Optimal Deployment of Cordon Sanitaire with Available Testing Capacity

During the COVID-19 pandemic, authorities in many places have implemented various countermeasures, including setting up a cordon sanitaire to restrict population movement. This paper proposes a bi-level programming model to deploy a limited number of parallel checkpoints at each entry link around the cordon sanitaire to achieve a minimum total waiting time for all travelers. At the lower level, it is a transportation network equilibrium with queuing for a fixed travel demand and given road network. The feedback process between trip distribution and trip assignment results in the predicted waiting time and traffic flow for each entry link. For the lower-level model, the method of successive averages is used to achieve a network equilibrium with queuing for any given allocation decision from the upper level, and the reduced gradient algorithm is used for traffic assignment with queuing. At the upper level, it is a queuing network optimization model. The objective is the minimization of the system's total waiting time, which can be derived from the predicted traffic flow and queuing delay time at each entry link from the lower-level model. Since it is a nonlinear integer programming problem that is hard to solve, a genetic algorithm with elite strategy is designed. An experimental study using the Nguyen-Dupuis road network shows that the proposed methods effectively find a good heuristic optimal solution. Together with the findings from two additional sensitivity tests, the proposed methods are beneficial for policymakers to determine the optimal deployment of cordon sanitaire given limited resources. © National Academy of Sciences: Transportation Research Board 2021.

A queueing‐theoretic framework for evaluating transmission risks in service facilities during a pandemic

We propose a new modeling framework for evaluating the risk of disease transmission during a pandemic in small‐scale settings driven by stochasticity in the arrival and service processes, that is, congestion‐prone confined‐space service facilities. We propose a novel metric, system‐specific basic reproduction rate, inspired by the "basic reproduction rate” concept from epidemiology, which measures the transmissibility of infectious diseases. We derive our metric for various queueing models of service facilities by leveraging a novel queueing‐theoretic notion: sojourn time overlaps. We showcase how our metric can be used to explore the efficacy of a variety of interventions aimed at curbing the spread of disease inside service facilities. Specifically, we focus on some prevalent interventions employed during the COVID‐19 pandemic: limiting the occupancy of service facilities, protecting high‐risk customers (via prioritization or designated time windows), and increasing the service speed (or limiting patronage duration). We discuss a variety of directions for adapting our transmission model to incorporate some more nuanced features of disease transmission, including heterogeneity in the population immunity level, varying levels of mask usage, and spatial considerations in disease transmission.

HEALTH RESOURCE ALLOCATION METHOD AT CORDON SANITAIRE FOR PANDEMIC CONTROL AND PREVENTION

The outbreak of COVID-19 and its variants has profoundly disrupted our normal life. Many local authorities enforced cordon sanitaires for the protection of sensitive areas. Travelers can only cross the cordon after being tested. This paper aims to propose a method to determine the optimal deployment of cordon sanitaires in terms of minimum queueing delay time with available health testing resources. A sequential two-stage model is formulated where the first-stage model describes transportation system equilibrium to predict traffic ows. The second-stage model, a nonlinear integer programming, optimizes health resource allocation along the cordon sanitaire. This optimization aims to minimize the system's total delay time among all entry gates. Note that a stochastic queueing model is used to represent the queueing phenomenon at each entry link. A heuristic algorithm is designed to solve the proposed two-stage model where the Method of Successive Averages (MSA) is adopted for the first-stage model, and a genetic algorithm (GA) with elite strategy is adopted for the second-stage model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that these methods can find a good heuristic solution, and it is not cost-effective for authorities to keep adding health resources after reaching a certain limit. These methods are useful for policymakers to determine the optimal deployment of health resources at cordon sanitaires for pandemic control and prevention. © 2023.

Multi-method simulation approach: multiple restaurants to multiple customers on-demand food delivery services in Dubai

Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy.

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals' data warehouses and regional infection data as recorded in the Netherlands. The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimising costs, the expected completion time, population travel time, and waiting time. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade-offs between solution cost, completion time, population travel time, and waiting time. © 2023 The Operational Research Society.

Design and Optimize of Campus Nucleic Acid Tests Queuing System

Covid-19 is more likely to spread in campus than it in other places because students live together without masks. In this case, it is necessary to take nucleic acid tests in a unified time regularly. To make nucleic acid tests efficient and convenient to manage students and the testing time, this article would apply queuing theory to design a nucleic acid tests queuing system by using the data from Sanda University in April 2022. According to the special conditions on campus, such as course schedule, students' daily activities, and campus management, students would be grouped by several management styles. The system would calculate the start time and waiting time for each group and would strive to take nucleic acid tests in an orderly manner with minimal waiting time. © 2022 IEEE.

Study on the mechanism of container port congestion under the epidemic

First, this paper analyzes the congestion of container ports at home and abroad under the current epidemic situation, then takes Yantian port of Shenzhen as the key research object to analyze the waiting time at anchorage and the stopping time at berth of the main container ports in China based on the data statistics. It studies the anchorage demand of ships with random and fluctuating arrival based on queuing theory. It gives the relationship between the stopping time of ship at berth and the maximum waiting time of the arriving ship, and reveals that the increase of ship stopping time and uneven arrival at the port are the main factors causing the current container port congestion, and puts forward some countermeasures to alleviate the port congestion. © 2022 SPIE

Apply the M/M/C Model of Queuing Theory in a Service System Based on FlexSim Simulation in the Post-COVID

The study includes a literature review, modeling and simulation concepts, applications, FlexSim characterization, and the M/M/C model, i.e., multiple channels. Customer service processes with Coronavirus Disease 2019 (COVID-19) have been affected by dissimilar reasons among them the distancing that causes queues to become longer and the set of operations to be carried out with the same personnel, being this a not so satisfactory experience for the customer. The article addresses key concepts related to the use of FlexSim software within a simulation model in a service process where decisions can be made based on the study of queuing theory. After performing the Poisson goodness-of-fit test, it was determined that the distribution of hourly queue arrivals does meet a Poisson-type distribution since its Chi-square test reaches a value of 0.92 which is well above the coefficient of 0.5. Therefore, the exact probability of finding n arrivals during a given time T can be found, if the process is random, as is the case of the cooperative. The average number of customers in the queue waiting to be served, gives a reduction from 1.04 to 0.14 customers, so it is understood that, if the increase of servers in the cooperative were applied, this would cause queues to be generated in the system, since its L_q is 0.14 customers. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Simulation of Patient Queuing System on MRI System at Tertiary Referral Hospital in Indonesia

Radiology department at a tertiary referral hospital faces service operation challenges such as huge and various patient arrival, which can increase the probability of patient queuing. During COVID-19 pandemic, it is mandatory to apply social distancing protocol in the radiology department. A strategy to prevent accumulation of patients at one spot would be required. The aim of this study is to identify an alternative solution which can reduce the patient's waiting time in MRI services. Discrete event simulation (DES) is used for this study by constructing several improvement scenarios with Arenao simulation software. Statistical analysis is used to test the validity of base case scenario model, and to investigate performance of the improvement scenarios. The result of this study shows that the selected scenario is able to reduce 83.6% of patient's length of stay, which lead into a more efficient MRI services in radiology department, be able to serve patients more effectively, and thus increase the patient satisfaction. The result of the simulation can be used by the hospital management to improve the operational performance of the radiology department. © 2021 IEEE.

Health resource allocation method at cordon sanitaire for pandemic control and prevention

The outbreak of COVID-19 and its variants has profoundly disrupted our normal life. Many local authorities enforced cordon sanitaires for the protection of sensitive areas. Travelers can only cross the cordon after being tested. This paper aims to propose a method to determine the optimal deployment of cordon sanitaires in terms of minimum queueing delay time with available health testing resources. A sequential two-stage model is formulated where the first-stage model describes transportation system equilibrium to predict traffic flows. The second-stage model, a nonlinear integer programming, optimizes health resource allocation along the cordon sanitaire. This optimization aims to minimize the system's total delay time among all entry gates. Note that a stochastic queueing model is used to represent the queueing phenomenon at each entry link. A heuristic algorithm is designed to solve the proposed two-stage model where the Method of Successive Averages (MSA) is adopted for the first-stage model, and a genetic algorithm (GA) with elite strategy is adopted for the second-stage model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that these methods can find a good heuristic solution, and it is not cost-effective for authorities to keep adding health resources after reaching a certain limit. These methods are useful for policymakers to determine the optimal deployment of health resources at cordon sanitaires for pandemic control and prevention.

An Integer Linear Programming for Hospital Bed Scheduling and Capacity Management

COVID-19 has struck the Philippines in December 2019 and has brought great panic to the country's healthcare system. In a short period of time, the number of infected increased exponentially. Hospitals are suddenly filled with patients infected by the virus to the extent that patients wait for hours to days to be admitted. Others die on the road even before finding hospitals that can accommodate them. The hospitals and the country's healthcare system must consider this increasing demand to serve patients fully. Patient planning is commonly used in other countries to maximize bed allocation. A recent study using Bernoulli Distributed Random Variable represents the binary integer program. The approach combines the queuing model and simulation to reduce the patient dismissal rate and increase hospital output. On the other hand, this paper deals with strategic hospital bed capacity optimization using linear integer programming by considering the diverse resources, such as doctors, nurses, beds, and hospital rooms. © 2022 ACM.

Application of Data Envelopment Analysis for pharmaceutical export decision-making during the COVID-19 pandemic

Decision-making in complex systems is undoubtedly quite difficult, mostly under exceptional circumstances. Indeed, in the context of international market selection, the COVID-19 pandemic has made pharmaceutical export decisions more complex. Several scientific approaches are used by researchers as well as practitioners to guide in this area. In particular, Operations Research techniques, including linear programming, discrete event simulation and queuing theory, are called by organizational leaders to make highquality decisions. This study presents a Benchmarking methodology to support the decision-making process for international market selection based on the Data Envelopment Analysis method. A computational numerical study was conducted to highlight the performance of the proposed approach. © 2022 IEEE.

Dynamic Allocation of Medical Resources During the Outbreak of Epidemics

During the outbreak of epidemics such as coronavirus disease (COVID-19), the local hospitals often withstand a sharp increase of patient influx, which renders the healthcare system on the verge of collapse. To alleviate the situation, the effective allocation of scarce medical resources during the pandemic plays a vital role. The essence of the healthcare system in time of emergency is to stay functional, and to be able to diagnose and hospitalize as many patients as possible. Fangcang shelter hospital, as a novel way to temporarily increase the capacity of the local healthcare system, is proven to be effective against the COVID-19 pandemic. To improve the performance of the healthcare system with Fangcang, many practical factors need to be taken into account, such as the patient deterioration during waiting to be admitted, the referral mechanism according to the severity of the patients, and the selective admission regulations. To address the high volatility and time-varying feature of the COVID-19, a multistage and multi-type medical service network model is established, and a dynamic allocation strategy of the medical resources at each stage is proposed based on a stochastic optimization problem, which is then solved via the fluid queueing approximation. Combined with the real data collected from Wuhan, it is revealed that the proposed algorithm could help with the allocation of medical resources during the outbreak of epidemics. Even with limited medical resources available, the method could still maintain a guaranteed service level while keeping the healthcare system operational. Furthermore, the simulation analysis validates that our method can effectively allocate medical resources at each stage, so as to stabilize the system performance and fulfill the medical demand for multiple types of patients. Note to Practitioners—To fend off the outbreak of epidemics, the lessons have to be learned from the past. The successful control of the spread of COVID-19 in Wuhan (China) is a classical example of applying modern medical practices and management tools. In the present article, the treating procedure of COVID-19 in Wuhan is modeled as a multistage decision problem, which includes the screening with nucleic acid testing, the further testing, the treatment of patients with mild/severe symptoms, or even critical patients. The introduction of Fangcang shelter hospital is crucial for winning the battle against COVID-19. The current study attempts to determine the timing of introducing the Fangcang shelter hospital during the outbreak of a major epidemic, and helps allocate the medical resources needed to contain the spread of the virus. It is discovered that the actual number of beds in the Fangcang shelter hospital is far more than what is necessary, and it would be better to have built the Fangcang some time in advance. In the meantime, the number of designated hospitals for COVID-19 is in line with the results obtained via the optimal staffing strategy proposed here, but it is also noticeable that these hospitals should be released of duty sooner to fight against not only COVID-19 but also other diseases in reality. [ FROM AUTHOR] Copyright of IEEE Transactions on Automation Science & Engineering is the property of IEEE 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.)

Poster: Safe Gate - IoT system to support sanitary measures to combat COVID-19

The COVID-19 pandemic crisis raised public health attention closer to our global society's demands. The disease proliferation occurs typically by droplet transmission, by being close to an infected person. Social distancing, a natural solution, is not always applicable to everyday needs, such as in the public transportation system, which is a space highly susceptible to viral proliferation. A set of ways to reduce proliferation in these infrastructures is by reinforcing facial masks usage, restraining symptomatic users, and reducing physical contact with public devices. Safe Gate, an Internet of Things (IoT) solution to enforce containment measures for disease proliferation, is proposed in this paper. This IoT solution is based on a network of edge computing devices used to control access to the entrance gate of the stations. The edge devices service samples an user's temperature and facial image to verify that body temperature is within normal bounds and the user is correctly wearing a face mask. The system is contact-free and does not require an active operator, with no personal data stored, preserving privacy. Additionally, it minimizes personnel involvement with passengers, ensuring staff protection. The research question is whether the solution with two levels of facial recognition using cognitive edge computing will meet the requirements of a real system. In addition to this question, a queuing model to verify the feasibility of the solution is presented and evaluates the operational impact on a real transportation system. © 2021 ACM.

A queueing-theoretic framework for evaluating transmission risks in service facilities during a pandemic

We propose a new modeling framework for evaluating the risk of disease transmission during a pandemic in small-scale settings driven by stochasticity in the arrival and service processes, that is, congestion-prone confined-space service facilities. We propose a novel metric, system-specific basic reproduction rate, inspired by the “basic reproduction rate” concept from epidemiology, which measures the transmissibility of infectious diseases. We derive our metric for various queueing models of service facilities by leveraging a novel queueing-theoretic notion: sojourn time overlaps. We showcase how our metric can be used to explore the efficacy of a variety of interventions aimed at curbing the spread of disease inside service facilities. Specifically, we focus on some prevalent interventions employed during the COVID-19 pandemic: limiting the occupancy of service facilities, protecting high-risk customers (via prioritization or designated time windows), and increasing the service speed (or limiting patronage duration). We discuss a variety of directions for adapting our transmission model to incorporate some more nuanced features of disease transmission, including heterogeneity in the population immunity level, varying levels of mask usage, and spatial considerations in disease transmission. © 2022 Production and Operations Management Society.

A Conceptual Model for Virtual Queuing System to Support Social Distancing

In the early December 2019, Coronavirus Disease 2019 (COVID-19) epidemic outbreak emerged from Wuhan City, Hubei Province, China and spread rapidly to the rest of the world. Globally, IT Professionals were forced to adapt and innovate rapidly in response to the pandemic and have devised a variety of IT solutions to ease peoples' transitions into the new normal. There are 4 major categories of IT-based COVID-19 solutions, namely contact tracing, quarantine management, symptom monitoring and information provision. However, most of these applications only focus on contact tracing as opposed to preventing the spread of the virus through the enforcement of social distancing. This paper presents a conceptual model for virtual queuing system, MyQueue which allows its users to enter virtual queues by scanning unique QR codes of various premises. It introduces proper process flows and approaches to eliminate waiting in a crowded queue and help support social distancing efforts to reduce the spread of COVID-19. © 2021 IEEE.

The effect on fulfillment time of the M-Division warehouse design approach

In last years, we are facing on the diffusion of on-line orders, a process further accelerated by the spread of the COVID-19 virus. A fundamental lever in the use of the e-Commerce is the continuous reduction of the fulfillment time. In order to obtain these improvements, the storage method has been revolutionized. In fact, we are talking about Online order fulfillment warehouses (F-Warehouses) which, unlike traditional warehouses, have a very large number of small bin locations, an explosive storage policy, where an incoming bulk is separated into small lots stocked in any bin throughout the warehouse, and commingled bin storage. The divided warehouse (M-Division) with explosive storage is a novel design, where the warehouse is divided in M-zones managed by specific pickers. In this paper we model the fulfillment process as an N server queuing model with uniform service times and compare the fulfillment time of the traditional warehouse with the M-Division warehouse. In particular, an analytical solution assigns items to the M-Division warehouse in order to minimize the fulfillment time. The results show that the M-Division approach permits a reduction of the fulfillment time compared with the traditional one. © 2021, AIDI - Italian Association of Industrial Operations Professors. All rights reserved.

DESIGNING AIRCRAFT TRAFFIC FLOWS USING DATA-DRIVEN QUEUING MODELS

This paper proposes a scientific and systematic method for designing future air traffic management systems by integrating data science, theoretical modeling, and simulation evaluation. Also, it presents a part of a case study focusing on the data-driven and theoretical modelings of arriving traffic flow in airports. A stochastic data analysis was conducted using actual radar tracks and flight plans before the impacts of COVID-19, where the queuing model parameters were estimated based on the conducted analysis. The proposed data-driven modeling approaches contribute to the analysis of the bottlenecks in air traffic and to their solutions. Overall, we believe that the outcomes of this study provide insights on future operational strategies and system designs, which can realize more efficient air traffic management systems. © 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.

A Study on the Effectiveness of the CLICK Approach in an Operations Research Course

This paper presents an investigation of the effectiveness of the connected learning and integrated course knowledge (CLICK) approach. The CLICK approach aims to integrate the knowledge across the industrial engineering (IE) curriculum by leveraging immersive technology, i.e., 3D simulation and virtual reality (VR). The effectiveness of the CLICK approach is measured by its impact on students' motivation, engineering identity, and learning outcomes. In this work, a virtual system that simulates a manufacturing assembly system was developed and used in an operations research (OR) course. The virtual system includes data collection tasks and exercises to calculate statistics that are taught in a probability and statistics course, and inventory and queueing theories concepts that are taught in an operations research course. The virtual system (CLICK learning module) is used to teach inventory and queueing theory concepts. Due to COVID-19 and the sudden shift to remote learning, the research team faced challenges including limitations in performing in-person experiments on campus as well as the potential risk of spreading the disease when VR headsets are used by several people. To alleviate some of the challenges, the researchers built the virtual system in simulation software, i.e., Simio, to provide more flexibility and scalability. The virtual system can be run on a regular personal computer without the need for a VR-ready computer and VR headsets. Yet, the virtual system can be run on an Oculus VR headset if the student prefers to do so. The study involves two groups: Control and intervention groups. The control group is represented by the students who are taught traditionally while the intervention group is represented by the students who are taught with the aid of the CLICK learning module. The results of this study compared the groups in terms of students' motivation, and engineering identity. The learning outcomes were assessed using a self-assessment instrument and the student's grades in the learning module. The data of the control and intervention groups were collected at Penn State Behrend in Fall 2019, and Fall 2020 semesters, respectively. The groups were not statistically significantly different for motivation and Engineering Identity, however, the resulted motivation and Engineering Identity scores for the intervention group were not worse than the control group considering the shift to remote learning setting. The students showed good learning outcomes when the CLICK learning module was used. The grades were positively correlated to the motivation and Engineering Identity scores. © American Society for Engineering Education, 2021