A Matheuristic Algorithm for a Large-Scale Energy Management Problem

Anghinolfi, Davide; Gambardella, L. M.; Montemanni, Roberto; Nattero, Cristiano; Paolucci, Massimo; Toklu, Nihat Engin

doi:10.1007/978-3-642-29843-1_19

Cited by 11 publications

(10 citation statements)

References 6 publications

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“…An aggressive local search, LocalSolver's forebear [29], provided most of the reactualized best primal solutions [15]. Heuristic decomposition approaches separate the maintenance and refueling planning decisions from the production optimization, and computes productions independently for each scenario s, as in [30][31][32][33]. Such approaches are less efficient.…”

Section: Solving Methodsmentioning

confidence: 99%

“…Under some hypotheses, it is proven such aggregation induces lower bounds of the original problem [17]. Secondly, many approaches used computations reduced on single scenarios, aggregating the scenarios into one average deterministic scenario as in [27], or using decomposition as in [25,26,[30][31][32][33]. It is proven that lower bounds of the EURO/ROADEF Challenge 2010 can be guaranteed with S single scenario computations [17].…”

Section: Reductions By Pre-processingmentioning

confidence: 99%

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Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

Dupin

Talbi

2020

Algorithms

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This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling and maintenance planning of nuclear power plants. Several MIP relaxations are presented to provide dual bounds computing smaller MIPs than the original problem. It is proven how to get dual bounds with scenario decomposition in the different 2-stage programming MILP formulations, with a selection of scenario guided by machine learning techniques. Several sets of dual bounds are computable, improving significantly the former best dual bounds of the literature and justifying the quality of the best primal solution known.

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Section: Solving Methodsmentioning

confidence: 99%

Section: Reductions By Pre-processingmentioning

confidence: 99%

Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

Dupin

Talbi

2020

Algorithms

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“…Some studies address UC jointly to the GMS problem or/and take into account the transmission network (see Section 2.2). Other authors deal with a more specific problem including an accurate fuel management (Anghinolfi et al, 2012;Brandt et al, 2013;Buljubasic and Gavranovic, 2012;Fourcade et al, 1997;Godskesen et al, 2013;Gorge et al, 2012;Khemmoudj et al, 2006;Jost and Savourey, 2013;Lusby et al, 2013;Rozenknop et al, 2013). We discuss this issue in more detail in Section 2.4.…”

Section: Regulated Power Systemsmentioning

confidence: 99%

“…Its stochastic nature can be explicitly considered. A set of scenarios that model alternative demands is used in (Anghinolfi et al, 2012;Brandt et al, 2013;Buljubasic and Gavranovic, 2012;Canto, 2008;Canto and Rubio-Romero, 2013;Godskesen et al, 2013;Gorge et al, 2012;Jost and Savourey, 2013;Lusby et al, 2013;Rozenknop et al, 2013). The maintenance decisions ensure that the demand is met in all the scenarios.…”

Section: Management Of Uncertaintymentioning

confidence: 99%

Maintenance scheduling in the electricity industry: A literature review

Froger

Gendreau

Mendoza

et al. 2016

European Journal of Operational Research

142

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The reliability of the power plants and transmission lines in the electricity industry is crucial for meeting demand. Consequently, timely maintenance plays a major role reducing breakdowns and avoiding expensive production shutdowns. By now, the literature contains a sound body of work focused on improving decision making in generation unit and transmission line maintenance scheduling. The purpose of this paper is to review that literature. We update previous surveys and provide a more global view of the problem: we study both regulated and deregulated power systems and explore some important features such as network considerations, fuel management, and data uncertainty.

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“…In order to solve the mathematically stronger yet still complex power-indexed model, we propose a new matheuristic, i.e., a heuristic algorithm based on combining mathematical programming techniques and metaheuristics (see e.g., [8][9][10]). Specifically, our new matheuristic is based on combining a Genetic Algorithm (GA) with variable fixing heuristics exploiting a suitable (tight) linear relaxation of the problem and an Integer Linear Programming (ILP) improvement heuristic.…”

mentioning

confidence: 99%

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

2020

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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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A Matheuristic Algorithm for a Large-Scale Energy Management Problem

Cited by 11 publications

References 6 publications

Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

Maintenance scheduling in the electricity industry: A literature review

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

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