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As a result of a wide variety of risks, diverse in scale, complexity, and consequences, countries must generate various strategies through which they can face them, protecting the population, the ecosystem, and the economy. The objective of the document to be presented is to help health organizations in decision-making in logistical aspects, specifically, in the location of facilities and distribution of supplies. Thiswork proposes a logistic model that allows the location of a feasible municipality through the integration of the classic p-median problem and the Multiple Vehicle Routing Problem (MVRP). The goal is to determine a feasible location to establish a warehouse and the routes to supply Personal protection supplements for the health sector personnel to municipalities that host hospitals of different public institutions with COVID-19 patients. The model is evaluated in one of the states belonging to Mexico, making a typification of its municipalities. The results are obtained in four scenarios, showing both the host municipalities and the delivery routes. The results showed the municipalities of Cuitláhuac, Huiloapan de Cuauhtémoc, Huatusco, and Tlalixcoyan as feasible locations for the warehouse. From the information provided and through the vehicle routing problem with time windows (VRPTW), new delivery routes are established, showing a comparison of results. The total of established routes for delivery is seven. Due to the characteristics of the content, this research falls into the classification of case studies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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This paper leverages online content to investigate the biggest impact of COVID-19 - remote work, by using China as a primary case study. Telecommuting has become popular since February 2020 primarily due to the pandemic, and people have been slowly returning to their office from May 2020. This study focuses on two time windows in the year 2020 to calculate the growth of different job sectors. Our results indicate the negative impact of teleworking in manufacturing industry, but shows that information technology-related industries are less affected by working from home. This paper also investigates the impact of COVID-19 on the stock market and discussed what plan of action the policy makers should take to provide a good economic environment for the country. In addition to the overall economic situation, we observed how the psychological situation of employees could affect their job performance, indirectly affecting the development of certain industry sectors. Therefore, misinformation in certain Chinese social media channels was also studied in this paper specifically examining the rumors and their latent topics. We believe that our work will initiate a dialogue between scientists, policy makers and government officials to consider the observations highlighted in this paper. © 2022 IEEE Computer Society. All rights reserved.
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The COVID-19 pandemic has been characterized by the high number of infected cases due to its rapid spread around the world, with more than 6 million of deaths. Given that we are all at risk of acquiring this disease and that vaccines do not completely stop its spread, it is necessary to continue proposing tools that help mitigate it. This is the reason why it is ideal to develop a method for early detection of the disease, for which this work uses the Stanford University database to classify patients with SARS-CoV-2, also commonly called as COVID-19, and healthy ones. In order to do that we used a densely connected neural network on a total of 77 statistical features, including permutation entropy, that were contrasted from two different time windows, extracted from the heart rate of 24 COVID patients and 24 healthy people. The results of the classification process reached an accuracy of 86.67% and 100% of precision with the additional parameters of recall and F1-score being 80% and 88.89% respectively. Finally, from the ROC curve for this classification model it could be calculated an AUC of 0.982. © 2022 Creative Commons.
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Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions;however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism. © 2022 The Author(s)
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In recent years, some phenomena such as the COVID-19 pandemic have caused the autonomous vehicle (AV) to attract much attention in theoretical and applied research. This paper addresses the optimization problem of a heterogeneous fleet that consists of autonomous electric vehicles (AEVs) and conventional vehicles (CVs) in a Business-to-Consumer (B2C) distribution system. The absence of the driver in AEVs results in the necessity of studying two factors in modeling the problem, namely time windows in the routing plan and different compartments in the loading space of AEVs. We developed a mathematical model based on these properties, that was NP-hard. Then we proposed a hybrid algorithm, including variable neighborhood search (VNS) via neighborhood structure of large neighborhood search (LNS), namely the VLNS algorithm. The numerical results shed light on the proficiency of the algorithm in terms of solution time and solution quality. In addition, employing AEVs in the mixed fleet is considered to be desirable based on the operational cost of the fleet. The numerical results show the operational cost in the mixed fleet decreases on average by 57.22% compared with the homogeneous fleet.
ABSTRACT
The COVID-19 pandemic has presented tremendous challenges to the world, one of which is the management of infectious waste generated by healthcare activities. Finding cost-efficient services with minimum threats to public health has become a top priority. The pandemic has induced extreme uncertainties, not only in the amount of generated waste, but also in the associated service times. With this in mind, the present study develops a mixed-integer linear programming (MILP) model for the location-routing problem with time windows (LRPTW). To handle the uncertainty in the amount of generated waste, three scenarios are defined respectively reflecting different severity levels of a pandemic. Furthermore, chance constraints are applied to deal with the variation of the service times at small generation nodes, and time windows at the transfer facilities. The complexity of the resulting mathematical model motivated the application of a branch-and-price (B&P) algorithm along with an É -constraint technique. A case study of the situation of Wuhan, China, during the initial COVID-19 outbreak is employed to examine the performance and applicability of the proposed model. Our numerical tests indicate that the B&P algorithm outperforms CPLEX in the computational times by more than 83% in small-sized problem instances and reduces the gaps by at least 70% in large-scale ones. Through a comparison with the current and deterministic systems, our proposed stochastic system can timely adjust itself to fulfill nearly four times the demand of other systems in an extreme pandemic scenario, while maintaining a cost-efficient operation with no outbreak.
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Compartmental pandemic models have become a significant tool in the battle against disease outbreaks. Despite this, pandemic models sometimes require extensive modification to accurately reflect the actual epidemic condition. The Susceptible-Infectious-Removed (SIR) model, in particular, contains two primary parameters: the infectious rate parameter ß and the removal rate parameter y, in addition to additional unknowns such as the initial infectious population. Adding to the complexity, there is an obvious challenge to track the evolution of these parameters, especially ß and y, over time which leads to the estimation of the reproduction number for the particular time window, RT. This reproduction number may provide better understanding on the effectiveness of isolation or control measures. The changing RT values (evolving over time window) will lead to even more possible parameter scenarios. Given the present Coronavirus Disease 2019 (COVID-19) pandemic, a stochastic optimization strategy is proposed to fit the model on the basis of parameter changes over time. Solutions are encoded to reflect the changing parameters of ßT and γt, allowing the changing RT to be estimated. In our approach, an Adaptive Differential Evolution (ADE) and Particle Swarm Optimization (PSO) are used to fit the curves into previously recorded data. ADE eliminates the need to tune the parameters of the Differential Evolution (DE) to balance the exploitation and exploration in the solution space. Results show that the proposed optimized model can generally fit the curves well albeit high variance in the solutions. © 2020 Tsinghua University Press.
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Online social networks (OSNs) are today a primary way to spread and consume information. Maybe the most important aspect of OSNs, both an opportunity and a weakness, is that OSNs are open: users can post anything, which leads to proliferation of information with various degrees of truthfulness. This impacts the volume of information, trending topics, and sentiment of users vis-à-vis of these topics. Our goal in this work is to analyze the spreading of information in Twitter, volume-wise and sentiment-wise (positive or negative), for COVID-19 vaccines overall, and for some specific brands. Our analysis was carried on over five 10-day time-windows in 2021, starting from February and until October. We also looked at what were the most popular tweets we collected during our predefined time-windows, and, by looking at the retweets counts, we observed how they trended over time. © 2022 ACM.
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Waste collection management plays a crucial role in controlling pandemic outbreaks. Electric waste collection systems and vehicles can improve the efficiency and effectiveness of sanitary processes in municipalities worldwide. The waste collection routing optimization involves designing routes to serve all customers with the least number of vehicles, total traveling distance, and time considering the vehicle capacity. This paper proposes a dynamic location-arc routing optimization model for electric waste collection vehicles. The proposed model suggests an optimal routing plan for the waste collection vehicles and determines the optimal locations of the charging stations, dynamic charging arcs, and waste collection centers. A genetic algorithm and grey wolf optimizer are used to solve the large-sized random generated NP-hard location-arc routing problems. We present a case study for the city of Edmonton in Canada and show the grey wolf optimizer outperforms the genetic algorithm. We further demonstrate the total number of waste collection centers, charging stations, and arcs for dynamic charging needed to ensure a minimum required service for electric vehicles throughout Edmonton's entire waste collection system.
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The Covid-19 pandemic has made a huge impact on the world. Vaccines are regarded as the universal solution to mitigate the spread of the pandemic. Vaccination programs have been initiated by all countries in the past one year or so. The public opinion about vaccinations has been dynamically changing during this period. We intend to track the perception of the masses since the arrival of the vaccines, through social media posts, and reflect on the reasons behind the dynamically evolving ideas of people. For this purpose, we propose the use of Latent Dirichlet Allocation (LDA) for topic modeling from vaccine-related discussions on the popular social media platform Twitter, in five temporal phases, in the duration of 20 December 2020 to 16 October 2021. The time windows are determined such that the tweets are equally distributed in each time slice. The ten most relevant terms in the top-10 topics in each time window are determined and presented in the form of bar charts. The relevancy of a term is interpreted as the sum of probabilistic scores associated with that term in the top-10 topics identified by LDA in a particular time period. The bar charts are further analyzed for inferring the topics of discussion in a particular phase of time. © 2022 ACM.
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The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so that the delivery process can be completed without interrupting the cold chain. Therefore, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the attended home delivery (AHD) problem in the scientific literature. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, the underlying optimization problem is a vehicle routing problem with a time windows. This work is concerned with a case given by an international grocery retailer’s online shopping service. We present an analysis of several efficient solution methods that can be employed to AHD services. A framework for the operational planning tools required to tackle the order placement process is provided. However, the basic framework can easily be adapted to be used for many similar vehicle routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing local search operations and mixed-integer linear programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches.
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With the rapid growth in e-commerce and virtual markets due to the COVID-19 pandemic, customer satisfaction due to logistics activities has become more imperative than ever. The Traveling Repairman Problem (TRP), which is also known as the cumulative traveling salesman problem, the deliveryman problem and the minimum latency problem, is a special variant of the Traveling Salesman Problem (TSP). All these problems differ in their objective function criteria. In TSP, the total cost (distance or time) of the salesman is minimized. In the case of the TRP, the total latency (waiting time or delay time) of all customers is minimized. Ultimately, with TRP, customer satisfaction is maximized through minimizing the total latency. This paper focuses on a generalized version of TRP with multi depots and time windows, namely Multi Depot Traveling Repairman Problem with Time Windows (MDTRPTW). A group of homogeneous repairmen initiate and finish their visit tours at depots. Each customer must be visited exactly by one repairman within their provided earliest end latest times, defined as their time windows. To the best of our knowledge, there is no study in the literature on this problem. In this paper, in addition to posing the problem, we propose a mixed integer programming model for MDTRPTW with O(n2) integer decision variables and O(n2) constraints. MDTRPTW is an NP-hard challenging combinatorial optimization problem. In order to find near optimal solutions within a reasonable computational time for realistic sized dimensions, we also propose a biogeography-based optimization algorithm as a metaheuristic approach. The performance of our formulation and metaheuristic are analyzed by solving instances with time windows from other problems in the literature that are adapted for MDTRPTW. We observe that our proposed formulation is able to solve small and moderate size problems in reasonable times. The efficacy of the metaheuristic solution approach is evaluated in terms of solution quality as well as computation time. The developed metaheuristic approach is able to solve problems with 300 customers within seconds. Moreover, when contrasted with the exact solution methodology, the proposed metaheuristic algorithm represents a high performance to find good quality solutions within acceptable CPU times for large-size problems. The main contribution of this paper is to define and to present a mathematical model for the multi depot Traveling Repairman Problem with time windows. In addition, to propose a metaheuristic approach for this problem. © IEOM Society International.
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In this period of the COVID-19 pandemic, it is critical to adjust the resources found in multiple fields regarding logistical challenges to ensure the minimization of transportation costs and maximization of patient’s comforts and preferences in-home health care. In this article, we will address the vehicle routing problem with time windows, priority, synchronization and lunch break constraints, all combined with scheduling and planning. Often, patients who no longer need to stay in the hospital must give way to another urgent case, but need more treatment than they can receive at home. Caregivers are assigned to provide special care to needy patients based on their requests within time constraints such as time windows, patient preferences, precedence, and synchronization. This paper aims to optimize the planning and scheduling of home care efficiently with consideration and respect to the criterion such as qualification of caregivers, availability and preferences of the patients. To resolve this problem, an Artificial Immune Algorithm (AIS) is proposed as a generator of routes, and a multi-agent approach is established to guarantee the upmost coordination and communication among the overall actors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.
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This work addresses the spread of the coronavirus through a non-parametric approach, with the aim of identifying communities of countries based on how similar their evolution of the disease is. The analysis focuses on the number of daily new COVID-19 cases per ten thousand people during a period covering at least 250 days after the confirmation of the tenth case. Dynamic analysis is performed by constructing Minimal Spanning Trees (MST) and identifying groups of similarity in contagions evolution in 95 time windows of a 150-day amplitude that moves one day at a time. The intensity measure considered was the number of times countries belonged to a similar performance group in constructed time windows. Groups’ composition is not stable, indicating that the COVID-19 evolution needs to be treated as a dynamic problem in the context of complex systems. Three communities were identified by applying the Louvain algorithm. Identified communities analysis according to each country’s socioeconomic characteristics and variables related to the disease sheds light on whether there is any suggested course of action. Even when strong testing and tracing cases policies may be related with a more stable dynamic of the disease, results indicate that communities are conformed by countries with diverse characteristics. The best option to counteract the harmful effects of a pandemic may be having strong health systems in place, with contingent capacity to deal with unforeseen events and available resources capable of a rapid expansion of its capacity. © 2022