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From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses. [ABSTRACT FROM AUTHOR]

Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit. [ABSTRACT FROM AUTHOR]

This paper presents a model formulation for the multiobjetive optimization of transportation costs, inventory costs and service level for the goods distribution process. The multiobjective model is composed by two objective functions, in which the transportation and inventory cost are optimized using the Inventory Routing Problem (IRP) and the service level is optimized using the number of accomplished time windows for the vehicles in the routing process. The model is solved using the Nondominated Sorting Genetic Algorithm II (NSGAII), implementing a novel representation of the chromosome, which allows assigning inventory and routes simultaneously. The algorithm allows calculating the Pareto Frontier for the analyzed distribution case, observing good qualities in the solution, compared with what is found in scientific literature. [ABSTRACT FROM PUBLISHER]

This paper presents the application of Inventory Routing Problem (IRP) for the distribution of goods between multiple clients (n) and multiple suppliers (m), which is based on the concept of collaboration between companies through vendor managed inventory (VMI). A review of the processes of distribution and routing of goods including the variable inventory and the IRP model with multiple warehouses are presented and it is proposed a genetic algorithm for its solution. The distribution of goods is a key process in the supply chain, since it plays a decisive role in the business logistics costs and in service level provided to customers. The current competitive environment has led companies to work together through partnerships of cooperation and collaboration processes, leading to find new forms of distribution involving multiple suppliers and multiple customers. The main results of the model and the proposed genetic algorithm, reductions in inventory costs and transportation to the problem studied are obtained. [ABSTRACT FROM PUBLISHER]

This paper is designed a hybrid algorithm for solving the traveling salesman problem, which is listed among the NP-complete and which has received much attention in recent decades. As a solution strategy, we propose a memetic algorithm; which is based on the combination of an evolutionary algorithm and local search (2-opt), while the first analyzes several search spaces, the second explodes (improvement) each of the solutions found by the evolutionary algorithm. An individual analysis of the main heuristics used is made in the article [ABSTRACT FROM PUBLISHER]