Finding regions of local repair in hierarchical constraint satisfaction

Algorithms for solving constraint satisfaction problems (CSP) have been successfully applied to several fields including scheduling, design, and planning. Latest extensions of the standard CSP to constraint optimization problems (COP) additionally provided new opportunities for solving several problems of combinatorial optimization more efficiently. Basically, two classes of algorithms have been used for searching constraint satisfaction problems (CSP): local search methods and systematic tree search extended by the classical constraint-processing techniques like e.g. forward checking and backmarking. Both classes exhibit characteristic advantages and drawbacks. This report presents a novel approach for solving constraint optimization problems that combines the advantages of local search and tree search algorithms which have been extended by constraint-processing techniques. This method proved applicability in a commercial nurse scheduling system as well as on randomly generated problems

A gyroscope calibration analysis for the Gamma Ray Observatory (GRO)

Current documentation and software do not adequately address the calculation and use of the optimal weight matrices involved in calibrating inertial reference units (IRU). Several facets of the GRO IRU calibration as it relates to the bias and misalignment weighting matrices are investigated. The physical meaning and use of the bias and misalignment weight matrices in IRU calibration are examined. The relation of the weighting and the final biases, misalignments, and their corrections are pursued. Methods for determining reliable, realistic weighting matrices to be used in the GRO IRU calibration (IRUCAL) utility are determined. Possible correlations among observation uncertainties are also explored. For the undetermined case where the maneuvers are insufficient to identify all calibration parameters, the weighting matrices allow as much information as possible to be extracted from the measurements. Finally, applicable simulated flight data are used, incorporating the appropriate calibration maneuvers, to test the weighting matrices in the IRUCAL utility, and examine correlation effects

Rescheduling with iterative repair

This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, produce modified schedules, quickly, and exhibits 'anytime' behavior. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. We also show the anytime characteristics of the system. These experiments were performed within the domain of Space Shuttle ground processing

Rescheduling with iterative repair

This paper presents a new approach to rescheduling called constraint-based iterative repair. This approach gives our system the ability to satisfy domain constraints, address optimization concerns, minimize perturbation to the original schedule, and produce modified schedules quickly. The system begins with an initial, flawed schedule and then iteratively repairs constraint violations until a conflict-free schedule is produced. In an empirical demonstration, we vary the importance of minimizing perturbation and report how fast the system is able to resolve conflicts in a given time bound. These experiments were performed within the domain of Space Shuttle ground processing

A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks

We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.Comment: This is the authors' final version of the paper published in G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I, LNCS 10199, pp. 1-17, 2017. DOI: 10.1007/978-3-319-55849-3\_16. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-55849-3_1

Bounds on Weighted CSPs Using Constraint Propagation and Super-Reparametrizations

We propose a framework for computing upper bounds on the optimal value of the (maximization version of) Weighted CSP (WCSP) using super-reparametrizations, which are changes of the weights that keep or increase the WCSP objective for every assignment. We show that it is in principle possible to employ arbitrary (under certain technical conditions) constraint propagation rules to improve the bound. For arc consistency in particular, the method reduces to the known Virtual AC (VAC) algorithm. Newly, we implemented the method for singleton arc consistency (SAC) and compared it to other strong local consistencies in WCSPs on a public benchmark. The results show that the bounds obtained from SAC are superior for many instance groups