A Tailored Meta-Heuristic for the Autonomous Electric Vehicle Routing Problem Considering the Mixed Fleet

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.

Studying of COVID-19 fractional model: Stability analysis.

This article focuses on the recent epidemic caused by COVID-19 and takes into account several measures that have been taken by governments, including complete closure, media coverage, and attention to public hygiene. It is well known that mathematical models in epidemiology have helped determine the best strategies for disease control. This motivates us to construct a fractional mathematical model that includes quarantine categories as well as government sanctions. In this article, we prove the existence and uniqueness of positive bounded solutions for the suggested model. Also, we investigate the stability of the disease-free and endemic equilibriums by using the basic reproduction number (BRN). Moreover, we investigate the stability of the considering model in the sense of Ulam-Hyers criteria. To underpin and demonstrate this study, we provide a numerical simulation, whose results are consistent with the analysis presented in this article.

A New Extension of the Topp–Leone-Family of Models with Applications to Real Data

In this article, we proposed a new extension of the Topp–Leone family of distributions. Some important properties of the model are developed, such as quantile function, stochastic ordering, model series representation, moments, stress–strength reliability parameter, Renyi entropy, order statistics, and moment of residual life. A particular member called new extended Topp–Leone exponential (NETLE) is discussed. Maximum likelihood estimation (MLE), least-square estimation (LSE), and percentile estimation (PE) are used for the model parameter estimation. Simulation studies were conducted using NETLE to assess the MLE, LSE, and PE performance by examining their bias and mean square error (MSE), and the result was satisfactory. Finally, the applications of the NETLE to two real data sets are provided to illustrate the importance of the NETLG families in practice;the data sets consist of daily new deaths due to COVID-19 in California and New Jersey, USA. The new model outperformed many other existing Topp–Leone's and exponential related distributions based on the real data illustrations.

Statistical modelling of the impact of online courses in higher education on sustainable development

Purpose>The concept of sustainable development (SD) is a popular response to society's need to preserve and extend the life span of natural resources. One of the 17 goals of the SD is "education quality” (Fourth Goal of Sustainable Development [SDG-4]). Education quality is an important goal because education is a powerful force that can influence social policies and social change. The SDG-4 must be measured in different contexts, and the tools to quantify its effects require exploration. So, this study aims to propose a statistical model to measure the impact of higher education online courses on SD and a structural equation model (SEM) to find constructs or factors that help us explain a sustainability benefits rate. These proposed models integrate the three areas of sustainability: social, economic and environmental.Design/methodology/approach>A beta regression model suggests features that include the academic and economic opportunities offered by the institution, the involvement in research activities and the quality of the online courses. A structural equation modelling (SEM) analysis allowed selecting the key variables and constructs that are strongly linked to the SD.Findings>One of the key findings showed that the benefit provided by online courses in terms of SD is 62.99% higher than that of offline courses in aspects such as transportation, photocopies, printouts, books, food, clothing, enrolment fees and connectivity.Research limitations/implications>The SEM model needs large sample sizes to have consistent estimations. Thus, despite the obtained estimations in the proposed SEM model being reliable, the authors consider that a limitation of this study was the required time to collect data corresponding to the estimated sample size.Originality/value>This study proposes two novel and different ways to estimate the sustainability benefits rate focused on SDG-4, and machine learning tools are implemented to validate and gain robustness in the estimations of the beta model. Additionally, the SEM model allows us to identify new constructs associated with SDG-4.

Developing Supply Chain Capabilities Through Digitalization and Viability for Controlling the Ripple Effect

The COVID-19 pandemic affected all industries and presented manufacturing firms with enormous challenges, with considerable changes in consumer demand for goods and services. Supply chain management disruption caused by the COVID-19 outbreak resulted in several socio-economic roadblocks. The slow propagation of disruption risk results in a ripple effect along the entire chain. The lack of resilience and risk management capability is the prime cause, attributed to the unavailability of digital resources, skills, and knowledge. The main objective of this article is to develop supply chain capability for disruption risk management and supply chain resilience for competitive gain in terms of controlling the ripple effect. The resource-based view approach was used to develop the theoretical structure in this article. Supply chain digitalization and viability provide necessary resources to develop the capability for managing risk and resilience to tackle the impact of disruptions due to pandemics, war, recession, and other such massive challenges on the supply chain. Seven hypotheses were proposed and evaluated for relevance using structural equation modeling (SEM). In total, 199 valid responses to a survey on SEM were gathered and examined using the AMOS V-21 software. Our research findings supported all the proposed hypotheses, thereby generating positive theoretical evidence for practitioners to digitalize their supply chain for enhanced supply chain capabilities and effective control of the ripple effect. IEEE

Complex dynamics of COVID-19 mathematical model on Erdős–Rényi network

In this study, a conformable fractional order Lotka–Volterra predator-prey model that describes the COVID-19 dynamics is considered. By using a piecewise constant approximation, a discretization method, which transforms the conformable fractional-order differential equation into a difference equation, is introduced. Algebraic conditions for ensuring the stability of the equilibrium points of the discrete system are determined by using Schur–Cohn criterion. Bifurcation analysis shows that the discrete system exhibits Neimark–Sacker bifurcation around the positive equilibrium point with respect to changing the parameter d and e. Maximum Lyapunov exponents show the complex dynamics of the discrete model. In addition, the COVID-19 mathematical model consisting of healthy and infected populations is also studied on the Erdős Rényi network. If the coupling strength reaches the critical value, then transition from nonchaotic to chaotic state is observed in complex dynamical networks. Finally, it has been observed that the dynamical network tends to exhibit chaotic behavior earlier when the number of nodes and edges increases. All these theoretical results are interpreted biologically and supported by numerical simulations. [ FROM AUTHOR]

Developing a solution algorithm for the autonomous electric vehicle routing problem considering the mixed fleet

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. The arrival and departure times of the AEV at the customer’s location must be pre-planned, because, the AEV is not able to decide what to do if the customer is late at this point. Also, due to increasing the security of the loads inside the AEVs and the lack of control of the driver during the delivery of the goods, each customer should only have access to his/her orders. Therefore, the compartmentation of the AEV’s loading area has been proposed in its conceptual model. We developed a mathematical model based on these properties and 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. Author

Personal Learning Environments: Modeling Students' Self-Regulation Enhancement through a Learning Management System Platform

A personal learning environment (PLE) is known as a crucial support for educators who lead learners through the process of collection, creation, and organization of personalized learning tools. In this manner, the learner can interpret a variety of new tools in their own interest, which makes the learning process easier. The PLE approach represents a considerable movement away from traditional learning, where learners are considered consumers of information through isolated channels, particularly learning management systems (LMSs), to a model where learners draw significant connections from numerous resources that they choose. Thus, educational settings have implemented LMSs fully into their respective learning contexts. In this sense, LMS is identified as a learning platform that helps learners and educators submit assignments, share ideas, and communicate through web-based systems with numerous benefits. Under these circumstances, self-regulation is addressed as a significant component that explains how learners build and manage PLEs and come up with more choices;they take ownership of their own learning and enhance self-regulated learning (SRL) practices. On this occasion, there is a belief that teachers can utilize LMSs to shift from passive to active learning and to improve self-reflection (SR). Therefore, considering all the above issues, the current study examines integrating a third-generation LMS to enhance learners’SR. This study considered PLEs by utilizing Zimmerman’s SRL model to investigate the integration of the third-generation LMS. SR is applied in this study in the form of a pretest and posttest following the involvement of the PLE course, which was designed and applied during the COVID-19 pandemic. Finally, the experimental findings of the current study formulated a model of SR factors in PLEs through the LMS platform with partial least squares structural equation modeling (SEM) before and after the intervention. Author

Toward masked face recognition: An effective facial feature extraction and refinement model in multiple scenes

With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks.

On constrained smoothing and out-of-range prediction using [formula omitted]-splines: A conic optimization approach

• We study the problem of estimating smooth curves which verify structural properties. • We propose a mathematical optimization formulation to build constrained P-splines. • An open-source Python library is developed: cpsplines. • We estimate constrained curves in simulated, COVID-19 and demographic data. Decision-making is often based on the analysis of complex and evolving data. Thus, having systems which allow to incorporate human knowledge and provide valuable support to the decider becomes crucial. In this work, statistical modelling and mathematical optimization paradigms merge to address the problem of estimating smooth curves which verify structural properties, both in the observed domain in which data have been gathered and outwards. We assume that the curve to be estimated is defined through a reduced-rank basis (B -splines) and fitted via a penalized splines approach (P -splines). To incorporate requirements about the sign, monotonicity and curvature in the fitting procedure, a conic programming approach is developed which, for the first time, successfully conveys out-of-range constrained prediction. In summary, the contributions of this paper are fourfold: first, a mathematical optimization formulation for the estimation of non-negative P-splines is proposed;second, previous results are generalized to the out-of-range prediction framework;third, these approaches are extended to other shape constraints and to multiple curves fitting;and fourth, an open source Python library is developed: cpsplines. We use simulated instances, data of the evolution of the COVID-19 pandemic and of mortality rates for different age groups to test our approaches. [ FROM AUTHOR]