Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives

The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm

Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

[EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. The analysis of the articles is carried out in three points: consequences faced by agri-food supply chains due to high risks, strategies applicable in AFSCs, and relationship between resilient strategies and the achievement of sustainability dimensions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events. IFIP Advances in Information and Communication Technology. 598:560-570. https://doi.org/10.1007/978-3-030-62412-5_46S560570598Gray, R.: Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. 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Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19

Pandemics cause chaotic situations in supply chains (SC) around the globe, which can lead towards survivability challenges. The ongoing COVID-19 pandemic is an unprecedented humanitarian crisis that has severely affected global business dynamics. Similar vulnerabilities have been caused by other outbreaks in the past. In these terms, prevention strategies against propagating disruptions require vigilant goal conceptualization and roadmaps. In this respect, there is a need to explore supply chain operation management strategies to overcome the challenges that emerge due to COVID-19-like situations. Therefore, this review is aimed at exploring such challenges and developing strategies for sustainability, and viability perspectives for SCs, through a structured literature review (SLR) approach. Moreover, this study investigated the impacts of previous epidemic outbreaks on SCs, to identify the research objectives, methodological approaches, and implications for SCs. The study also explored the impacts of epidemic outbreaks on the business environment, in terms of effective resource allocation, supply and demand disruptions, and transportation network optimization, through operations management techniques. Furthermore, this article structured a framework that emphasizes the integration of Industry 4.0 technologies, resilience strategies, and sustainability to overcome SC challenges during pandemics. Finally, future research avenues were identified by including a research agenda for experts and practitioners to develop new pathways to get out of the crisis.</jats:p

Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.Comment: 19 page

Resource planning strategies for healthcare systems during a pandemic

We study resource planning strategies, including the integrated healthcare resources’ allocation and sharing as well as patients’ transfer, to improve the response of health systems to massive increases in demand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which mitigates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our optimization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters

Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal–spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min–max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.University of Macau MYRG2016-00069-FSTFDCT Macau FDCT/126/2014/A32018 Guangzhou Science and Technology Innovation and Development of Special Funds201907010001EF003/FST-FSJ/2019/GSTI

A Data-Driven Optimization Model for Medical Resource Allocation during the Pandemic

The outbreak of Covid-19 in recent years has once again brought the critical issue of medical resource allocation during a pandemic to the forefront of research and public attention. The dynamic and rapid nature of the pandemic has posed significant challenges in accurately predicting the demands for medical resources and developing effective strategies for their distribution. In this study, we aim to address these challenges by studying the medical resource allocation problem during a pandemic and proposing a data-driven optimization methodology that combines mathematical programming and machine learning techniques. To tackle the problem of demand prediction, we utilize a Long Short-Term Memory(LSTM) model to predict medical resource demand using historical pandemic time series data. Building upon the demand predictions, we develop a linear programming model to optimize the allocation of medical resources. The objective is to maximize the total accessibility of hospitals within each region while also ensuring a balanced distribution of accessibility across all regions. We also conducted a case study on the application of this framework to the Quebec, Canada, pandemic hospitalization case scenarios. The dataset we utilized consisted of hospitalization case numbers from 16 regions in Quebec, along with the geographical locations of 15 regions and their corresponding healthcare facilities. The prediction performance is evaluated by mean absolute error(MAE) and root mean square error(RMSE), which yielded average values of 3.079 and 5.491, respectively. And after optimizing, the total accessibility of all regions is 4.503. The results indicate the effectiveness of our proposed method in accurately predicting future hospitalization numbers and determining the necessary increase in bed capacity for each region, showcasing its potential to assist in resource planning and allocation during a pandemic