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Resilient Service Embedding in IoT Networks

IEEE ACCESS 2020年 8卷 123571-123584页

作者： Al-Shammari, Haider Qays Lawey, Ahmed Q. El-Gorashi, Taisir E. H. Elmirghani, Jaafar M. H. Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency.

关键词： Energy efficiency Internet of Things mixed integer linear programming queuing resilience smart buildings service-oriented architecture virtualization

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Evaluating and Optimizing Opportunity Fast-Charging Schedules in Transit Battery Electric Bus Networks

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TRANSPORTATION SCIENCE 2020年第6期54卷 1601-1615页

作者： Abdelwahed, Ayman van den Berg, Pieter L. Brandt, Tobias Collins, John Ketter, Wolfgang Erasmus Univ Rotterdam Sch Management NL-3062 PA Rotterdam Netherlands Univ Minnesota Coll Sci & Engn Minneapolis MN 55455 USA Univ Cologne Fac Management Econ & Social Sci Cologne Inst Informat Syst D-50969 Cologne Germany Univ Cologne Inst Energy Econ D-50969 Cologne Germany

Public transport operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fast-charging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two mixed-integer linear programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event Optimization (DEO) discretizes the time and decisions into nonuniform slots based on arrival and departure events in the network. In addition to the DEO's higher practicability, the comparative computational study carried out on the transit-bus network in the city of Rotterdam, Netherlands, shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared with two reference common-sense greedy strategies: First-in-First-Served and Lowest-Charge-Highest-Priority.

关键词： transit electric bus networks charging schedule mixed integer linear programming

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Wind Power with Energy Storage Arbitrage in Day-ahead Market by a Stochastic MILP Approach

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LOGIC JOURNAL OF THE IGPL 2020年第4期28卷 570-582页

作者： Gomes, I. L. R. Melicio, R. Mendes, V. M. F. Pousinho, H. M., I Univ Evora Dept Fis Escola Ciencias & Tecnol P-7000645 Evora Portugal Univ Evora Inst Ciencias Terra ICT P-7000645 Evora Portugal Univ Lisbon Inst Super Tecn IDMEC P-1049001 Lisbon Portugal Inst Super Engn Lisboa Dept Elect Engn & Automat P-1959007 Lisbon Portugal Univ Beira Interior Electromechatron Syst Res Ctr CISE P-6201001 Covilha Portugal

This paper is about a support information management system for a wind power (WP) producer having an energy storage system (ESS) and participating in a day-ahead electricity market. Energy storage can play not only a leading role in mitigation of the effect of uncertainty faced by a WP producer, but also allow for conversion of wind energy into electric energy to be stored and then released at favourable hours. This storage provides capability for arbitrage, allowing an increase on profit of a WP producer, but must be supported by a convenient problem formulation. The formulation proposed for the support information management system is based on an approach of stochasticity written as a mixed integer linear programming problem. WP and market prices are considered as stochastic processes represented by a set of scenarios. The charging/discharging of the ESS are considered dependent on scenarios of market prices and on scenarios of WP. The effectiveness of the proposed formulation is tested by comparison of case studies using data from the Iberian Electricity Market. The comparison is in favour of the proposed consideration of stochasticity.

关键词： Electricity markets energy storage mixed integer linear programming stochastic optimization wind power

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Multi-objective optimization of sustainable biomass supply chain network design

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APPLIED ENERGY 2020年 272卷 115259-115259页

作者： Durmaz, Yesim Gital Bilgen, Bilge DokuzEylul Univ Grad Sch Nat & Appl Sci Dept Ind Engn Tinaztepe Campus TR-35397 Izmir Turkey DokuzEylul Univ Dept Ind Engn Tinaztepe Campus TR-35397 Izmir Turkey

This paper addresses the optimal design and planning of the biomass supply chain network that encompasses flow from poultry farms to biogas facilities. A novel multi-stage solution methodology is developed to solve the sustainable biomass supply chain network design problem. Geographical Information Systems, and Analytic Hierarchy Process Techniques are used to determine the candidate location of biogas facilities. The proposed multi-objective mixed integer linear programming model is capable of making strategic decisions (optimal biogas facility locations with capacities) along with the tactical decisions (transportation network flows). The model incorporates the two objective function of maximization of the profit, and minimization of total distance between poultry farms and biogas facilities. The aim is to determine the optimal number, location, and size of the biogas facilities, as well as the network flow, and electricity generated. The applicability of the model and solution methodology is demonstrated through a case study for a poultry supply chain network in Turkey. Additionally, sensitivity analysis is conducted to account for the impact of different parameters on the model. Sensitivity analysis show that both maximum distance parameter, and purchasing prices have major impact on decisions, and financial yield.

关键词： Biomass supply chain network design Multi-objective optimization Geographic information systems Analytical hierarchy process mixed integer linear programming Poultry waste

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Optimal European cooperative supply chains for carbon capture, transport, and sequestration with costs share policies

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AICHE JOURNAL 2020年第4期66卷 e16872-e16872页

作者： d'Amore, Federico Bezzo, Fabrizio Univ Padua Dept Ind Engn CAPE Lab Comp Aided Proc Engn Lab Via Marzolo 9 I-35131 Padua PD Italy

In the past decades, CO2 constituted nearly the 80% of anthropogenic greenhouse gases emissions therefore, global actions are needed to tackle the increase of carbon concentration in the atmosphere. CO2 (carbon) capture and storage has been highlighted among the most promising options to decarbonize the energy and industry sectors. Considering a large-scale infrastructure at European level, economic cooperation has been highlighted as a key requirement to relieve single countries from too high risk and commitment. This article proposes an economic optimization for cooperative supply chains for CO2 capture and storage, by adopting policies that balance the spread of costs among countries, according to local characteristics in terms of population, CO2 emissions, and macroeconomic outcome. Results show that the additional European investment for cooperation (max. +2.6% with respect to a noncooperative network) should not constitute a barrier toward the installation and operation of such more effective network designs.

关键词： carbon capture transport and storage costs share and differential cooperation European supply chain optimization mixed integer linear programming

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Optimal Switch Placement in Distribution Systems: A High-Accuracy MILP Formulation

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IEEE TRANSACTIONS ON SMART GRID 2020年第6期11卷 5009-5018页

作者： Shahbazian, Abbas Fereidunian, Alireza Manshadi, Saeed D. KN Toosi Univ Technol Fac Elect Engn Tehran 163171419 Iran San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

A new solution method is introduced to the problem of optimally deploying manual and automatic switches in distribution systems, where the product of two continuous variables and the inverse of a continuous variable are reformulated as a linear relation. This leads to a (mixed integer linear problem) MILP power flow formulation too. The objective function includes cost and reliability. The cost term itself includes capital investment, installation, and maintenance costs (MC) as well as customer interruption cost (CIC);while the reliability term is represented by system average interruption duration index (SAIDI). The problem is formulated as a MILP, which guarantees a global optimal solution. The effectiveness of the proposed method is validated through various case studies and sensitivity analyses on the RBTS4, followed by a comprehensive discussion and analysis of results. The proposed MILP formulation prescribes fewer switches while achieving lower SAIDI, compared to that of a previous MINLP formulation.

关键词： Switches Reliability Circuit faults Manuals Load flow Indexes linear programming Distribution automation system reliability evaluations switch placement linear Power flow product of two continuous variable mixed integer linear programming

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A bottom-up packing approach for modeling the constrained two-dimensional guillotine placement problem

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COMPUTERS & OPERATIONS RESEARCH 2020年 115卷 104851-000页

作者： Martin, Mateus Morabito, Reinaldo Munari, Pedro Univ Fed Sao Carlos Dept Prod Engn Via Washington Luiz Km 235 BR-13565905 Sao Carlos SP Brazil

In this paper, we address the Constrained Two-dimensional Guillotine Placement Problem (C2GPP). This problem considers a rectangular large object and a set of rectangular small item types of given sizes and values. The objective is to select and cut the most valuable subset of small items from the large object using orthogonal guillotine cuts and constrained patterns. To completely model the problem, we present pseudo-polynomial and compact integer non-linear formulations. Then, we obtain an equivalent mixed integer linear programming (MILP) formulation from each non-linear model. These novel formulations are related to a bottom-up packing approach of successive horizontal and vertical builds of the small items. Additionally, we develop a set of constraints for each model which allows us to strictly consider d-staged guillotine cutting patterns, for a given positive integer d. To evaluate the MILP models and compare their performance to the state-of-the-art formulation of the C2GPP, we run computational experiments using three sets of benchmark instances, two of them from the literature. The results show that the proposed models, based on a bottom-up packing approach, lead to optimal or near-optimal solutions in reasonable processing times, even for scenarios that are intractable for the benchmark model. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cutting & packing problems Constrained two-dimensional cutting Non-staged and staged guillotine cuts mixed integer linear programming Bottom-up packing

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Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 184卷 106298-000页

作者： Ge, Xiaolin Chen, Quan Fu, Yang Chung, C. Y. Mi, Yang Shanghai Univ Elect Power Elect Power Coll Shanghai Peoples R China Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

An optimization model for offshore wind farm maintenance scheduling is presented, considering minimum maintenance costs and maximum power generation. For power generation, the wind speed at each tower site plays an important role that is impacted not only by the dynamically changing wake overlap area and the consequence of variation of wind direction but also by the relative position of the wake affected by the maintenance status. This paper combines the wake model with the maintenance status to accurately express the input wind speed of the wind turbine (WT) in each period. Because the optimization model includes complex dynamical coupling relationships and a number of nonlinear constraints, mixed integer second-order cone programming (MISOCP) are employed to address these issues. The MISOCP model is relaxed as a mixed integer linear programming (MILP) model to improve computational efficiency and the e-constraint method is utilized to handle the multi-objective function. The proposed model and method are tested in a short-term maintenance case of an offshore wind farm. The numerical results demonstrate that the proposed approach can achieve sound economic benefits and provide comprehensive decision support.

关键词： Offshore wind farm Maintenance Wake effect mixed integer linear programming Multi-objective

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Scheduling directed acyclic graphs with optimal duplication strategy on homogeneous multiprocessor systems

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2020年第0期138卷 115-127页

作者： Tang, Qi Zhu, Li-Hua Zhou, Li Xiong, Jun Wei, Ji-Bo Natl Univ Def Technol Dept Elect Sci & Engn Changsha Peoples R China Hunan Univ Changsha Peoples R China

Modern applications generally need a large volume of computation and communication to fulfill the goal. These applications are often implemented on multiprocessor systems to meet the requirements in computing capacity and communication bandwidth, whereas, how to obtain a good or even the optimal performance on such systems remains a challenge. When tasks of the application are mapped onto different processors for execution, inter-processor communications become inevitable, which delays some tasks' execution and deteriorates the schedule performance. To mitigate the overhead incurred by inter-processor communications and improve the schedule performance, task duplication strategy has been employed in the schedule. Most available techniques for the duplication-based scheduling problem utilize heuristic strategies to produce sub-optimal solutions, however, how to find the optimal duplication-based solution with the minimal schedule makespan remains an unsolved issue. To fill in this gap, this paper proposes a novel mixed integer linear programming (MILP) formulation for this problem, together with a set of key theorems which enable and simplify the MILP formulation. The proposed MILP formulation can optimize the duplication strategy, serialize the execution of task instances on each processor and determine data precedences among different task instances, thus producing the optimal solution. The proposed method is tested on a set of synthesized applications and platforms and compared with the well-known algorithm. The experimental results demonstrate the effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Task duplication Multiprocessor Schedule mixed integer linear programming Makespan

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Designing an efficient clustering strategy for combined Fog-to-Cloud scenarios

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 109卷 392-406页

作者： Asensio, A. Masip-Bruin, X. Duran, R. J. de Miguel, I Ren, G. Daijavad, S. Jukan, A. Univ Politecn Cataluna Adv Network Architectures Lab CRAAX UPC Barcelona Spain Univ Valladolid Valladolid Spain IBM Corp Almaden Res Ctr San Jose CA USA IBM Corp Almaden Res Ctr Next Generat Comp Syst Dept San Jose CA USA Tech Univ Braunschweig Commun Networks Braunschweig Germany

The evolution of the Internet of Things (IoT) is imposing many distinct challenges, particularly to guarantee both wide and global systems communication, and to ensure an optimal execution of services. To that end, IoT services must make the most out of both cloud and fog computing (turning into combined fog-cloud scenarios), which indeed requires novel and efficient resource management procedures to handle such diversity of resources in a coordinated way. Most of the related works that can be found in the literature focus on resource mapping for service-specific execution in fog-cloud;however, those works assume a control and management architecture already deployed. Interestingly, few works propose algorithms to set that control architecture, necessary to execute the services and effectively implement services and resource mapping. This paper addresses that challenge by solving the problem of optimal clustering of devices located at the edge of the network offering their resources to support fog computing while defining the control and management role of each of them in the architecture, in order to ensure access to management functions in combined fog-cloud scenarios. In particular, we set out the Fog-Cloud Clustering (FCC) problem as an optimization problem, which is based on multi-objective optimization, including realistic, novel and stringent constraints;e.g., to improve the architecture's robustness by means of a device acting as a backup in the cluster. We model the FCC problem as a mixed integer linear programming (MILP) formulation, for which a lower and an upper bound on the number of required clusters is derived. We also propose a machine learning-based heuristic that provides scalable and near-optimal solutions in realistic scenarios in which, due to the high number of connected devices, solving the MILP formulation is not viable. By means of a simulation study, we demonstrate the effectiveness of the algorithms comparing its results with those of MILP

关键词： Fog-to-Cloud systems Fog-to-Cloud management Clustering mixed integer linear programming Machine learning

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