COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Covid 19 vaccine order allocation: an optimization model with substitution

PurposeThis paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.Design/methodology/approachThe weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.FindingsCovid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.Originality/valueThis paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors' knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.

A goal-programming based optimal port docking scheme under COVID-19.

The Covid-19 epidemic, has caused a large-scale congestion in many ports around the world. This increases the cost of port docking, as well as delays the loading and unloading of goods, which affects the price and timely supply of many products. Although scholars have carried out in-depth discussion and analysis on the port congestion problem from different perspectives, there is still no appropriate model and algorithm for the large-scale comprehensive port docking problem. This paper presents a new mixed integer programming model for optimal docking of ships in ports that is comprehensive enough to include four essential objectives. It discusses the generalization and application of the model from the perspectives of the shortest overall waiting time of ships, the balance of tasks at each berth, completion of all docking tasks as soon as possible and meeting the expected berthing time of ships. We demonstrate the results of our models using relevant examples and show that our model can obtain the optimal docking scheme based on different perspectives and relevant objectives. We also show that the scale of the exact solution can reach tens of thousands of decision variables and more than a million constraints. This fully reflects the possibility that the model can be put into use in any real life scenario. This model can not only effectively improve the docking efficiency of the port, but is also suitable for the complex queuing problem of multi window and the same type of service.

Drones-aided Asset Maintenance in Hospitals

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.

Modeling social, economic, and health perspectives for optimal pandemic policy decision-making.

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented ε-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic.

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.

Digital twin-driven robust bi-level optimisation model for COVID-19 medical waste location-transport under circular economy

To mitigate the spread of novel coronavirus, how to optimise COVID-19 medical waste location-transport strategies remains an open but urgent issue. In this paper, a novel digital twin-driven conceptual framework is proposed to improve the strategic decision on the location of temporary disposal centres and, subsequently, the operational decision on the transport of COVID-19 medical waste in the presence of hierarchical relationships amongst stakeholders, circular economy, uncertainty in infection probability, and service level. The circular economy aspect is measured by the reduction of infection risks and costs, as well as limiting exhaust emissions. The polyhedral uncertainty set is introduced to characterise stochastic infection probability. Digital twin technology is further used to estimate the upper and lower bound of the uncertainty set. Such a problem is formulated as a digital twin-driven robust bi-level mixed-integer programming model to minimise total infection risks on the upper level and total costs on the lower level. A hybrid solution strategy is designed to combine dual theory, Karush-Kuhn-Tucker (KKT) conditions, and a branch-and-bound approach. Finally, a real case study from Maharashtra in India is presented to evaluate the proposed model. Results demonstrate that the solution strategy performs well for such a complex problem because the CPU time required to conduct all experiments is less than one hour. Under a given uncertainty level of 36 and perturbation ratio of 20%, a regional transport strategy is preferred from generation points to transfer points, while a cross-regional one is usually implemented from transfer points to disposal centres. It is of significance to determine the bound of available temporary disposal centres. Using digital technology (e.g., digital twin) to accurately estimate the amount of COVID-19 medical waste is beneficial for controlling the pandemic. Reducing infection risks relative to cost is the prioritised goal in cleaning up COVID-19 medical waste within a relatively long period.

Optimizing COVID-19 vaccine allocation considering the target population.

Vaccine allocation strategy for COVID-19 is an emerging and important issue that affects the efficiency and control of virus spread. In order to improve the fairness and efficiency of vaccine distribution, this paper studies the optimization of vaccine distribution under the condition of limited number of vaccines. We pay attention to the target population before distributing vaccines, including attitude toward the vaccination, priority groups for vaccination, and vaccination priority policy. Furthermore, we consider inventory and budget indexes to maximize the precise scheduling of vaccine resources. A mixed-integer programming model is developed for vaccine distribution considering the target population from the viewpoint of fairness and efficiency. Finally, a case study is provided to verify the model and provide insights for vaccine distribution.

Multi-resource collaborative scheduling problem of automated terminal considering the AGV charging effect under COVID-19.

Since the COVID-19 ravaged the global terminals, the Automated Container Terminal (ACT) has become one of important approach to promote the stronger quick response capacity to deal with the uncertainty that COVID-19 brought to the terminal. This research takes Automated Guided Vehicle (AGV) and their effects into account the multi-resource collaborative scheduling model to tradeoff ACT operational efficiency and energy savings. Firstly, the dual-cycle strategy of QC and the pooling strategy of AGV are given, which coordinates the scheduling of Quay Cranes (QCs), Yard Cranes (YCs) and other equipment. Furthermore, a multi-resource collaborative scheduling optimization model is proposed which roots from the principle of the Blocking-type Hybrid Flow Shop Problem (B-HFSP) with the objectives of minimizing the makespan of QC and the transportation energy consumption. And simultaneously, a mixed algorithm SA-GA is designed for solving this mixed integer programming model by an optimizing effect of Simulated Annealing on Genetic algorithms. Numerical experiments show that the model in this research is effective. The convergence of SA-GA is effective for small-scale cases and superior for large-scale cases. Considering both goals of high efficiency and energy saving, the Pareto solution set and collaborative scheduling solution take a priority to ensure that the bottlenecked QC runs efficiently. Here and now the average idle rate of QC is about [14%, 35%] lower than that of other equipment. The collaborative scheduling model constructed above not only has reference value for other multi-device and multi-stage scheduling problem, but also enhance the integrated decision-making ability of the ACT in the post-epidemic era.

Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability.

A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

Purpose>One of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approach>One of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.Findings>The proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/value>The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Optimal Scheduling of COVID-19 Pandemic Supply Disruptions Under Uncertainty

With the outbreak of COVID-19 pandemic, the problem of supply chain emergency scheduling has had a great influence on economic benefits of enterprises. The uncertainty of the COVID-19 pandemic and uncertainty of supply chain have made the problem of emergency scheduling more complicated. In this study, a multiobjective multiperiod mixed-integer programming optimization model was developed, in which two conflicting benefit factors, cost and service level were taken as the optimization target of the model. Cost and service level were normalized, and the weighted sum was taken as the objective function, which transformed the problem into a multiperiod nonlinear one. Two algorithms of Bi-level Modified Hybrid Genetic Algorithm (BMHGA) and Bi-level Hybrid Genetic Algorithm (BHGA) were designed to solve the model. Three emergency strategies have been proposed, including enabling alternative suppliers, repairing failure nodes and enabling internal suppliers' flexibility. The parameter of disruption scenario and its solution method were designed. Finally, the practicability of the proposed model and algorithm was demonstrated through application to a case study of an electronics supply chain. The results indicated that the optimal solution of two algorithms was between 2%-5%, and BMHGA could obtain a better optimal solution.

A queueing Network approach for capacity planning and patient Scheduling: A case study for the COVID-19 vaccination process in Colombia.

This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach representing the dynamics of an open Jackson Network and a Generalized Network. To test these models, we proposed three objective functions and analyzed different configurations of the process corresponding to various levels of the models' parameters as well as the conditions present in the case study. To assess the computational performance of the models, we also experimented with larger instances in terms of number of steps or stations used and number of patients scheduled. The computational results show how parameters such as the minimum percentage of patients served, the maximum occupation allowed per station and the objective functions used have an impact on the configuration of the process. The proposed approach can support the decision-making process in vaccination centers to efficiently assign human and material resources to maximize the number of patients vaccinated while ensuring reasonable waiting times, number of patients in queue and servers' utilization rates, which in turn are key to avoid overcrowding and other negative conditions in the system that could increase the risk of infections.

Design of hazardous medical waste network under emergency conditions

The medical waste generated during the epidemic is highly infectious. If it is not handled promptly and properly, it will have a bad effect. Under the constraints of limited time and funds, how to effectively and timely deal with the hazardous medical waste generated by epidemic patients is an issue of public concern. Reasonable network design of hazardous medical waste management system under emergency conditions is the key to solving this problem. However, so far, there are few related studies. This article considers factors such as multiple time periods, multiple types of medical waste, treatment technologies, and recycling. This research aims to explore issues such as the location of medical waste treatment facilities and whether they are operating in each time period, the distribution and transportation of various medical wastes, the selection of medical waste treatment technologies, and the processing capabilities of network nodes. With the goal of minimizing total economic cost and total risk, a multiobjective mixed integer programming model is constructed to obtain the optimal hazardous medical waste management system network. Take the management of hazardous medical waste during the COVID-19 in Wuhan as an example. The linear weighted summation method, augmented ε constraint method and augmented weighted Tchebycheff method are used to obtain high-quality non-dominated solutions. The numerical performance is compared and the validity and feasibility of the model are verified. The experimental results show that the augmented ε constraint method can obtain non-dominated solutions with more uniform distribution. A reasonable network design of the hazardous medical waste management system can well balance the total economic cost and the total risk. Decision makers can effectively control the total economic cost and total risk in the process of medical waste management by adjusting parameters such as weighting factors and adjusting the processing capacity and recovery rate of network nodes such as processing centers and recycling centers. The first part considers the network design of hazardous medical waste management system under emergency conditions, including the location of facilities, the transportation of hazardous medical waste, the selection of treatment technology and recycling. The network nodes involved mainly include: hospitals, temporary and existing hazardous medical waste treatment centers, recycling centers, and garbage disposal centers. The main problems to be solved in this paper are: The transportation and treatment of various types of hazardous medical waste generated by hospitals in a short period of time with a significant increase in quantity;In order to deal with a large amount of hazardous medical waste in time, the location of temporary treatment center, recycling center and garbage disposal center involved;Opening and operating the temporary processing center and recycling center in each time period;When the temporary processing center and recycling center are opened, the technical issues that need to be handled;The processing capacity of network nodes and the impact of factors such as recycling on the management of hazardous medical waste and other issues. Two goals were considered: One is to minimize the total cost, including the fixed open cost of each network node, operating cost, hazardous medical waste treatment cost and transportation cost, etc.;The second is to minimize the total risks, including the risks arising from the handling and transportation of hazardous medical waste. The two goals are in conflict with each other. This article uses a multiobjective optimization method to balance the two goals, and finally builds a multi-objective mixed integer programming model. In the second part, it is difficult to clearly distinguish the relative importance of multiple goals when considering the decisionmaker′ s decision-making. Therefore, a representative set of non-dominant solutions needs to be required for decision-makers to make decisions based on personal preference and actual management issues. It is different from the weighted summation method used in most literature. In this paper, both the augmented weighted Tchebycheff method and the augmented ε constraint method are used to solve the multi-objective mixed integer programming model constructed in this paper, and finally the Pareto optimal solution is given. In the third part, the management of hazardous medical waste generated during the period of COVID-19 in Wuhan, China was taken as an example to verify the feasibility and effectiveness of the model in practice, and compared the numerical performance of three multi-objective optimization methods. According to the model in this paper, it is found through comparison that the augmented ε constraint method is better than the other two methods in terms of calculation time and uniformity of solution distribution. The results of numerical examples show that decision-makers can effectively control the total economic cost and total risk of the hazardous medical waste management system by adjusting the weighting factors, network node processing capacity ratios and recovery rates. When dealing with the problem of hazardous medical waste generated by a highly contagious epidemic (such as COVID-19), risk factors can be considered first, followed by cost. Adjust the weight of the objective function according to the specific problem, and then solve the problem of hazardous medical waste management more rationally and efficiently. Finally, it is hoped that the model in this article can solve the actual hazardous medical waste management problems, and the uncertain issues involved in the hazardous medical waste treatment process will be further studied in the future. © 2022, Journal of Industrial Engineering/ Engineering Management. All Rights Reserved.

Covid 19 vaccine order allocation: an optimization model with substitution

Purpose: This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty. Design/methodology/approach: The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model. Findings: Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic. Originality/value: This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature. © 2022, Emerald Publishing Limited.

A combined optimization-simulation approach for modified outside-in boarding under COVID-19 regulations including limited baggage compartment capacities.

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated for potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50%, 66%, and 80% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30% by applying our approach, while keeping the transmission risk at the lowest level.

Allocation Optimization of Medical Samples For Distributed Testing

The world is witnessing the COVID-19 pandemic, which originated in the city of Wuhan, China, and has quickly spread to the whole world, with many cases having been reported in India as well. The healthcare system is going through unprecedented load on its resources while the available infrastructure is inadequate.COVID-19 samples are being tested at a massive scale and even small optimizations at this scale can save time, huge amounts of money, and resources. Particularly, the manual approach or even baseline greedy approach being used to allocate COVID-19 samples to medical labs across a state can lead to underutilization of resources. Hence, this work proposes a system to optimize the problem of allocation of medical samples to medical testing laboratories with high efficiency and minimal economic penalty. We use the Mixed Integer Programming (MIP) Model using high-performance MIP based solvers for custom applications by providing a tight integration with the branch-and-cut algorithms of the supported solvers to improve the results compared to baseline greedy approach. The system provides a transportation schedule optimized with respect to capacity of different labs and COVID-19 cases across the state of Karnataka. We tested the model on various datasets and observed significant improvement over the baseline greedy model.

Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study

This paper presents a multi-portfolio approach and scenario-based stochastic MIP (mixed integer programming) models for optimization of supply chain operations under ripple effect. The ripple effect is caused by regional pandemic disruption risks propagated from a single primary source region and triggering delayed regional disruptions of different durations in other regions. The propagated regional disruption risks are assumed to impact both primary and backup suppliers of parts, OEM (Original Equipment Manufacturer) assembly plants as well as market demand. As a result, simultaneous disruptions in supply, demand and logistics across the entire supply chain is observed. The mitigation and recovery decisions made to improve the supply chain resilience include pre-positioning of RMI (Risk Mitigation Inventory) of parts at OEM plants and ordering recovery supplies from backup suppliers of parts, located outside the primary source region. The decisions are spatiotemporally integrated. The pre-positioning of RMI implemented before a disruptive event is optimized simultaneously with the RMI usage and recovery supply portfolios for the backup suppliers in the aftermath periods. The recovery supplies of parts and production at OEM plants, are coordinated under random availability of suppliers and plants and random market demand. The resilient solutions for the resilient supply portfolios are compared with the non-resilient solutions with no recovery resources available. The findings indicate that the resilient measures commonly used to mitigate the impacts of region-specific disruptions can be successfully applied for mitigation the impacts of multi-regional pandemic disruptions and the ripple effect.(c) 2022 Elsevier Ltd. All rights reserved.

A mathematical model for the optimization of e-commerce logistics operations in the face of the spread of COVID-19

In this work, it was carry out a new model of attention to product orders in e-commerce operations in a pandemic situation. The model was evaluated in situations that require capacity and flexibility for companies when the forecast presents a high level of uncertainty. A two-echelon multi-period MIP model was used. It was shown from a case example the model's behavior in stressful situations, which represent a pandemic moment or high demand, and a case in which companies can use the model for decision-making when demand is lower or stable. It is proposed to use a productivity factor and extra hours to decide to hire permanent or temporary employees to take the best strategies in its logistics operations. The results show the usability of the model for decisions and the flexibility obtained for better productivity in e-commerce operations. © 2021 IEEE.

Social distancing in airplane seat assignments for passenger groups

We provide a mixed-integer programming model (MIP) to assign airplane passengers to seats while preserving two types of social distancing: the distance from the passengers' seats to the aisle and the distance among groups of passengers who are not travelling together. The method assigns passengers travelling within a family group to seats near others of the same group. We present a heuristic algorithm to solve the proposed MIP. This algorithm is warm started with an initial seat assignment. Stochastic simulation experiments using the new method confirm that more passengers can be assigned safely to the seats when family groups are considered. For a certain load of passengers, as the percentage of family groups compared to singleton passengers increases, the model can practice social distancing among more passengers from different groups. The proposed model provides a superior seating assignment compared to an airline policy of blocking all middle-seats.