Outlier Detection Methods for Industrial Applications

An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several causes. The measurement can be incorrectly observed, recorded or entered into the process computer, the observed datum can come from a different population with respect to the normal situation and thus is correctly measured but represents a rare event. In literature different definitions of outlier exist: the most commonly referred are reported in the following: - "An outlier is an observation that deviates so much from other observations as to arouse suspicions that is was generated by a different mechanism " (Hawkins, 1980). - "An outlier is an observation (or subset of observations) which appear to be inconsistent with the remainder of the dataset" (Barnet & Lewis, 1994). - "An outlier is an observation that lies outside the overall pattern of a distribution" (Moore and McCabe, 1999). - "Outliers are those data records that do not follow any pattern in an application" (Chen and al., 2002). - "An outlier in a set of data is an observation or a point that is considerably dissimilar or inconsistent with the remainder of the data" (Ramasmawy at al., 2000). Many data mining algorithms try to minimize the influence of outliers for instance on a final model to develop, or to eliminate them in the data pre-processing phase. However, a data miner should be careful when automatically detecting and eliminating outliers because, if the data are correct, their elimination can cause the loss of important hidden information (Kantardzic, 2003). Some data mining applications are focused on outlier detection and they are the essential result of a data-analysis (Sane & Ghatol, 2006). The outlier detection techniques find applications in credit card fraud, network robustness analysis, network intrusion detection, financial applications and marketing (Han & Kamber, 2001). A more exhaustive list of applications that exploit outlier detection is provided below (Hodge, 2004): - Fraud detection: fraudulent applications for credit cards, state benefits or fraudulent usage of credit cards or mobile phones. - Loan application processing: fraudulent applications or potentially problematical customers. - Intrusion detection, such as unauthorized access in computer networks

A two-stage stochastic mixed-integer program modelling and hybrid solution approach to portfolio selection problems

In this paper, we investigate a multi-period portfolio selection problem with a comprehensive set of real-world trading constraints as well as market random uncertainty in terms of asset prices. We formulate the problem into a two-stage stochastic mixed-integer program (SMIP) with recourse. The set of constraints is modelled as mixed-integer program, while a set of decision variables to rebalance the portfolio in multiple periods is explicitly introduced as the recourse variables in the second stage of stochastic program. Although the combination of stochastic program and mixed-integer program leads to computational challenges in finding solutions to the problem, the proposed SMIP model provides an insightful and flexible description of the problem. The model also enables the investors to make decisions subject to real-world trading constraints and market uncertainty. To deal with the computational difficulty of the proposed model, a simplification and hybrid solution method is applied in the paper. The simplification method aims to eliminate the difficult constraints in the model, resulting into easier sub-problems compared to the original one. The hybrid method is developed to integrate local search with Branch-and-Bound (B&B) to solve the problem heuristically. We present computational results of the hybrid approach to analyse the performance of the proposed method. The results illustrate that the hybrid method can generate good solutions in a reasonable amount of computational time. We also compare the obtained portfolio values against an index value to illustrate the performance and strengths of the proposed SMIP model. Implications of the model and future work are also discussed

Combining heuristics with simulation and fuzzy logic to solve a flexible-size location routing problem under uncertainty

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/501100011033). In addition, it has received the support of the Doctoral School at the Universitat Oberta de Catalunya (Spain) and the Universidad de La Sabana (INGPhD-12-2020).Peer ReviewedPostprint (published version

Hazardous materials: An inventory model with risk and safety considerations

Hazardous materials (HAZMAT) are essential to the economy in different industrial processes for the operation of different machines and systems. However, these materials also represent a risk to people, property, and the environment. A literature review was conducted to observe the approaches and considerations that have been taken into account in previous research related to HAZMAT inventory management. As a result, it was observed that most of the studies do not take into account risk considerations, the presence of fire protection systems, or regulatory compliance. The most common objective of the reviewed articles is to minimize the total inventory cost of the HAZMAT inventories. The main objective of this research is to develop an inventory model that allows the determination of the order quantities of HAZMAT that have to be purchased and the inventory risk level. This model will consider the maximum allowable quantities indicated by the U.S. HAZMAT regulations for transportation, storage, and fire protection systems, to provide regulatory compliance. To create the HAZMAT inventory model, modifications to the classic economic order quantity model have been made considering the existing HAZMAT regulations such as the International Building Code and the 49 C.F.R. by the Department of Transportation. An interactive tool has been developed to allow the user to use the proposed model. In addition, the proposed HAZMAT inventory model with risk and safety considerations will allow users to understand the impact of the different inventory conditions such as the type of occupancy of the facility and the type of fire protection system on the order quantity and the inventory risk level

Fuzzy Real Options Analysis Applied to Urban Renewable Energy Investments

The application of real options and fuzzy real options to renewable energy investment decisions is explored in the context of the valuation of three urban rooftop solar projects. Four real options methods were used to analyse the abandonment option present within these projects. Two of these methods, Fuzzy Black-Scholes and Fuzzy Binomial, used fuzzy numbers for the cashflow and salvage inputs. The resulting European put valuations for the option to sell the projects off for salvagewere consistent across the various techniques, including both classical and fuzzy. The real options present within the solar projects consistently added value to the adjusted net present values of the projects, which should improve their investment prospects. Additionally, we discuss the role of using fuzzy options pricing techniques as opposed to traditional real options and their usefulness to the practitioner

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man