirus has brought some problems for supply chain networks. Remarkably, perishable product supply chain networks, such as pharmaceutical, dairy, blood, and food supply chains deal with more sophisticated situations. Generally, during pandemic outbreaks, the activities of these industries can play an influential role in society. On the one hand, products of these industries are considered to be daily necessities for living. However, on the other hand, there are many new restrictions to control the coronavirus prevalence, such as closing down all official gatherings and lessening the work hours, which subsequently affect the economic growth and gross domestic product. Therefore, risk assessment can be a useful tool to forestall side-effects of the coronavirus outbreaks on supply chain networks. To that aim, the decision-making trial and evaluation laboratory approach is used to evaluate the risks to perishable product supply chain networks during the coronavirus outbreak era. Feedback from academics was received to identify the most important risks. Then, experts in pharmaceutical, food, and dairy industries were inquired to specify the interrelations among risks. Then, Pythagorean fuzzy sets are employed in order to take the uncertainty of the experts' judgments into account. Finally, analyses demonstrated that the perishability of products, unhealthy working conditions, supply-side risks, and work-hours are highly influential risks that can easily affect other risk factors. Plus, it turned out that competitive risks are the most susceptive risk in the effect category. In other words, competition among perishable product supply chain networks has become even more fierce during the coronavirus outbreak era. The practical outcomes of this study provide a wide range of insights for managers and decision-makers in order to prevent risks to perishable product supply chain networks during the coronavirus outbreak era.

The decision to work from home (WFH) or to commute during COVID-19 is having a major structural impact on individuals' travel, work and lifestyle. There are many possible factors influencing this non-marginal change, some of which are captured by objective variables while others are best represented by a number of underlying latent traits captured by attitudes towards WFH and the use of specific modes of transport for the commute that have a bio-security risk such as public transport (PT). We develop and implement a hybrid choice model to investigate the sources of influence, accounting for the endogenous nature of latent soft variables for workers in metropolitan areas in New South Wales and Queensland. The data was collected between September-October 2020, during a period of no lockdown and relatively minor restrictions on workplaces and public gatherings. The results show that one of the most important attributes defining the WFH loving attitude is the workplace policy towards WFH, with workers that can decide where to work having a higher probability of WFH, followed by those that are being directed to, relative to other workplace policies. The bio-security concern with using shared modes such as public transport is a key driver of WFH and choosing to commute via the safer environment of the private car.

While the swift development and production of a COVID-19 vaccine has been a remarkable success, it is equally crucial to ensure that the vaccine is allocated and distributed in a timely and efficient manner. Prior research on pandemic supply chain has not fully incorporated the underlying factors and constraints in designing a vaccine allocation model. This study proposes an innovative vaccine allocation model to contain the spread of infectious diseases incorporating key contributing factors to the risk of uninoculated people including susceptibility rate and exposure risk. Analyses of the data collected from the state of Victoria in Australia show that a vaccine allocation model can deliver a superior performance in minimizing the risk of unvaccinated people when a multi-period approach is employed and augmenting operational mechanisms including transshipment between medical centers, capacity sharing, and mobile units being integrated into the vaccine allocation model.

Subways play an important role in public transportation to and from work. In the traditional working system, the commuting time is often arranged at fixed time nodes, which directly leads to the gathering of "morning peak" and "evening peak" in the subway. Under the COVID-19 pandemic, this congestion is exacerbating the spread of the novel coronavirus. Several countries have resorted to the strategy of stopping production to curb the risk of the spread of the epidemic seriously affecting citizens' living needs and hindering economic operation. Therefore, orderly resumption of work and production without increasing the risk of the spread of the epidemic has become an urgent problem to be solved. To this end, we propose a mixed integer programming model that takes into account both the number of travelers and the efficiency of epidemic prevention and control. Under the condition that the working hours remain the same, it can adjust the working days and commuting time flexibly to realize orderly off-peak travel of the workers who return to work. Through independent design of travel time and reasonable control of the number of passengers, the model relaxes the limitation of the number of subway commuters and reduces the probability of cross-travel between different companies. We also take the data of Beijing subway operation and apply it to the solution of our model as an example. The example analysis results show that our model can realize the optimal travel scheme design of returning to work at the same time node and avoiding the risk of cross infection among enterprises under different epidemic prevention and control levels.

In this work, we formulate the blood products supply chain problem in the wake of disasters such as the COVID-19 (SARS-CoV-2) pandemic using two-stage stochastic programming where uncertainty of both demand and supply is considered. The products considered are red blood cells (RBCs), plasma, and platelets. Age-based demand and blood type substitution are included in our model. A heuristic is developed to solve the instances a commercial optimization software failed to solve in a reasonable amount of time. To obtain managerial insight a sensitivity analysis is conducted. Results of the analysis show that bigger capacities of permanent collection facilities are favored over the mobility of temporary facilities while accounting for blood substitution and age-based demand in the planning phase reduced shortages significantly. Moreover, different objective functions were considered to ensure fairness in distribution of the products among hospitals. The fairer distribution resulted in an increase in the total unmet demand.

This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.

Resilience amidst a crisis is vital to survival in the turbulent contemporary business environment. Diversifying the supply chain has been proposed as an important means to build this capability. However, there is insufficient empirical evidence demonstrating the merits of supply chain diversification during a crisis. Sampling 1434 Chinese manufacturing firms amidst the COVID-19 crisis, our two-stage least squares (2SLS) regression analyses show that firms with a diversified supply base are associated with a larger supply stream (increased abnormal inventory) and increased profitability during the COVID-19 crisis, including both the disruption and recovery periods. In addition, firms with a diversified customer base are associated with a larger demand stream (reduced abnormal inventory) during the COVID-19 crisis (both disruption and recovery periods) but show increased profitability only during the recovery period. Our study contributes to the literature on supply chain risk, disruption, diversification, and inventory management. We also discuss the practical implications of supply chain structure design in building resilience.

The Covid-19 pandemic has dramatically changed consumer purchase behavior, and the "stay-at-home order" policy has altered the operations of brick-and-mortar (B&M) retail stores. These changes have induced local B&M retailers to start online retailing with home delivery as an added option. B&M retailers can choose to offer online retailing on their own (referred to as self-building mode) or via a third-party online-to-offline (O2O) platform (referred to as platform mode). This paper investigates how the interplay between capacity, pricing, and online retailing mode is affected by the absence/presence of the pandemic. We characterize the equilibrium between the B&M retailer and the O2O platform provider. We find that the impact of the "stay-at-home orders" on B&M retailers differs by the online retailing mode. Interestingly, we find that the "stay-at-home orders" does not necessarily lower the B&M retailer's profit if they engage in online retailing. Under self-building mode, the stay-at-home order leaves the B&M retailer with just the online channel. We identify the threshold delivery cost above (below) which the B&M retailer's profit is lower (higher) than before. Under the platform mode, the "stay-at-home order" alters the retailer's sales channel from dual channel to single channel, which mitigates the competition between the retailer and the O2O platform. The retailer's profit increases if it has sufficiently high capacity. Finally, we extend the model to examine the effect of a "reopening policy" with a government subsidy. We find that although the subsidy improves the B&M retailer's profitability, it may hurt the consumer surplus under some conditions. We suggest that governments take the B&M retailer's capacity and operations mode into account when designing subsidy policies.

In this study, we examine the impact of the COVID-19 pandemic on food retail supply chains (SCs) and their resilience. Based on real-life pandemic scenarios encountered in Germany, we develop and use a discrete-event simulation model to examine SC operations and performance dynamics with the help of anyLogistix digital SC twin. The computational results show that food retail SC resilience at the upheaval times is triangulated by the pandemic intensity and associated lockdown/shutdown governmental measures, inventory-ordering dynamics in the SC, and customer behaviours. We observe that surges in demand and supplier shutdowns have had the highest impact on SC operations and performance, whereas the impact of transportation disruptions was rather low. Transportation costs have spiked because of chaotic inventory-ordering dynamics leading to more frequent and irregular shipments. On bright side, we observe the demand growth and utilization of online sales channels yielding higher revenues. We propose several directions and practical implementation guidelines to improve the food retail SC resilience. We stress the importance of SC digital twins and end-to-end visibility along with resilient demand, inventory, and capacity management. The outcomes of our study can be instructive for enhancing the resilience of food retail SCs in preparation for future pandemics and pandemic-like crises.

Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR). With the help of delivery robots, considerable operational time savings can be achieved by dispatching robots to serve nearby customers while a driver is also serving a customer. We provide a mathematical model for the VRPTWDR and investigate the challenges and benefits of using delivery robots as assistants for city logistics. A two-stage matheurisitic algorithm is developed to solve medium scale VRPTWDR instances. Finally, results of computational experiments demonstrate the value of self-driving delivery robots in urban areas by highlighting operational limitations on route planning.