Machine learning in drug supply chain management during disease outbreaks: a systematic review

The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks

A Snowmelt Optimization Algorithm Applied to Green Low Carbon Logistics Pathways Optimization Problems

INTRODUCTION: Efficient and accurate optimization of green and low-carbon logistics paths, as one of the key technologies of green and low-carbon logistics, can not only promote the high-quality development of the economy, but also reduce the negative impacts of logistics on the environment and increase the cost of logistics delivery. OBJECTIVES: To address the problems of slow convergence and easy to fall into local optimization in the current performance prediction research on talent team building. METHODS: This paper proposes a snowmelt heuristic optimization algorithm to solve the green low-carbon logistics path optimization problem. Firstly, the objective function of green low-carbon logistics path optimization is designed by analyzing the optimization cost and conditional constraints of the green low-carbon logistics path optimization problem; then, a method based on intelligent optimization algorithm is proposed by designing the position-order array coding and fitness function, combined with the snow-melting optimization algorithm; finally, the validity and superiority of the proposed method are verified by simulation experiments. RESULTS: The results show that the proposed method not only improves the convergence speed but also increases the optimization fitness value. Conclusion: The problem of slow convergence and easy to fall into local optimum in the solution of green low-carbon logistics path optimization problem is solved

SIMULATION-BASED OPTIMIZATION OF TRANSPORTATION SYSTEMS: THEORY, SURROGATE MODELS, AND APPLICATIONS

The construction of new highway infrastructure has not kept pace with the growth of travel, mainly due to the limitation of land and funding availability. To improve the mobility, safety, reliability and sustainability of the transportation system, various transportation planning and traffic operations policies have been developed in the past few decades. On the other hand, simulation is widely used to evaluate the impacts of those policies, due to its advantages in capturing network and behavior details and capability of analyzing various combinations of policies. A simulation-based optimization (SBO) method, which combines the strength of simulation evaluation and mathematical optimization, is imperative for supporting decision making in practice. The objective of this dissertation is to develop SBO methods that can be efficiently applied to transportation planning and operations problems. Surrogate-based methods are selected as the research focus after reviewing various existing SBO methods. A systematic framework for applying the surrogate-based optimization methods in transportation research is then developed. The performance of different forms of surrogate models is compared through a numerical example, and regressing Kriging is identified as the best model in approximating the unknown response surface when no information regarding the simulation noise is available. Accompanied with an expected improvement global infill strategy, regressing Kriging is successfully applied in a real world application of optimizing the dynamic pricing for a toll road in the Inter-County Connector (ICC) regional network in the State of Maryland. To further explore its capability in dealing with problems that are of more interest to planners and operators of the transportation system, this method is then extended to solve constrained and multi-objective optimization problems. Due to the observation of heteroscedasticity in transportation simulation outputs, two surrogate models that can be adapted for heteroscedastic data are developed: a heteroscedastic support vector regression (SVR) model and a Bayesian stochastic Kriging model. These two models deal with the heteroscedasticity in simulation noise in different ways, and their superiority in approximating the response surface of simulations with heteroscedastic noise over regressing Kriging is verified through both numerical studies and real world applications. Furthermore, a distribution-based SVR model which takes into account the statistical distribution of simulation noise is developed. By utilizing the bootstrapping method, a global search scheme can be incorporated into this model. The value of taking into account the statistical distribution of simulation noise in improving the convergence rate for optimization is then verified through numerical examples and a real world application of integrated corridor traffic management. This research is one of the first to introduce simulation-based optimization methods into large-scale transportation network research. Various types of practical problems (with single-objective, with multi-objective or with complex constraints) can be resolved. Meanwhile, the developed optimization methods are general and can be applied to analyze all types of policies using any simulator. Methodological improvements to the surrogate models are made to take into account the statistical characteristics of simulation noise. These improvements are shown to enhance the prediction accuracy of the surrogate models, and further enhance the efficiency of optimization. Generally, compared to traditional surrogate models, fewer simulation evaluations would be needed to find the optimal solution when these improved models are applied

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods

Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Efficiency and Optimization of Buildings Energy Consumption: Volume II

This reprint, as a continuation of a previous Special Issue entitled “Efficiency and Optimization of Buildings Energy Consumption”, gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption