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

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

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 %

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