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Adaptive reallocation of cybersecurity analysts to sensors for balancing risk between sensors

SERVICE ORIENTED COMPUTING AND APPLICATIONS 2018年第2期12卷 123-135页

作者： Shah, Ankit Ganesan, Rajesh Jajodia, Sushil Cam, Hasan George Mason Univ Ctr Secure Informat Syst Mail Stop 5B5 Fairfax VA 22030 USA Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA

Cyber Security Operations Center (CSOC) is a service-oriented system. Analysts work in shifts, and the goal at the end of each shift is to ensure that all alerts from each sensor (client) are analyzed. The goal is often not met because the CSOC is faced with adverse conditions such as variations in alert generation rates or in the time taken to thoroughly analyze new alerts. Current practice at many CSOCs is to pre-assign analysts to sensors based on their expertise, and the alerts from the sensors are triaged, queued, and presented to analysts. Under adverse conditions, some sensors have more number of unanalyzed alerts (backlogs) than others, which results in a major security gap for the clients if left unattended. Hence, there is a need to dynamically reallocate analysts to sensors;however, there does not exist a mechanism to ensure the following objectives: (i) balancing the number of unanalyzed alerts among sensors while maximizing the number of alerts investigated by optimally reallocating analysts to sensors in a shift, (ii) ensuring desirable properties of the CSOC: minimizing the disruption to the analyst to sensor allocation made at the beginning of the shift when analysts report to work, balancing of workload among analysts, and maximizing analyst utilization. The paper presents a technical solution to achieve the objectives and answers two important research questions: (i) detection of triggers, which determines when-to reallocate, and (ii) how to optimally reallocate analysts to sensors, which enable a CSOC manager to effectively use reallocation as a decision-making tool.

关键词： Cybersecurity analysts Adaptive reallocation Balance risk between sensors Reactive scheduling Goal programming mixed-integer programming

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Energy-oriented production planning with time-dependent energy prices

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Procedia CIRP 2019年 80卷 245-250页

作者： Christoph Johannes Matthias G. Wichmann Thomas S. Spengler Institute of Automotive Management and Industrial Production Technische Universität Braunschweig Mühlenpfordtstr. 23 38106 Braunschweig Germany

Based on a more sustainable power generation, companies are faced so far with increasing energy costs for industrial production processes. However, in the future there is a possibility to reduce energy costs by the occurrence of time-dependent energy prices in the course of a day. In order to use these time-dependent energy prices best possible and to manufacture the products at minimal decision relevant costs, production planning approaches have to consider energy costs. To date, time-dependent energy prices are considered in numerous Job Shop Scheduling Problems, but in only few simultaneous lot-sizing and scheduling approaches. Hence, an appropriate model formulation for the consideration of time-dependent energy prices in simultaneous lot-sizing and scheduling and an investigation of the cost saving potential are missing. For this purpose, the Energy-Oriented Lot-sizing and Scheduling Problem (EOLSP) is introduced in this contribution. Furthermore, the energy and total cost saving potential of the presented model formulation compared to conventional planning is discussed within an illustrative example for a pre-crushing system in a recycling company.

关键词： Energy-oriented production planning lot-sizing scheduling time-dependent energy prices mixed-integer programming

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Learning customized and optimized lists of rules with mathematical programming

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MATHEMATICAL programming COMPUTATION 2018年第4期10卷 659-702页

作者： Rudin, Cynthia Ertekin, Seyda Duke Univ Dept Comp Sci Durham NC 27706 USA Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA Duke Univ Dept Stat Sci Durham NC 27706 USA Middle Eastern Tech Univ Dept Comp Engn Ankara Turkey MIT Sloan Sch Management Cambridge MA 02139 USA

We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. The flexible framework of mathematical programming allows users to create customized models with a provable guarantee of optimality. The software reviewed as part of this submission was given the DOI (Digital Object Identifier) https://***/10.5281/zenodo.1344142.

关键词： mixed-integer programming Decision trees Decision lists Sparsity Interpretable modeling Associative classification 68T05-Computer Science Artificial intelligence Learning and adaptive systems

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The impact of filtering in a branch-and-cut algorithm for multicommodity capacitated fixed charge network design

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2018年第2期6卷 143-184页

作者： Chouman, Mervat Crainic, Teodor Gabriel Gendron, Bernard Effat Univ Coll Business Jeddah Saudi Arabia Univ Quebec Montreal Dept Management & Technol Sch Management Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada Univ Montreal Interuniv Res Ctr Enterprise Networks Logist & Tr Dept Informat & Rech Operat Montreal PQ Canada

In this paper, we present a state-of-the-art branch-and-cut (B&C) algorithm for the multicommodity capacitated fixed charge network design problem (MCND). This algorithm combines bounding and branching procedures inspired by the latest developments in mixed-integer programming (MIP) software tools. Several filtering methods that exploit the structure of the MCND are also developed and embedded within the B&C algorithm. These filtering methods apply inference techniques to forbid combinations of values for some variables. This can take the form of adding cuts, reducing the domains of the variables, or fixing the values of the variables. Our experiments on a large set of randomly generated instances show that an appropriate selection of filtering techniques allows the B&C algorithm to perform better than the variant of the algorithm without filtering. These experiments also show that the B&C algorithm, with or without filtering, is competitive with a state-of-the-art MIP solver.

关键词： mixed-integer programming Multicommodity capacitated fixed charge network design Branch-and-cut Filtering

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A Mathematical Modelling and Optimization Approach for a Maritime Facility Location Transshipment Problem

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Informatica 2019年第4期29卷 609-632页

作者： Al-Yakoob, Salem M. Sherali, Hanif D. Department of Mathematics College of Science Kuwait University P.O. Box: 5969 Safat 13060 State of Kuwait Grado Department of Industrial and Systems Engineering (0118) Virginia Tech Blacksburg VA 24061 USA. E-mails:

This paper presents an optimization based mathematical modelling approach for a single source single destination crude oil facility location transshipment problem. We began by formulating a mixed-integer nonlinear programming model and use a rolling horizon heuristic to find an optimal location fora storage facility within a restricted continuous region. We next design a hybrid two-stage algorithm that combines judicious facility locations resulting from the proposed model into a previously developed column generation approach. The results indicate that improved overall operational costs can be achieved by strategically determining cost-effective locations of the transshipment facility.

关键词： mixed-integer programming vessel scheduling transportation inventory

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Drawing and Labeling High-Quality Metro Maps by mixed-integer programming

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2011年第5期17卷 626-641页

作者： Noellenburg, Martin Wolff, Alexander KIT Inst Theoret Informat D-76131 Karlsruhe Germany Univ Wurzburg Lehrstuhl Informat 1 D-97074 Wurzburg Germany

Metro maps are schematic diagrams of public transport networks that serve as visual aids for route planning and navigation tasks. It is a challenging problem in network visualization to automatically draw appealing metro maps. There are two aspects to this problem that depend on each other: the layout problem of finding station and link coordinates and the labeling problem of placing nonoverlapping station labels. In this paper, we present a new integral approach that solves the combined layout and labeling problem (each of which, independently, is known to be NP-hard) using mixed-integer programming (MIP). We identify seven design rules used in most real-world metro maps. We split these rules into hard and soft constraints and translate them into an MIP model. Our MIP formulation finds a metro map that satisfies all hard constraints (if such a drawing exists) and minimizes a weighted sum of costs that correspond to the soft constraints. We have implemented the MIP model and present a case study and the results of an expert assessment to evaluate the performance of our approach in comparison to both manually designed official maps and results of previous layout methods.

关键词： Network visualization graph drawing graph labeling metro map octilinear layout mixed-integer programming

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Ensuring content validity of patient-reported outcomes: a shadow-test approach to their adaptive measurement

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QUALITY OF LIFE RESEARCH 2018年第7期27卷 1683-1693页

作者： Choi, Seung W. van der Linden, Wim J. Pacific Metr Corp 1 Lower Ragsdale Dr Monterey CA 93940 USA

Most computerized adaptive testing (CAT) applications in patient-reported outcomes (PRO) measurement to date are reliability-centric, with a primary objective of maximizing measurement efficiency. A key concern and a potential threat to validity is that, when left unconstrained, individual CAT administrations could have items with systematically different attributes, e.g., sub-domain coverage. This paper aims to provide a solution to the problem from an optimal test design framework using the shadow-test approach to CAT. Following the approach, a case study was conducted using the PROMISA (R) (Patient-Reported Outcomes Measurement Information System) fatigue item bank both with empirical and simulated response data. Comparisons between CAT administrations without and with the enforcement of content and item pool usage constraints were examined. The unconstrained CAT exhibited a high degree of variation in items selected from different substrata of the item bank. Contrastingly, the shadow-test approach delivered CAT administrations conforming to all specifications with a minimal loss in measurement efficiency. The optimal test design and shadow-test approach to CAT provide a flexible framework for solving complex test-assembly problems with better control of their domain coverage than for the conventional use of CAT in PRO measurement. Applications in a wide array of PRO domains are expected to lead to more controlled and balanced use of CAT in the field.

关键词： Optimal test design Shadow-test approach to CAT mixed-integer programming Patient-reported outcomes measurement information system (PROMIS) Content validity

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A mixed-integer programming approach to strategic planning of chemical centres: A case study in the UK

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COMPUTERS & CHEMICAL ENGINEERING 2011年第8期35卷 1359-1373页

作者： Liu, Pei Whitaker, Alan Pistikopoulos, Efstratios N. Li, Zheng Univ London Imperial Coll Sci Technol & Med CPSE Dept Chem Engn London SW7 2AZ England Tsinghua Univ State Key Lab Power Syst Dept Thermal Engn Beijing 100084 Peoples R China

Chemical centres provide great potential to tackle the worldwide energy and environmental issues via integrated chemical synthesis and heat and power generation. However, planning of chemical centres still involves many formidable challenges, including locating production sites, arrangement of transportation, and selection of appropriate technologies. These problems become further complicated when considering the geographic situation of a region under study. In this paper, we propose a multi-period mixed-integer programming (MIP) approach to the optimal planning of chemical centres. The planning horizon is firstly divided into several time intervals, and the planning region is represented by a grid. Then a superstructure representation is developed to capture all available logistic and technical options. Based on the superstructure representation, an MIP problem is developed, and by solving it an optimal planning strategy can be obtained. A real-life case study for the UK follows, where the UK is divided into a grid of 34 cells. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Strategic planning Process integration Chemical synthesis Emissions reduction Superstructure mixed-integer programming

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Maximizing Carsharing Profits: An Optimization Model to Support the Carsharing Planning

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Procedia Manufacturing 2019年 39卷 1968-1976页

作者： Cristiano Martins Monteiro Claudia A. Soares Machado Mariana de Oliveira Lage Fernando Tobal Berssaneti Clodoveu Augusto Davis Jr. José Alberto Quintanilha Departament of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil Department of Transportation Engineering Polytechnic School University of São Paulo São Paulo Brazil Institute of Energy and Environment University of São Paulo São Paulo Brazil Department of Production Engineering Polytechnic School University of São Paulo São Paulo Brazil

Carsharing services aim to offer short-term car rentals, including round-trip and one-way alternatives. Round-trip clients must deliver the rented car at the same station where the rental has started. One-way clients can return the vehicle in a different station. This work proposes a mixed-integer Linear programming Model to optimize the fleet-sizing of a carsharing service for the one-way and round-trip alternatives, seen as utilization scenarios. The proposed model aims to maximize the company’s profit, finding the best number of vehicles to be allocated to each carsharing station. Different scenarios were analyzed for the one-way and round-trip settings, varying service costs, rental prices, number of clients, rental duration and driven distance. Simulations were performed using real spatial data from the city of São Paulo, Brazil. Results showed that round-trip profits can benefit from rentals with higher durations, and that one-way profits can overcome the profits from round-trip if user demand and number of available vehicles are enough.

关键词： Carsharing Planning Optimization of Fleet Sizing mixed-integer programming

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A Distributed Algorithm for Distribution Network Reconfiguration

A Distributed Algorithm for Distribution Network Reconfigura...

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China International Conference on Electricity Distribution (CICED)

作者： Gao, Yuanqi Wang, Peng Yu, Nanpeng Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781538667750

Distribution network reconfiguration algorithms change the status of sectionalizing and tie switches to reduce network line losses, relieve network overloads, minimize loss of load, or increase hosting capacity for distributed energy resources. Most of the existing work adopt centralized or hierarchical approaches to solve the network reconfiguration problem. This paper proposes a distributed scheme for network reconfiguration. In the distributed approach, the switches are represented by edge computing agents who communicate and work collectively with their neighbors to find the optimal reconfiguration solution. This scheme breaks the complex computation tasks required by centralized algorithms into much smaller ones. It also relieves the communication and data sharing burden via neighbor-to-neighbor communication. Simulation results on a 16-bus distribution test feeder demonstrate that the quality of the distributed solution is comparable to that of the centralized approach.

关键词： Distribution network reconfiguration distributed optimization mixed-integer programming

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