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Scheduling software updates for connected cars with limited availability

APPLIED SOFT COMPUTING 2019年 82卷

作者： Andrade, Carlos E. Byers, Simon D. Gopalakrishnan, Vijay Halepovic, Emir Poole, David J. Tran, Lien K. Volinsky, Christopher T. AT&T Labs Res 200 South Laurel Ave Middletown NJ 07748 USA AT&T Labs Res 1 AT&T Way Bedminster NJ 07921 USA

The current and the new generation of Internet of Things (IoT) devices present several challenges, among them the software update of legacy and new devices using wireless connections. In this paper, we study a problem of scheduling massive Firmware-Over-The-Air updates for millions of connected cars. We model this problem as a new generic problem called Time- and Machine-Dependent Scheduling Problem (TMDSP) that resembles project scheduling problems with variable-intensity activities. In the TMDSP, jobs, machine utilization, and production rates vary over time. In each period, a given job can be executed by a subset of machines with different capacities and production rates. The objective is to deploy a feasible schedule with minimum total completion time. We explore solving the problem using several approaches among than biased random-key genetic algorithm (BRKGA), Iterated Local Search (ILS), Simulating Annealing (SA), two variations of Tabu Search (TB), and traditional genetic algorithms (TGA), in addition to a Mixed Integer Programming (MIP) model. We test the proposed approaches on a synthetic benchmark and very large real instances in IoT space. While using a commercial solver and the MIP model, we are able to solve only a few real instances (with, at most, 1,976 cars and 9,116 sectors);on the other hand, BRKGA presents significantly better results when compared to ILS, MIP solver and a simple multi-start heuristic. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Connected cars Internet of Things Scheduling Time-and machine-dependent scheduling biased random-key genetic algorithm

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genetic algorithm for the Knapsack Problem with Irregular Shaped Items 44

Genetic Algorithm for the Knapsack Problem with Irregular Sh...

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44th Latin American Computing Conference on Informatics (CLEI)

作者： Queiroz, Layane R. S. Andretta, Marina Mundim, Leandro R. Univ Sao Paulo Inst Ciencias Matemat & Comp Sao Carlos SP Brazil Optimized Decis Making Sao Carlos SP Brazil

ISBN: (纸本)9781728104379

The two-dimensional knapsack problem with irregularly shaped items is solved in this work. It is utilized the concept of inner-fit raster and no-fit raster to verify packing feasibility, which stands for non-overlapping between items that are entirely contained inside the bin. The problem solution is obtained with a biased random-key genetic algorithm in which each chromosome contains information related to the order and rotation where each item should be packed into the bin. The chromosome also contains information about which heuristic has to be used to pack items and the probability of an offspring inheriting information from an elite parent. It is adopted three heuristics for positioning items, which are: bottom-left, left-bottom, and horizontal zig-zag. The experiments over literature instances showed that the developed genetic algorithm is very effective since it could obtain an optimal solution for 53.4% of the instances and improved the bin's occupancy ratio in about 2.1% when observing all the instances.

关键词： biased random-key genetic algorithm Two-dimensional Knapsack Problem Irregularly shaped items Nesting problems

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A cloud brokerage approach for solving the resource management problem in multi-cloud environments

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 95卷 16-26页

作者： Heilig, Leonard Lalla-Ruiz, Eduardo Voss, Stefan Univ Hamburg Inst Informat Syst IWI Hamburg Germany Pontificia Univ Catolica Valparaiso Escuela Ingn Ind Valparaiso Chile

Cloud computing is increasingly becoming a mainstream technology-delivery model from which companies and research aim to gain value. As different cloud providers offer cloud services in various forms, there is a huge potential of optimizing the selection of those services to better fulfill user-, i.e., consumer- and application-related requirements. Recently, multi-cloud environments have been introduced thus making it possible to execute applications not only on single-provider resources, but also by using resources from multiple cloud providers. Due to the growing complexity in cloud marketplaces, a cloud brokerage mechanism, interacting on behalf of the consumers with various cloud providers, can be used to provide decision support for consumers. In this paper, we address the Cloud Resource Management Problem in multi-cloud environments that is a recent optimization problem aimed at reducing the monetary cost and the execution time of consumer applications using Infrastructure as a Service of multiple cloud providers. Due to the fact that consumers require real-time and high-quality solutions to economically automate cloud resource management and corresponding deployment processes, we propose an efficient biased random-key genetic algorithm. The computational experiments over a large benchmark suite generated based on real cloud market resources indicate that the performance of our approach outperforms the approaches proposed in the literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cloud computing Multi-cloud Cloud brokerage IaaS biased random-key genetic algorithm Optimization

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Heuristics for a Hub Location-Routing Problem

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NETWORKS 2016年第1期68卷 54-90页

作者： Lopes, Mauro Cardoso de Andrade, Carlos Eduardo de Queiroz, Thiago Alves Resende, Mauricio G. C. Miyazawa, Flavio Keidi Univ Estadual Campinas Inst Comp Ave Albert Einstein 1251 BR-13083852 Campinas SP Brazil AT & T Labs Res Adv Technol Dept 200 South Laurel Ave Middletown NJ 07748 USA Univ Fed Goias Inst Math & Technol Ave Dr Lamartine Pinto de Avelar 1120 BR-75704020 Catalao Go Brazil Amazon Com Inc Math Optimizat & Planning Grp 333 Boren Ave North Seattle WA 98109 USA Univ Estadual Campinas Inst Comp Ave Albert Einstein 1251 BR-13083852 Campinas SP Brazil

We investigate a variant of the many-to-many hub location-routing problem which consists in partitioning the set of nodes of a graph into routes containing exactly one hub each, and determining an extra route interconnecting all hubs. A variable neighborhood descent with neighborhood structures based on remove/add, swap and exchange moves nested with routing and location operations is used as a local search procedure in a multistart algorithm. We also consider a sequential version of this local search in the multistart. In addition, a biased random-key genetic algorithm working with a local search routine, which also considers routing and location operations, is applied to the problem. To compare the heuristic solutions, we develop an integer programming formulation which is solved with a branch-andcut algorithm. Capacity and path elimination constraints are added in a cutting plane fashion. The separation algorithms are based on the computation of min-cut trees and on the connected components of a support graph. Computational experiments were conducted on several benchmark instances of routing problems and show that the heuristics are effective on medium to large-sized instances, while the branch-and-cut algorithm solves small to medium sized problems to optimality. These algorithms were also compared with a commercial hybrid solver showing that the heuristics are quite competitive. (C) 2016 Wiley Periodicals, Inc.

关键词： Hub location-routing problem heuristics variable neighborhood descent biased random-key genetic algorithm integer formulation

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Dynamic Cargo Stability in Loading and Transportation of Containers

Dynamic Cargo Stability in Loading and Transportation of Con...

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IEEE International Conference on Automation Science and Engineering

作者： Evandro C. Bracht Thiago A. de Queiroz Rafael C. S. Schouery Flavio K. Miyazawa Institute of Computing Institute of Mathematics and Technology

ISBN: (纸本)9781509024100

We present an approach to deal with the problem of packing boxes in a container with fixed dimensions (the container loading problem) constrained that the packing must be dynamically stable, that is, no box can move due to forces such as gravity and horizontal forces caused by transportation of the container. To solve this problem, an approach based on the metaheuristic BRKGA (biased random-key genetic algorithm) considering the geometrical nature of the problem, in order to better combine two solutions, is considered. The approach uses a physical simulation package to determine if the given packing is, indeed, dynamically stable. The results show that this approach can later be used as a tool for automated processes involving loading and transportation of containers.

关键词： Cargo stability Container loading biased random-key genetic algorithm Physics engine

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biased random-key genetic algorithms with applications in telecommunications

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TOP 2012年第1期20卷 130-153页

作者： Resende, Mauricio G. C. AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

This paper surveys several applications of biased random-key genetic algorithms (BRKGA) in optimization problems that arise in telecommunications. We first review the basic concepts of BRKGA. This is followed by a description of BRKGA-based heuristics for routing in IP networks, design of survivable IP networks, redundant server location for content distribution, regenerator location in optical networks, and routing and wavelength assignment in optical networks.

关键词： Optimization in telecommunications genetic algorithm biased random-key genetic algorithm random keys Combinatorial optimization Heuristics Metaheuristics

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randomized heuristics for the family traveling salesperson problem

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2014年第1期21卷 41-57页

作者： Moran-Mirabal, L. F. Gonzalez-Velarde, J. L. Resende, M. G. C. Tecnol Monterrey Qual & Mfg Ctr Monterrey Mexico AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

This paper introduces the family traveling salesperson problem (FTSP), a variant of the generalized traveling salesman problem. In the FTSP, a subset of nodes must be visited for each node cluster in the graph. The objective is to minimize the distance traveled. We describe an integer programming formulation for the FTSP and show that the commercial grade integer programming solver CPLEX11 can only solve small instances of the problem in reasonable running time. We propose two randomized heuristics for finding optimal and near-optimal solutions of this problem. These heuristics are a biased random-key genetic algorithm and a GRASP with evolutionary path-relinking. Computational results comparing both heuristics are presented in this study.

关键词： family traveling salesperson problem distance minimization randomized heuristics biased random-key genetic algorithm GRASP

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Evolutionary algorithms for Overlapping Correlation Clustering 14

Evolutionary Algorithms for Overlapping Correlation Clusteri...

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16th genetic and Evolutionary Computation Conference (GECCO)

作者： Andrade, Carlos E. Resende, Mauricio G. C. Karloff, Howard J. Miyazawa, Flavio K. Univ Estadual Campinas BR-1251 Campinas SP Brazil AT&T Labs Res Middletown NJ 07748 USA Yahoo Labs New York NY 10018 USA

ISBN: (纸本)9781450326629

In Overlapping Correlation Clustering (OCC), a number of objects are assigned to clusters. Two objects in the same cluster have correlated characteristics. As opposed to traditional clustering where objects are assigned to a single cluster, in OCC objects may be assigned to one or more clusters. In this paper, we present biased random-key genetic algorithms for OCC. We present computational experiments such results outperformed the state of art methods for OCC.

关键词： Data mining Correlation Clustering biased random-key genetic algorithm

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randomized heuristics for handover minimization in mobility networks

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JOURNAL OF HEURISTICS 2013年第6期19卷 845-880页

作者： Moran-Mirabal, L. F. Gonzalez-Velarde, J. L. Resende, M. G. C. Silva, R. M. A. Tecnol Monterrey Qual & Mfg Ctr Monterrey Mexico AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA Univ Fed Pernambuco Ctr Informat CIn Recife PE Brazil

A mobile device connects to the cell tower (base station) from which it receives the strongest signal. As the device moves it may connect to a series of towers. The process in which the device changes the base station it is connected to is called handover. A cell tower is connected to a radio network controller (RNC) which controls many of its operations, including handover. Each cell tower handles an amount of traffic and each radio network controller has capacity to handle a maximum amount of traffic from all base stations connected to it. Handovers between base stations connected to different RNCs tend to fail more often than handovers between base stations connected to the same RNC. Handover failures result in dropped connections and therefore should be minimized. The Handover Minimization Problem is to assign towers to RNCs such that RNC capacity is not violated and the number of handovers between base stations connected to different RNCs is minimized. We describe an integer programming formulation for the handover minimization problem and show that state-of-the-art integer programming solvers can solve only very small instances of the problem. We propose several randomized heuristics for finding approximate solutions of this problem, including a GRASP with path-relinking for the generalized quadratic assignment problem, a GRASP with evolutionary path-relinking, and a biased random-key genetic algorithm. Computational results are presented.

关键词： Mobility networks Handover minimization randomized heuristics GRASP biased random-key genetic algorithm

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