FIZ Karlsruhe

Portfolio optimization is the process of determining the best combination of securities and proportions with the aim of having less risk and obtaining more profit in an investment. Utilizing covariance as a risk measure, mean-variance portfolio optimization model has brought a revolutionary approach to quantitative finance. Since then, along with the advancements in computational power and algorithmic enhancements, a lot of efforts have been made on improving this model by considering real-life conditions and solving model variants with various methodologies tested on various data and performance measures. A comprehensive literature review of recent and novel papers is crucial to establish a pattern of the past, and to pave the way on future directions. In this paper, a total of 175 papers published in the last two decades are selected within the scope of operations research community and reviewed in detail. Thus, a comprehensive survey on the deterministic models and applications suggested for mean-variance portfolio optimization in which several variants of this model as well as additional real-life constraints are studied. The review classifies the approaches according to exact and approximate attempts and analyzes the proposed algorithms based on various data and performance indicators in depth. Areas of future research are outlined.

A comprehensive review of deterministic models and applications for mean-variance portfolio optimization

Fault Tree Analysis of floating offshore wind turbines

https://isiarticles.com/bundles/Article/pre/pdf/42388.pdf

Construction and evaluation framework for a real-life project database

This paper proposes a new approach for cost and risk assessment in the multi-objective selection of routes for the transport of hazardous materials (hazmat) on a network of city roads. The model is based on the application of an Adaptive Neuro Fuzzy Inference System (ANFIS). The values of the cost and risk criteria are, using an adaptive neuro-fuzzy network trained with an Artificial Bee Colony (ABC) algorithm, integrated into a single CR value by means of which the worthiness of each branch in the network is expressed, and after which the selection of the route is made using Dijkstra's algorithm. The ANFIS adequately treats a number of uncertainties and ambiguities in the input data and enables the inclusion of the knowledge of experts and the preferences of the decision makers. The procedure is also applicable in cases in which the decision maker does not have high quality data available. The proposed model is tested in a real urban route planning problem, in a case study of the distribution of oil and oil derivatives in Belgrade, Serbia.

Cost and risk aggregation in multi-objective route planning for hazardous materials transportation—A neuro-fuzzy and artificial bee colony approach

The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very ...

A classification of community detection methods in social networks: a survey

This paper presents a variant of vehicle routing problem, incorporating factors of transportation costs in conjunction with adverse situations. The effects of hazardous materials (HazMat) transportation are examined on air-pollution exposure levels, as well as the risk and damage in the case of accidents. In an urban network, the various weights that can be are assigned to each road of a route are associated to the following factors: time, cost, pollution exposure levels, and the risk of damage in HazMat transportation. These weights express the travel cost and the estimated provoked adverse consequences at each road of a vehicle transit route between two disparate points of the network concerning the pollution and risk of unforeseen accident during hazardous materials transportation. The sum of the weights assigned to every road of a route represents the route value according to the corresponding factors. We introduce an optimization criterion in order to select the optimal route among all vehicle transition routes joining any given pair of the network points that does not surpass a predefined threshold route value for each factor. The predefined weights for each factor are a percentage increment of its corresponding shortest route value. The optimal location of a central depot is determined where each vehicle serves a customer using one specific route with respect to the posed restrictions. Subsequently, a suitable partition into sub-networks is carried out in the case of large urban areas and a depot is assigned to each sub-network by applying a specific optimization criterion. A branch and bound tree search algorithm is developed for the determination of the optimal routes while the locations of the depots are investigated within the framework of graph theory. Finally, small numerical examples are given.

A multi-objective transportation routing problem

In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.

A Survey on the Recent Advances of Deep Community Detection

Several synchronous applications are based on the graph-structured data; among them, a very important application of this kind is community detection. Since the number and size of the networks modeled by graphs grow larger and larger, some level of parallelism needs to be used, to reduce the computational costs of such massive applications. Social networking sites allow users to manually categorize their friends into social circles (referred to as lists on Facebook and Twitter), while users, based on their interests, place themselves into groups of interest. However, the community detection and is a very effortful procedure, and in addition, these communities need to be updated very often, resulting in more effort. In this paper, we combine parallel processing techniques with a typical data structure like threaded binary trees to detect communities in an efficient manner. Our strategy is implemented over weighted networks with irregular topologies and it is based on a stepwise path detection strategy, where each step finds a link that increases the overall strength of the path being detected. To verify the functionality and parallelism benefits of our scheme, we perform experiments on five real-world data sets: Facebook
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, Twitter
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, Google+
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, Pokec, and LiveJournal.

A Parallel Algorithm for Community Detection in Social Networks, Based on Path Analysis and Threaded Binary Trees

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of data that arrives continuously Streams of big data led to the emergence of several Distributed Stream Processing Systems (DSPS) that assign processing tasks to the available resources (dynamically or not) and route streaming data between them Efficient scheduling of processing tasks can reduce application latencies and eliminate network congestions However, the available DSPSs' in-built scheduling techniques are far from optimal In this work, we extend our previous work, where we proposed a linear scheme for the task allocation and scheduling problem Our scheme takes advantage of pipelines to handle efficiently applications, where there is need for heavy communication (all-to-all) between tasks assigned to pairs of components In this work, we prove that our scheme is periodic, we provide a communication refinement algorithm and a mechanism to handle many-to-one assignments efficiently For concreteness, our work is illustrated based on Apache Storm semantics The performance evaluation depicts that our algorithm achieves load balance and constraints the required buffer space For throughput testing, we compared our work to the default Storm scheduler, as well as to R-Storm Our scheme was found to outperform both the other strategies and achieved an average of 25%-40% improvement compared to Storm's default scheduler under different scenarios, mainly as a result of reduced buffering (≈ 45% less memory) Compared to R-storm, the results indicate an average of 35%-45% improvement

/pdf/pipeline-based-linear-scheduling-of-big-data-streams-in-the-1oh1jdeizp.pdf

Stefanos Katsavounis

Papers

A classification of community detection methods in social networks: a survey

A multi-objective transportation routing problem

A Survey on the Recent Advances of Deep Community Detection

A Parallel Algorithm for Community Detection in Social Networks, Based on Path Analysis and Threaded Binary Trees

Pipeline-Based Linear Scheduling of Big Data Streams in the Cloud