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Optimization-Time Analysis for Cybersecurity

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第4期19卷 2365-2383页

作者： Zhang, Yunxiao Malacaria, Pasquale Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

A mathematical framework to reason about time resilience in cybersecurity is here introduced. We first consider an attacker who is able to mount several multi-stage attacks on the organization: the defender's objective is to select an optimal portfolio of security controls, within a given budget, to withstand the highest number of attacks. The mathematical model is a Markov chain with an initial state called the safe state, intermediate states for all possible attacks (each attack state denoting a probabilistic attack graph), and a sink state denoting a successful attack. The overall defence problem is formulated as a bi-level multi-objective optimization, i.e., the defender selects an optimal portfolio of security controls to mitigate an optimal attacker. In order to determine the probability of success of an attack two cases will be considered: (a) the expected probability of success and (b) the highest probability of success. We refer to these two cases as expected-time analysis and worst-case time analysis, respectively. To solve precisely these bi-level optimizations strong duality and mixed integer linear programming are used. We then extend the framework to investigate resilience in terms of the total duration of the attacks;variations of the previous optimizations are presented to this purpose. Finally numerical evaluations are provided to compare the results obtained from the expected-time analysis and the worst-case time analysis.

关键词： Security Resilience Organizations Optimization Markov processes Analytical models Mathematical model Optimization security Markov processes cyber-security mixed integer linear programming time resilience

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A Heuristic for Load Distribution on Data Center Hierarchy: A MEC Approach

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IEEE ACCESS 2022年 10卷 69462-69471页

作者： Vieira, Rafael F. Souza, Daniel Da Silva Da Silva, Marcelino Silva Cardoso, Diego Lisboa Fed Univ Para Postgrad Program Elect Engn BR-66075110 Belem Para Brazil

Mobile Edge Computing (MEC) extends Cloud Computing to the edge of the network, creating a hierarchy of data centers. This paradigm provides computing capacity close to final users, relieves backhaul and the leading network, and serves latency-sensitive applications. When providing computing services at different network levels, it becomes necessary to carry out a more efficient distribution of the resources that come to coexist. A random allocation of these resources can lead to a low service acceptance rate and backhaul overhead problems. Problems like these can be solved with MEC. To maximize the service acceptance and ensure a fair distribution of services in the kinds of servers guaranteeing their quality of service requirements, we propose a MILP (mixed integer linear programming). The model performs an optimal allocation of applications in a two-level hierarchy of data centers: (i) MEC and (ii) cloud computing. On a large scale, the use of MILP becomes unfeasible due to the high computational cost, so we propose a heuristic based on the application profiles. We compare the proposed heuristic with two metaheuristics: (i) Genetic Algorithm and (ii) Particle Swarm Optimization. The solutions are compared in terms of service acceptance rate, large-scale performance, and efficient use of available resources. Results show that the proposed heuristic reaches 91% of the optimal solution and over 140% compared to GA and PSO solutions.

关键词： Servers Cloud computing Resource management Quality of service Task analysis Internet of Things Delays Mobile edge computing cloud computing data center hierarchy mixed integer linear programming genetic algorithm particle swarm optimization

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A Recycling Routing Problem of Broken Bikes With Incentives in Bike Sharing Systems

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IEEE ACCESS 2022年 10卷 106191-106201页

作者： Xu, Guoxun Zou, An Hainan Univ Sch Tourism Haikou 570228 Hainan Peoples R China

In recent years, the truck-based broken bike recycling mode (i.e., dedicated trucks deal with all transportation activities of broken bikes) in bike sharing systems (BSSs) has led to some problems in China, such as BSS operators have no willingness to collect broken bikes from a bike station in time. As a result, many BSSs are in bad condition due to an increase in the number of broken bikes. To solve this problem, many cities in China have started to introduce a new broken bike recycling mode, where some citizens (i.e., sanitation workers, freelancers, volunteers, etc.) are incentivized to participate in recycling activities. To deal with this new recycling mode, a recycling routing problem of broken bikes with incentives in BSSs is proposed, where the recycling operation is performed by citizen participants and a fleet of homogeneous trucks. Participants are incentivized to relocate broken bikes scattered in a BSS to transshipment stations in exchange for monetary rewards, and then these broken bikes are transported from transshipment stations to the depot by trucks. The problem is formulated as a mixed-integer linear programming model to minimize the total operation cost, and an efficient modified tabu search is developed to solve the proposed problem. Experimental results reveal the proposed broken bike recycling mode is beneficial for BSS operators to reduce the operation cost. Experimental results also show the proposed modified tabu search can generate high-quality solutions in short computing times.

关键词： Bicycles Costs Urban areas Search problems Vehicle routing Transportation Routing mixed integer linear programming Bike sharing vehicle routing problem broken bike mixed-integer linear programming tabu search

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Effect of mitigation technologies on the total cost and carbon dioxide emissions of a cement plant under multi-objective mixed linear programming optimisation

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2022年 186卷 326-349页

作者： Ishak, Siti Aktar Hashim, Haslenda Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Dept Chem Engn Johor Baharu Johor Malaysia Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Green Energy & Environm Planning Res Grp Johor Baharu Johor Malaysia

With the increase of industrial activities in past decades, climate change has been a challenge worldwide. The cement industry is considered one of the industries that generate high carbon dioxide (CO2) emissions. With an imminent increase in CO2 emissions due to increased cement demand, this study proposed a systematic model for a cost-optimal cement plant that fulfils carbon limitations without compromising product specifications. This study considered three mitigation methods for cement manufacturing: co-processing, kiln system improvements, and carbon capture. The mixed integer linear programming model was executed in GAMS. From the optimisation model, it was found that a cement plant with co-processing, kiln improvements, and carbon capture managed to reduce more than 90 % CO2 emissions with USD 135.96/tonne clinker. (C) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： mixed integer linear programming Cement plant Carbon dioxide mitigation Optimisation Mathematical programming Multi-objective epsilon-constraint method

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Generative Modeling of Pedestrian Behavior: A Receding Horizon Optimization-Based Trajectory Planning Approach

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IEEE ACCESS 2022年 10卷 81624-81641页

作者： Gupta, Saumya Zaki, Mohamed H. Vela, Adan Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA

Urbanization is bringing together various modes of transport, and with that, there are challenges to maintaining the safety of all road users, especially vulnerable road users (VRUs). There is a need for street designs that encourages cooperation between road users. Shared space is a street design approach that softens the demarcation of vehicles and pedestrian traffic by reducing traffic rules, traffic signals, road marking, and regulations. Understanding the interactions and trajectory formations of various VRUs will facilitate the design of safer shared spaces. In line with this goal, this paper aims to develop a methodology for generating VRUs trajectories that accounts for behaviors and social interactions. We develop a receding horizon optimization-based pedestrian trajectory planning algorithm capable of modeling pedestrian trajectories in a variety of shared space scenarios. Focusing on three scenarios-group interactions, unidirectional interaction, and fixed obstacle interaction-case studies are performed to demonstrate the strengths of the resulting generative model. Additionally, generated trajectories are validated using two benchmark datasets - DUT and TrajNet++. The three case studies are shown to yield low or near-zero Mean Euclidean Distance and Final Displacement Error values supporting the performance validity of the models. We also analyze gait parameters (step length and step frequency) to further demonstrate the model's capability at generating realistic pedestrian trajectories.

关键词： Behavioral sciences Trajectory Legged locomotion Safety Roads Transportation Planning Optimization mixed integer linear programming shared space social interaction rules trajectory-planning vulnerable road users urban design and planning

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Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第8期33卷 1982-1995页

作者： Shi, Tao Ma, Hui Chen, Gang Hartmann, Sven Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand Tech Univ Clausthal Dept Informat D-38678 Clausthal Zellerfeld Germany

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

关键词： Cloud computing Costs Time factors Optimization Upper bound Pricing User experience Multi-cloud Web application deployment service replication cost optimization mixed integer linear programming large neighborhood search

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An efficient Energy Management System for long term planning and real time scheduling of flexible polygeneration systems

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RENEWABLE ENERGY 2022年 200卷 1180-1201页

作者： La Fata, Alice Brignone, Massimo Procopio, Renato Bracco, Stefano Delfino, Federico Barilli, Riccardo Ravasi, Martina Zanellini, Fabio Univ Genoa Elect Elect & Telecommun Engn & Naval Architecture Genoa Italy Falck Renewables SpA Milan Italy

The ever-increasing participation of new energy sources, storage systems and controllable loads to electricity markets has introduced more complexity in the operation of power plants, microgrids (MGs) and networks. To face these issues, it is often necessary to design Distributed Energy Systems to optimize the power exchange. Usually, MG operation is managed by an Energy Management System (EMS) dealing with large amounts of parameters to be taken into account and that may drive to problems difficult to be faced and requiring long computational timings. In this direction, this paper describes the mixed integer linear programming MATLAB Based Energy Management System (MB-EMS) developed at the University of Genoa, aimed at optimizing the operating costs of a generic polygeneration system, considering different electrical and thermal production units, energy storage systems, non-programmable loads and flexible loads. Many important technical details are accounted: the duration and costs related to maintenance interventions and CO2 emissions costs are considered for all production units as well as constraints and incentives related to the High-Efficiency Cogeneration systems. Constraints related to the minimum and maximum State-Of-Charge of energy storage systems, plus their dete-rioration, with the consequent optimization of their utilization, are also modelled. Specific test cases for all functionalities are presented. Moreover, a test related to a real MG performed over a time horizon of one year with a time step of 1 h is performed and solved in a short computational time. The linearization of constraints reduces the computational effort, avoiding the need of clustering or averaging parameters for similar production units. Consequently, calculations are rapidly performed, also when dealing with long term planning problems (a realistic test case with time horizon of one year and time step of 15 min can be simulated in some seconds). In this sense, the developed tool c

关键词： Energy management system Renewable energy source polygeneration mixed integer linear programming

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Application of polynomial regression and MILP for under-frequency load shedding scheme in islanded distribution system

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ALEXANDRIA ENGINEERING JOURNAL 2022年第1期61卷 659-674页

作者： Sarwar, Sohail Mokhlis, Hazlie Othman, Mohamadariff Shareef, Hussain Wang, Li Mansor, Nurulafiqah Nadzirah Khairuddin, Anis Salwa Mohd Mohamad, Hasmaini Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Dept Elect Engn Al Ain 155511 U Arab Emirates Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Univ Teknol MARA Sch Elect Engn Shah Alam 40450 Malaysia

Distributed Generation (DG) integration, especially based on renewable energy resources, has gained great attention by power utilities and frequently utilized in the electrical distribution systems. However, DG integration imposes some risks towards system stability which may lead to system blackouts. This mainly occurs when the grid is decoupled from a portion of the distribution system consisting DGs while the total load demand is greater than total DGs output power. In order to overcome this problem, load shedding technique can be adopted to stabilize the system frequency. However, existing load shedding techniques were unable to accurately estimate the power imbalance due the variation in system loading. This results in excessive/inadequate load shedding to stabilize the system frequency. Moreover, random selection of the loads without load prioritization might cause vital loads to be shed. Therefore, in this paper, a new load shedding strategy for islanded distribution system is proposed. Polynomial regression analysis estimates the power mismatch while MILP optimization estimates optimal load combination for shedding. Furthermore, load priority (i.e., vital, non-vital, and semi-vital) is also considered to avoid disconnecting vital loads. Efficiency of the proposed scheme is evaluated on three different test systems. Validation is performed by modelling the proposed load shedding on PSCAD/EMTDC software for dynamic analysis. From the results, it can be analyzed that the proposed technique is superior compared to other techniques proposed in the literature. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

关键词： Frequency instability Load priority mixed integer linear programming Polynomial regression System blackout Under frequency load shedding

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Analytical Maneuver Library for Remote Inspection with an Underactuated Spacecraft

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第4期45卷 611-622页

作者： Jia-Richards, Oliver Lozano, Paulo C. MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

This paper presents an analytical library for maneuvering of an underactuated spacecraft around a central target with application to remote inspection. Free-flying small spacecraft are an attractive option for in situ remote inspection but require maneuvering strategies that are computationally simple, such that they can be implemented on a small spacecraft computer, while still allowing for constraints like plume impingement and underactuation to be included. An analytical maneuver library provides a suboptimal solution but can be computed without the need for numerical optimization. In addition, the low fuel cost of maneuvers means that the degree of suboptimality of the analytical maneuvers is marginal. To develop the library, forced circular motion is used in order to place the spacecraft in a controllable state such that external disturbances can be rejected. From there, trajectories can be designed in order to manipulate the circular motion and accomplish maneuvers such as joining the circular motion from rest or changing the circle's radius. Conditions under which plume impingement is guaranteed to be avoided are developed, and applications of the maneuver library to point-to-point maneuvering and landing on/docking to a rotating target are shown.

关键词： Spacecrafts mixed integer linear programming Propulsion System Propellant Thrust Vectoring Model Predictive Control Orbital Period CubeSat Low Thrust Propulsion Spacecraft Trajectories

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Optimizing emergency services for road safety using a decomposition method: a case study of Delhi

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OPSEARCH 2023年第1期60卷 155-173页

作者： Wajid, Shayesta Nezamuddin, N. Indian Inst Technol Delhi Dept Civil Engn New Delhi 110016 India

Road traffic crashes are among the top ten leading causes of deaths in India and emergency medical services play a vital role in reducing fatality after a road crash. Being one of the largest metropolitan areas in the world, Delhi has thousands of fatal crashes each year which makes it challenging to achieve a timely response to crashes. In this study, we demonstrated the efficiency of a district-level decomposition approach for solving various types of ambulance optimization problems (for coverage, for survivability, and by incorporating travel time uncertainty) for the city of Delhi, India. Three scenarios were compared to optimize the ambulance locations: the first scenario (S1) considered only the existing ambulance sites;the second (S2) and third (S3) scenarios considered a larger set of potential ambulance sites and relocated ambulances within districts and across districts, respectively. Results showed that the city-level coverage increased by at least 10% with the reallocation of ambulances across districts. For the coverage and survivability maximization models, the decomposition approach was between two to six times faster than the non-decomposition approach. The decomposition approach was critical in solving a robust coverage model accounting for travel time uncertainty and containing nearly half-a-million integer variables. It solved the robust model on a regular PC with 16 GB RAM in half an hour. The non-decomposition approach needed a workstation with 124 GB RAM to solve the same problem in 5 h.

关键词： Emergency medical services Double standard model mixed integer linear programming Decomposition method Facility location

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