ABSTRACT
This study presents and discusses the home delivery services in stochastic queuing-inventory modeling (SQIM). This system consists of two servers: one server manages the inventory sales processes, and the other server provides home delivery services at the doorstep of customers. Based on the Bernoulli schedule, a customer served by the first server may opt for a home delivery service. If any customer chooses the home delivery option, he hands over the purchased item for home delivery and leaves the system immediately. Otherwise, he carries the purchased item and leaves the system. When the delivery server returns to the system after the last home delivery service and finds that there are no items available for delivery, he goes on vacation. Such a vacation of a delivery server is to be interrupted compulsorily or voluntarily, according to the prefixed threshold level. The replenishment process is executed due to the (s,Q) reordering policy. The unique solution of the stationary probability vector to the finite generator matrix is found using recursive substitution and the normalizing condition. The necessary and sufficient system performance measures and the expected total cost of the system are computed. The optimal expected total cost is obtained numerically for all the parameters and shown graphically. The influence of parameters on the expected number of items that need to be delivered, the probability that the delivery server is busy, and the expected rate at which the delivery server's self and compulsory vacation interruptions are also discussed.
ABSTRACT
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.
ABSTRACT
The outbreak of the COVID-19 pandemic disrupted ofur normal life. Many cities enforced a cordon sanitaire as a countermeasure to protect densely inhabited areas. Travelers can only cross the cordon after being checked. To minimize the waiting time in the queue, this paper proposes a method to determine the scientific planning of urban cordon sanitaire for desired queuing time, which is a significant problem that has not been explored. A novel two-stage optimization model is proposed where the first stage is the transportation system equilibrium problem to predict traffic inflow, and the second stage is the queuing network design problem to determine the allocation of test stations. This method aims to minimize the total health infrastructure investment for the desired maximum queuing time. Note that queuing theory is used to represent the queuing phenomenon at each urban entrance. A heuristic algorithm is designed to solve the proposed model where the Method of Successive Averages (MSA) is adopted for the first stage, and the Genetic Algorithm (GA) with elite strategy is adopted for the second stage. An experimental study with sensitivity analysis is conducted to demonstrate the effectiveness of the proposed methods. The results show that the methods can find a good heuristic optimal solution. This research is helpful for policymakers to determine the optimal investment and planning of cordon sanitaire for disease prevention and control, as well as other criminal activities such as drunk driving, terrorists, and smuggling.
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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.
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In recent times, several strategies to minimize the spread of 2019 novel coronavirus disease (COVID-19) have been adopted. Some recent technological breakthroughs like the drone-based tracking systems have focused on devising specific dynamical approaches for administering public mobility and providing early detection of symptomatic patients. In this paper, a smart real-time image processing framework converged with a non-contact thermal temperature screening module was implemented. The proposed framework comprised of three modules v i z . , smart temperature screening system, tracking infection footprint, and monitoring social distancing policies. This was accomplished by employing Histogram of Oriented Gradients (HOG) transformation to identify infection hotspots. Further, Haar Cascade and local binary pattern histogram (LBPH) algorithms were used for real-time facial recognition and enforcing social distancing policies. In order to manage the redundant video frames generated at the local computing device, two holistic models, namely, event-triggered video framing (ETVF) and real-time video framing (RTVF) have been deduced, and their respective processing costs were studied for different arrival rates of the video frame. It was observed that the proposed ETVF approach outperforms the performance of RTVF by providing optimal processing costs resulting from the elimination of redundant data frames. Results corresponding to autocorrelation analysis have been presented for the case study of India pertaining to the number of confirmed COVID-19 cases, recovered cases, and deaths to obtain an understanding of epidemiological spread of the virus. Further, choropleth analysis was performed for indicating the magnitude of COVID-19 spread corresponding to different regions in India.
ABSTRACT
The outbreak of COVID-19 disrupted our everyday life. Many local authorities enforced a cordon sanitaire for the protection of sensitive areas. Travellers can only pass the cordon after tested. This paper aims to propose a method to design an on-ramp control scheme to maximise urban freeway network throughput with a predetermined queuing delay constraint at all off-ramps around cordon sanitaire. A bi-level programming model is formulated where the lower-level is a transportation system equilibrium to predict traffic flow, and the upper-level is onramp metering optimisation that is nonlinear programming. A stochastic queuing model is used to represent the waiting phenomenon at each off-ramp where testing is conducted, and a heuristic algorithm is designed to solve the proposed bi-level model where a method of successive averages (MSA) is adopted for the lower-level model;A genetic algorithm (GA) with elite strategy is adopted for ed to demonstrate the effectiveness of the proposed methramp control for disease control and prevention.
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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.
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Background: In recent years, the rapid development of information and communications technology enabled by innovations in videoconferencing solutions and the emergence of connected medical devices has contributed to expanding the scope of application and expediting the development of telemedicine. Objective: This study evaluates the use of teleconsultations (TCs) for specialist consultations at hospitals in terms of costs, resource consumption, and patient travel time. The key feature of our evaluation framework is the combination of an economic evaluation through a cost analysis and a performance evaluation through a discrete-event simulation (DES) approach. Methods: Three data sets were used to obtain detailed information on the characteristics of patients, characteristics of patients’ residential locations, and usage of telehealth stations. A total of 532 patients who received at least one TC and 18,559 patients who received solely physical consultations (CSs) were included in the initial sample. The TC patients were recruited during a 7-month period (ie, 2020 data) versus 19 months for the CS patients (ie, 2019 and 2020 data). A propensity score matching procedure was applied in the economic evaluation. To identify the best scenarios for reaping the full benefits of TCs, various scenarios depicting different population types and deployment strategies were explored in the DES model. Associated break-even levels were calculated. Results: The results of the cost evaluation reveal a higher cost for the TC group, mainly induced by higher volumes of (tele)consultations per patient and the substantial initial investment required for TC equipment. On average, the total cost per patient over 298 days of follow-up was €356.37 (US $392) per TC patient and €305.18 (US $336) per CS patient. However, the incremental cost of TCs was not statistically significant: €356.37 – €305.18 = €51.19 or US $392 – US $336 = US $56 (95% CI –35.99 to 114.25;P=.18). Sensitivity analysis suggested heterogeneous economic profitability levels within subpopulations and based on the intensity of use of TC solutions. In fact, the DES model results show that TCs could be a cost-saving strategy in some cases, depending on population characteristics, the amortization speed of telehealth equipment, and the locations of telehealth stations. Conclusions: The use of TCs has the potential to lead to a major organizational change in the health care system in the near future. Nevertheless, TC performance is strongly related to the context and deployment strategy involved.
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A pandemic like Covid19 has shifted the paradigm of our daily life. Other than mask and sanitizer, social distancing has become another big point of concern. In this work, we have dealt with social distancing issue in public vehicles. Our aim is to allocate passengers with the vehicles in an optimal manner, maintaining the constraint of social distancing. Our solution is based on the consideration that it is a smart city which allows V2V, V2P and V2I communication in 5G environment. The vehicles and passengers communicate between themselves to perform the allocation. Using the concept of queuing theory, we have tried to model the passengers standing in the bus-stoppage and provide allocation to them based on M/M/1 model. We have also tried to conclude the rate of incoming vehicle in a route so that passengers can get allocation even after maintaining the social distance and vehicles can also get required number of passengers without the wastage of seats. We have implemented our proposed model using omnet++ and SUMO and our simulated result establishes our assumptions based on the queuing theory.
ABSTRACT
The overall aim of this project is to investigate the application of a machine learning method in finding the optimized length of asleep time interval (TAS) in a cyclic sleep mechanism (CSM). Since past decade, the implementations of CSM in the optical network unit (ONU) to reduce the energy consumption in 10 gigabit-passive optical network (XG-PON) were extensively researched. However, the newest era sees the emergence of various network traffic with stringent demands that require further improvements on the TAS selection. Since conventional methods utilize complex algorithm, this paper presents the employment of an artificial neural network (ANN) to facilitate ONU to determine the optimized TAS values using learning from past experiences. Prior to simulation, theoretical analysis was done using the M/G/1 queueing system. The ANN was than trained and tested for the XG-PON network for optimal TAS decisions. Results have shown that towards higher network load, a decreasing TAS trend was observed from both methods. A wider TAS range was recorded from the ANN network as compared to the theoretical values. Therefore, these findings will benefit the network operators to have a flexibility measure in determining the optimal TAS values at current network conditions.
ABSTRACT
The throughput of a finite-capacity queueing system is the mean number of clients served during a time interval. The COVID-19 outbreak has posed a serious challenge for many commercial establishments, including the retails, which have struggled to adapt to new working dynamics. Retails have been forced to adjust their service guidelines to comply with biosecurity protocols, ensuring to observe governmental and public health policies. A significant change for the retail market has been the capacity restrictions to ensure social distancing, i.e., a limitation on the number of customers simultaneously shopping in the store. Such a constraint has an impact on the throughput that can be achieved by a retail. This article assesses the impact of the capacity restriction measures on an Amazon Go-like retail performance through a throughput analysis under COVID-19-related capacity restrictions. For the assessment, we first retrieved real data from a retail located in Cartagena, Colombia. Two scenarios were considered: i) low demand and ii) high demand. Further, we built an Amazon Go-like, two-queue, M/M/c/K retail model with a CONWIP (Constant Work-In-Process) approach, considering biosecurity-based capacity restrictions due to the COVID-19 pandemic. The R package 'queueing' was used to set up the model, and an algorithm was created to go over each sampling period and find the hourly optimum capacity and throughput under the dynamic conditions of both scenarios (low and high demand). Results from the performance analysis show that, for some operational conditions, the optimum maximum throughput is achieved with capacities below the biosecurity-based capacity, while for some other operational conditions the maximum throughput cannot be achieved with the restrictions, as the optimum capacity lies beyond the biosecurity-based capacity. These results suggest that the maximum capacity definition should not be static. Instead, it should be done considering the retail's dimensions, the biosecurity policies, and the dynamic retail's operational conditions such as the demand and service capacity.
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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.
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A multi-server infinite buffer queueing system with additional servers (assistants) providing help to the main servers when they encounter problems is considered as the model of real-world systems with customers’ self-service. Such systems are widely used in many areas of human activity. An arrival flow is assumed to be the novel essential generalization of the known Markov Arrival Process (MAP) to the case of the dynamic dependence of the parameters of the MAP on the rating of the system. The rating is the process defined at any moment by the quality of service of previously arrived customers. The possibilities of a customers immediate departure from the system at the entrance to the system and the buffer due to impatience are taken into account. The system is analyzed via the use of the results for multi-dimensional Markov chains with level-dependent behavior. The transparent stability condition is derived, as well as the expressions for the key performance indicators of the system in terms of the stationary probabilities of the Markov chain. Numerical results are provided.
ABSTRACT
The entire world is spreading of coronavirus-COVID-19 has increased exponentially across the globe, and still, no vaccine is available for the treatment of patients. The crowd has grown tremendously in the hospitals where the facilities are minimal. The queue theory is applied for the Single-server system and its self-similarity existence in a queue used to identify the queue time, waiting time, and Hurst parameter by different patient arrivals methods Health care center in our local area located in Hosapete, Ballari district, Karnataka. Due to more arrivals to the health care center for the identification and confirmation of disease covid-19. This study paper presents a sequential queuing model for estimating infections' detection and identification in severe loading conditions. The goal is to offer a simplified probabilistic model to determine the general behavior to predict how long the treatment cycle will diagnose and classify people already tested and get negative or positive results. For this type of Method, there are some graphical representations of the various measurement criteria. The modelling results showed that the patient's waiting period in the course of inquiries, detections, detecting, or treating COVID-19 in the event of imbalances in the system as a whole rise following the logarithm rule.