146 research outputs found
A Multicore Tool for Constraint Solving
*** To appear in IJCAI 2015 proceedings *** In Constraint Programming (CP), a
portfolio solver uses a variety of different solvers for solving a given
Constraint Satisfaction / Optimization Problem. In this paper we introduce
sunny-cp2: the first parallel CP portfolio solver that enables a dynamic,
cooperative, and simultaneous execution of its solvers in a multicore setting.
It incorporates state-of-the-art solvers, providing also a usable and
configurable framework. Empirical results are very promising. sunny-cp2 can
even outperform the performance of the oracle solver which always selects the
best solver of the portfolio for a given problem
SUNNY-CP and the MiniZinc Challenge
In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems
In the context of Constraint Programming, a portfolio
approach exploits the complementary strengths of a portfolio of
different constraint solvers. The goal is to predict and run the best
solver(s) of the portfolio for solving a new, unseen problem. In
this work we reproduce, simulate, and evaluate the performance
of different portfolio approaches on extensive benchmarks of
Constraint Satisfaction Problems. Empirical results clearly show
the benefits of portfolio solvers in terms of both solved instances
and solving time
SUNNY: a Lazy Portfolio Approach for Constraint Solving
*** To appear in Theory and Practice of Logic Programming (TPLP) ***
Within the context of constraint solving, a portfolio approach allows one to
exploit the synergy between different solvers in order to create a globally
better solver. In this paper we present SUNNY: a simple and flexible algorithm
that takes advantage of a portfolio of constraint solvers in order to compute
--- without learning an explicit model --- a schedule of them for solving a
given Constraint Satisfaction Problem (CSP). Motivated by the performance
reached by SUNNY vs. different simulations of other state of the art
approaches, we developed sunny-csp, an effective portfolio solver that exploits
the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests
conducted on exhaustive benchmarks of MiniZinc models show that the actual
performance of SUNNY conforms to the predictions. This is encouraging both for
improving the power of CSP portfolio solvers and for trying to export them to
fields such as Answer Set Programming and Constraint Logic Programming
feasibility study of a chp plant with steam turbine and biomass gasification for tissue paper production
Abstract In remote areas, such as India, Africa and Southeast Asia, typically not connected to the natural gas distribution network, tissue paper production is currently carried out using a Yankee cylinder and two hot air hoods heated with saturated steam produced in conventional boilers. In this way the drying air is heated at medium temperature, around 160-180 °C, with consequent low levels of dried paper production. In this context, the present study intends to evaluate the technical and economic feasibility of using a wood biomass fixed bed downdraft gasification plant for the production of syngas to be used as fuel in gas hoods, in order to reach high drying temperatures (around 500 °C), comparable with those of the current modern hoods powered with natural gas. Using previously developed calculation codes, an evaluation of energy performance of the paper drying system and of the gasification plant has been performed. The present study also evaluates the possibility of applying a CHP plant, powered by biomass, for the production of steam and electric, this last obtained adopting a steam turbine, thus covering all the electrical and thermal needs of the paper mill. Results show that, for a paper mill with a production of about 80 t/day of paper, two gasification reactors with a thermal output of about 1.95 MWt, and a consumption of dry biomass of 0.86 t/h, are required. For the steam system, the net electric power needed to meet the needs of the paper mill is about 3100 kW with a consumption of 4.72 t/h of moist biomass and a net efficiency of 23.9 %
Performance and Economic Comparison of Solar Cooling Configurations
In this paper a performance and economic comparison of solar cooling configurations using a new integrated approach combining the hourly thermal-optical performance assessment of the solar systems with the economic aspects has been conducted. Evacuated tube solar collectors with single effect LiBr absorption chiller and compact solar linear concentrating Fresnel collectors with single effect or medium temperature double effect LiBr absorption chiller have been taken into account. Considering that all the produced cold thermal energy could be delivered to a final user, the latter solar cooling configuration shows the possibility to have the Levelized Cost Of Cooling (LCOC) comparable with standard electric compression cooling. However, technology improvements and economy of scale are necessary in order to reduce solar field cost in the range 150-250 €/m2
On the Evaluation of (Meta-)solver Approaches
Meta-solver approaches exploit many individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can adopt the metrics typically used for individual solvers (e.g., runtime or solution quality) or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers by exposing their strengths and weaknesses
A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing
Disasters like terrorist attacks, earthquakes, hurricanes, and volcano eruptions are usually unpredictable events that affect a high number of people. We propose an approach that could be used as a decision support tool for a post-disaster response that allows the assignment of victims to hospitals and organizes their transportation via emergency vehicles. By exploiting the synergy between Mixed Integer Programming and Constraint Programming techniques, we are able to compute the routing of the vehicles so as to rescue much more victims than both heuristic based and complete approaches in a very reasonable time
sunny-as2: Enhancing SUNNY for Algorithm Selection
SUNNY is an Algorithm Selection (AS) technique originally tailored for
Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of
solvers, a subset of solvers to be run on a given CP problem. This approach has
proved to be effective for CP problems, and its parallel version won many gold
medals in the Open category of the MiniZinc Challenge -- the yearly
international competition for CP solvers. In 2015, the ASlib benchmarks were
released for comparing AS systems coming from disparate fields (e.g., ASP, QBF,
and SAT) and SUNNY was extended to deal with generic AS problems. This led to
the development of sunny-as2, an algorithm selector based on SUNNY for ASlib
scenarios. A preliminary version of sunny-as2 was submitted to the Open
Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the
best approach for the runtime minimization of decision problems. In this work,
we present the technical advancements of sunny-as2, including: (i)
wrapper-based feature selection; (ii) a training approach combining feature
selection and neighbourhood size configuration; (iii) the application of nested
cross-validation. We show how sunny-as2 performance varies depending on the
considered AS scenarios, and we discuss its strengths and weaknesses. Finally,
we also show how sunny-as2 improves on its preliminary version submitted to
OASC
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