Integrating LP-guided variable fixing with MIP heuristics in the robust design of hybrid wired-wireless FTTx access networks

D’Andreagiovanni, Fabio; Mett, Fabian; Nardin, Antonella; Pulaj, Jonad

doi:10.1016/j.asoc.2017.07.018

Cited by 28 publications

(19 citation statements)

References 38 publications

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“…LP relaxation can also be used to process specific repair strategies to build integer feasible solutions, using compact LP relaxation or Lagrangian bounds as in D'Andreagiovanni et al. () and Cheng et al. ().…”

Section: Related Workmentioning

confidence: 99%

Parallel matheuristics for the discrete unit commitment problem with min‐stop ramping constraints

Dupin

Talbi

2018

Int Trans Operational Res

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The discrete unit commitment problem with min‐stop ramping constraints optimizes the daily production of thermal power plants, subject to an operational reactivity of thermal units in a 30‐minute delay. Previously, mixed integer programming (MIP) formulations aimed at an exact optimization approach. This paper derives matheuristics to face the short time limit imposed by the operational constraints. Continuous relaxations guide the search for feasible solutions exploiting tailored variable fixing strategies. Parallel matheuristics are derived considering complementary strategies in parallel. Tests were performed on more than 600 real‐life instances. Our parallel matheuristic provides high‐quality solutions and outperforms the MIP approach in the time limits imposed by the industrial application. This paper illustrates a special interest for matheuristics in industrial highly constrained problems: many tailored neighborhood searches can be derived from an MIP formulation, and their combination in a parallel scheme improves the solution quality as well as the consistency of the heuristic.

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Section: Related Workmentioning

confidence: 99%

Parallel matheuristics for the discrete unit commitment problem with min‐stop ramping constraints

Dupin

Talbi

2018

Int Trans Operational Res

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“…Also, the adoption of bio-inspired and genetic heuristics is not new: if we focus on genetic and evolutionary algorithms, we can report the remarkable cases of: [15], which addresses the problem of the optimal positioning of base stations in a mobile network, encoding the location of base stations in the chromosomes of the genetic algorithm; [16], which addresses the decision problem of how establishing the optimal assignment of users to deployed transmitters, in particular in the context of WiMAX networks, encoding the assignment in the chromosomes; [17], which focuses on the frequency assignment problem (FAP), proposing a permutation-based genetic algorithm to solve minimum span and fixed spectrum variants of the FAP; [18], which focuses on the problem of setting the power emissions of base stations, proposing two distributed power control algorithms that are based on evolutionary computation techniques to fast solve the linear equation systems associated with power updates of the stations; [19], which proposes a genetic algorithm for addressing the joint problem of power, frequency and modulation scheme assignment in fixed networks based on the WiMAX technology. Moreover, it is interesting to note that other works have also tried to tackle sources of data uncertainty within wireless network design problems, such as [20,21], which adopt a stochastic optimization approach to find a robust location plan of base stations to deal with fluctuations in traffic demand, [22], which deals with the stochastic scheduling of 5G multimedia services and [23][24][25], which propose stochastic programming and robust optimization approaches to deal with signal propagation uncertainty of wireless technologies in real-world environments.…”

Section: Related Workmentioning

confidence: 99%

A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks

2020

Self Cite

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Because of the introduction and spread of the second generation of the Digital Video Broadcasting—Terrestrial standard (DVB-T2), already active television broadcasters and new broadcasters that have entered in the market will be required to (re)design their networks. This is generating a new interest for effective and efficient DVB optimization software tools. In this work, we propose a strengthened binary linear programming model for representing the optimal DVB design problem, including power and scheduling configuration, and propose a new matheuristic for its solution. The matheuristic combines a genetic algorithm, adopted to efficiently explore the solution space of power emissions of DVB stations, with relaxation-guided variable fixing and exact large neighborhood searches formulated as integer linear programming (ILP) problems solved exactly. Computational tests on realistic instances show that the new matheuristic performs much better than a state-of-the-art optimization solver, identifying solutions associated with much higher user coverage.

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“…In [46], the authors propose a genetic algorithm as a fast heuristic to solve large realistic instances of the power, frequency, and modulation assignment problem and show that the proposed genetic algorithm helps to improve the value of solutions found through a wider exploration of the power space. In [47], the authors propose a robust optimization model for tackling the uncertainty of wireless signal propagation in the design of fiber-based telecommunications access networks integrating wired and wireless technologies. Then, they propose a heuristic algorithm to find the solution to the proposed algorithm.…”

Section: Proposed Energy Saving Schemementioning

confidence: 99%

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

Kang

Chung

2017

Energies

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Abstract:Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with heterogeneous macro cells and small cells, where data and control planes are separated. We consider two types of data traffic, i.e., low rate data traffic and high rate data traffic. In basic separation architecture, a macro cell base station (MBS) manages control signals, while a small cell base station (SBS) manages both low rate data traffic and high rate data traffic. In the considered modified separation architecture, an MBS manages control signals and low rate data traffic, while an SBS manages high rate data traffic. Then, an efficient energy saving scheme for base stations (BSs) is proposed, where the state of a BS is determined depending on the number of user equipments (UEs) that request high rate data traffic and the number of UEs that exist under the overlapping areas commonly covered by the considered BS and the neighbor BSs. We formulate an optimization problem for the proposed energy saving scheme and obtain the solution using particle swarm optimization (PSO). Numerical results show that the proposed energy saving scheme in the modified separated network architecture has better energy efficiency compared to the conventional energy saving schemes in both basic and modified separated network architectures. Also, the proposed energy saving scheme has lower aggregate delay.

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Integrating LP-guided variable fixing with MIP heuristics in the robust design of hybrid wired-wireless FTTx access networks

Cited by 28 publications

References 38 publications

Parallel matheuristics for the discrete unit commitment problem with min‐stop ramping constraints

Parallel matheuristics for the discrete unit commitment problem with min‐stop ramping constraints

A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks

An Efficient Energy Saving Scheme for Base Stations in 5G Networks with Separated Data and Control Planes Using Particle Swarm Optimization

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