Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PINN training using biobjective optimization: The trade-off between data loss and residual loss

Physics informed neural networks (PINNs) have proven to be an efficient tool to represent problems for which measured data are available and for which the dynamics in the data are expected to follow some physical laws. In this paper, we suggest a multiobjective perspective on the training of PINNs by treating the data loss and the residual loss as two individual objective functions in a truly biobjective optimization approach. As a showcase example, we consider COVID-19 predictions in Germany and built an extended susceptibles-infected-recovered (SIR) model with additionally considered leaky-vaccinated and hospitalized populations (SVIHR model) to model the transition rates and to predict future infections. SIR-type models are expressed by systems of ordinary differential equations (ODEs). We investigate the suitability of the generated PINN for COVID-19 predictions and compare the resulting predicted curves with those obtained by applying the method of non-standard finite differences to the system of ODEs and initial data. The approach is applicable to various systems of ODEs that define dynamical regimes. Those regimes do not need to be SIR-type models, and the corresponding underlying data sets do not have to be associated with COVID-19.

Resilient supply chain network design without lagging sustainability responsibilities

In the 21st century, global supply chains have experienced severe risks due to disruptions caused by crises and serious diseases, such as the great tsunami, SARS, and, more recently, COVID-19. Building a resilient supply chain is necessary for business survival and growth. Similarly, there is increasing regulatory and social pressure for managers to continuously design and implement sustainable supply chain networks, encompassing economic, social, and environmental components. Hence, a panacea approach is required to establish a compromise position between resiliency concerns and sustainability responsibilities. To address this, this work presents a hybrid integrated BWM-CoCoSo-multi-objective programming model (BC-MOPM) formulated to deliver a compromise between resilience and sustainability supply chain network design (RS-SCND). First, a thorough literature review analysis is conducted to explore the relationship and correlation between resilience and sustainability to develop a framework for the resiliency and sustainability criteria, in a supply chain context. Second, four objectives were formulated, including the minimisation of total cost and environmental impact and the maximisation of social and resilience paradigms. A real two-tier supply chain network is deployed to evaluate the applicability of the developed BC-MOPM. Furthermore, sensitivity analysis is conducted to establish the relative importance of the identified criteria to prove the model's robustness. Results demonstrate the capability of the BC-MOPM in revealing trade-offs between the resiliency and sustainability aspects. © 2023 Elsevier B.V.

Multi-objective Harris Hawks Optimization for Optimal Emergency Vehicle Dispatching During a Pandemic

Harris Hawks Optimization (HHO) is a Swarm Intelligence (SI) algorithm that is inspired by the cooperative behavior and hunting style of Harris Hawks in the nature. Researchers' interest in HHO is increasing day by day because it has global search capability, fast convergence speed and strong robustness. On the other hand, Emergency Vehicle Dispatching (EVD) is a complex task that requires exponential time to choose the right emergency vehicles to deploy, especially during pandemics like COVID-19. Therefore, in this work we propose to model the EVD problem as a multi-objective optimization problem where a potential solution is an allocation of patients to ambulances and the objective is to minimize the travelling cost while maximizing early treatment of critical patients. We also propose to use HHO to determine the best allocation within a reasonable amount of time. We evaluate our proposed HHO for EVD using 2 synthetic datasets. We compare the results of the proposed approach with those obtained using a modified version of Particle Swarm Optimization (PSO). The experimental analysis shows that the proposed multi-objective HHO for EVD is very competitive and gives a substantial improvement over the enhanced PSO algorithm in terms of performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device's impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method's efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network. Copyright © 2023 Jitendra Singh Bhadoriya et al.

A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Designing Fog Device Network for Digitization of University Campus

With the evolving digitization, services of Cloud and Fog make things easier which is offered in form of storage, computing, networking etc. The importance of digitalization has been realized severely with the home isolation due to COVID-19 pandemic. Researchers have suggested on planning and designing the network of Fog devices to offer services nearby the edge devices. In this work, Fog device network design is proposed for a university campus by formulating a mathematical model. This formulation is used to find the optimal location for the Fog device placement and interconnection between Fog devices and the Cloud (Centralized Information Storage). The proposed model minimizes the deployment cost and the network traffic towards Cloud. The IBM CPLEX optimization tool is used to evaluate the proposed multi-objective optimization problem. Classical multi-objective optimization method, i.e., Weighted Sum approach is used for the purpose. The experimental results exhibit optimal placement of Fog devices with minimum deployment cost. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prognostic Assessment of COVID-19 Vaccination Levels

In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the "demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage. © 2022 Elsevier Ltd

Performance Measurement of the Sustainable Supply Chain During the COVID-19 Pandemic: A real-life case study

This paper aims to introduce a framework to measure the sustainable performance of the supply chain (SC) during the COVID-19 pandemic. The SC stakeholders in this investigation are Suppliers, Production / Remanufacturing / Refurbishing Centers (Factories), Collection / Distribution Centers, Recycling / Landfill Centers, and Customers. The suggested sustainable supply chain (SSC) performance measurement included three pillars with 23 indicators. To evaluate the overall sustainability of the SC understudy, a composite index has been developed that combines all the indicators to reflect the sustainability performance of the SC. Four steps are involved in creating a composite index:1) measuring the value of indicators, 2) weighing indicators, 3) Using the normalization technique, and 4) Evaluating the overall SSC indicator. The real case in Iran is selected as an illustrative case. Our research contributions are: We suggested a novelty indicator of SSC to better show the economic, environmental, and social tradeoffs during the COVID-19 pandemic and lockdowns. We have found and measured the negative and positive impacts of COVID-19 on aspects of sustainability in SC. Based on the achieved data of the real case study, a numerical example is represented to explain how to calculate the composite index. The main contribution of this paper is the development of SSC indicators during the COVID-19 epidemic.

Decision Support System for Student Selection Recipients of COVID-19 Impacts using MOORA

SMPN 174 Jakarta is a junior high school in East Jakarta that has 719 students and 22 classrooms, including being one of the schools with government programs in the form of assistance such as Smart Jakarta Card, Smart Indonesia Program, and School Operational Assistance. To meet educational needs and distribute to students who are entitled to assistance. Currently, the selection process for student beneficiaries at SMPN 174 Jakarta is still done manually, so there is a risk of making decisions that are not on target and require a longer time. Therefore, a decision support system was made to select students who receive assistance. The development of the decision support system used in this research is the Multi-Objective Optimization Method on the Basic of Ratio (MOORA). The tests include black-box testing and comparison testing of calculation results with manual calculations. The Decision Support System for Selection of Student Recipients for the Impact of COVID-19 has been successfully created, and the results of selecting recipients of this assistance are obtained based on the calculation of the highest optimization value. The verifier has approved them with a percentage of 66.7% strongly agreeing and 33.3% agreeing. © 2022 IEEE.

Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation

The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Enhanced Binary Multi-Objective Hybrid Filter-Wrapper Chimp Optimization based Feature Selection Method for COVID-19 Patient Health Prediction

This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer’s search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts: modifying the population using HHO to produce an HHO-based population;creating hybrid entities according to HHO-based and ChOA-based individuals;and altering the search agent in the light of greedy technique and ChOA’s tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients. Author

Prognostic Assessment of COVID-19 Vaccination Levels

In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mid-semester Pandemic-driven Course Rescheduling with Integer Programming

As universities begin the return to in-person course work, uncertainty remains about the future of SARS-CoV-2 virus and its variants. In the years to come, other novel pathogens may emerge. Pandemic-driven social distancing requirements reduce the number of students in classrooms, and when these requirements are instituted mid-semester, universities must make quick changes to classroom assignments and course delivery mode. In this work, we introduce two integer programs to optimize mid-semester changes: (i) a conflict-matrix-based model that determines new classroom capacities and designs the corresponding seat map, and (ii) a hierarchical model that optimizes room assignment and course delivery mode according to prioritized objectives. We test our methods with University of Michigan's engineering course schedule for Fall 2021, under a hypothetical 3-foot social distancing requirement. We compare the performance of the models under different hierarchical objectives and room assignment assumptions and discuss the managerial implications of our results. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization

The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the highest probability of survival in critical clinical situations. Motivated by this, a battery of mortality prediction models with different performances has been developed to assist physicians and hospital managers. Logistic regression, one of the most popular classifiers within the clinical field, has been chosen as the basis for the generation of our models. Whilst a standard logistic regression only learns a single model focusing on improving accuracy, we propose to extend the possibilities of logistic regression by focusing on sensitivity and specificity. Hence, the log-likelihood function, used to calculate the coefficients in the logistic model, is split into two objective functions: one representing the survivors and the other for the deceased class. A multi-objective optimization process is undertaken on both functions in order to find the Pareto set, composed of models not improved by another model in both objective functions simultaneously. The individual optimization of either sensitivity (deceased patients) or specificity (survivors) criteria may be conflicting objectives because the improvement of one can imply the worsening of the other. Nonetheless, this conflict guarantees the output of a battery of diverse prediction models. Furthermore, a specific methodology for the evaluation of the Pareto models is proposed. As a result, a battery of COVID-19 mortality prediction models is obtained to assist physicians in decision-making for specific epidemiological situations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prediction-driven collaborative emergency medical resource allocation with deep learning and optimization

This study addresses two key issues, ie, the “cold-start problem” in transmission prediction of new or rare epidemics and the collaborative allocation of emergency medical resources considering multiple objectives. These two issues have not yet been well addressed in data-driven emergency medical resource allocation systems. A decision support prediction-then-optimization framework combing deep learning and optimization is developed to address these two issues. Two transfer learning based convolutional neural network models are built for epidemic transmission predictions in the initial and the subsequent outbreak regions using transfer learning to deal with the “cold-start problem”. A prediction-driven collaborative emergency medical resource allocation model is built to address the issue of collaborative decisions by simultaneously considering the inter- and intra-echelon resource flows in a multi-echelon system and considering the efficiency and fairness as the objective functions. A case study of the COVID-19 pandemic shows that combining transfer learning and convolutional neural networks can improve the performances of epidemic transmission predictions, and good predictions can improve both the efficiency and fairness of emergency medical resource allocation decisions. Moreover, the computational results show that the prediction errors are asymmetrically amplified in the optimization stage, and the shortage of the resource reserve quantity mediates the asymmetrical amplification effect. © Operational Research Society 2022.

Eco-economics Approach for Non-infectious Medical Waste Emergency Disposal During the COVID-19 Pandemic

After the outbreak of COVID-19, it spread rapidly all over the world. A large number of infected patients have led to a sharp increase in medical waste. This puts great pressure on the medical waste treatment system. The disposal capacity of the system may not be able to meet such a large amount of medical waste, which may lead to delayed treatment of infectious medical waste (IMW) and accumulation of non-infectious medical waste (NMW). Therefore, this paper proposes that it is necessary to classify IMW and NMW, and reconstruct the domestic waste disposal plant to treat NMW to alleviate the pressure of the treatment system. This paper also establishes an eco-economics model to optimize the emergency disposal scheme. The effectiveness of the model is verified by a real case in Wuhan. It is found that the location and carbon emission coefficient of the domestic waste disposal plants are the key factors affecting its selection. At the same time, sufficient budget may lead to waste money. In addition, carbon emission and total cost always change in the opposite trend when the budget changes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Customer Prioritization Integrated Supply Chain Optimization Model with Outsourcing Strategies.

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains' dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains.