425,968 research outputs found
Iterative repair for scheduling and rescheduling
An iterative repair search method is described called constraint based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. The utility of the constraint based framework is shown by comparing search performance with and without the constraint framework on a suite of randomly generated problems. Results are also shown of applying the technique to the NASA Space Shuttle ground processing problem. These experiments show that the search methods scales to complex, real world problems and reflects interesting anytime behavior
Toward an automaton Constraint for Local Search
We explore the idea of using finite automata to implement new constraints for
local search (this is already a successful technique in constraint-based global
search). We show how it is possible to maintain incrementally the violations of
a constraint and its decision variables from an automaton that describes a
ground checker for that constraint. We establish the practicality of our
approach idea on real-life personnel rostering problems, and show that it is
competitive with the approach of [Pralong, 2007]
Building a Truly Distributed Constraint Solver with JADE
Real life problems such as scheduling meeting between people at different
locations can be modelled as distributed Constraint Satisfaction Problems
(CSPs). Suitable and satisfactory solutions can then be found using constraint
satisfaction algorithms which can be exhaustive (backtracking) or otherwise
(local search). However, most research in this area tested their algorithms by
simulation on a single PC with a single program entry point. The main
contribution of our work is the design and implementation of a truly
distributed constraint solver based on a local search algorithm using Java
Agent DEvelopment framework (JADE) to enable communication between agents on
different machines. Particularly, we discuss design and implementation issues
related to truly distributed constraint solver which might not be critical when
simulated on a single machine. Evaluation results indicate that our truly
distributed constraint solver works well within the observed limitations when
tested with various distributed CSPs. Our application can also incorporate any
constraint solving algorithm with little modifications.Comment: 7 page
Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results
Using constraint-based local search, we effectively model and efficiently
solve the problem of balancing the traffic demands on portions of the European
airspace while ensuring that their capacity constraints are satisfied. The
traffic demand of a portion of airspace is the hourly number of flights planned
to enter it, and its capacity is the upper bound on this number under which
air-traffic controllers can work. Currently, the only form of demand-capacity
balancing we allow is ground holding, that is the changing of the take-off
times of not yet airborne flights. Experiments with projected European flight
plans of the year 2030 show that already this first form of demand-capacity
balancing is feasible without incurring too much total delay and that it can
lead to a significantly better demand-capacity balance
Parallel constraint-based local search on the HA8000 supercomputer (abstract)
We present a parallel implementation of a constraint-based local search algorithm and investigate its performance re- sults on hardware with several hundreds of processors
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