Google OR-Tools v9.11
a fast and portable software suite for combinatorial optimization
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#include <cp_model_lns.h>
Public Member Functions | |
SchedulingTimeWindowNeighborhoodGenerator (NeighborhoodGeneratorHelper const *helper, absl::string_view name) | |
Neighborhood | Generate (const CpSolverResponse &initial_solution, double difficulty, absl::BitGenRef random) final |
Public Member Functions inherited from operations_research::sat::NeighborhoodGenerator | |
NeighborhoodGenerator (absl::string_view name, NeighborhoodGeneratorHelper const *helper) | |
virtual | ~NeighborhoodGenerator ()=default |
virtual bool | ReadyToGenerate () const |
Returns true if the neighborhood generator can generate a neighborhood. | |
double | GetUCBScore (int64_t total_num_calls) const |
void | AddSolveData (SolveData data) |
double | Synchronize () |
std::string | name () const |
Returns a short description of the generator. | |
int64_t | num_calls () const |
Number of times this generator was called. | |
int64_t | num_fully_solved_calls () const |
Number of time the neighborhood was fully solved (OPTIMAL/INFEASIBLE). | |
int64_t | num_improving_calls () const |
Out of num_calls(), how many improved the given solution. | |
int64_t | num_consecutive_non_improving_calls () const |
double | difficulty () const |
The current difficulty of this generator. | |
double | deterministic_limit () const |
The current time limit that the sub-solve should use on this generator. | |
Additional Inherited Members | |
Protected Attributes inherited from operations_research::sat::NeighborhoodGenerator | |
const std::string | name_ |
const NeighborhoodGeneratorHelper & | helper_ |
absl::Mutex | generator_mutex_ |
Similar to SchedulingNeighborhoodGenerator except the set of intervals that are relaxed are from a specific random time interval.
Definition at line 664 of file cp_model_lns.h.
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inlineexplicit |
Definition at line 666 of file cp_model_lns.h.
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finalvirtual |
Generates a "local" subproblem for the given seed.
The difficulty will be in [0, 1] and is related to the asked neighborhood size (and thus local problem difficulty). A difficulty of 0.0 means empty neighborhood and a difficulty of 1.0 means the full problem. The algorithm should try to generate a neighborhood according to this difficulty which will be dynamically adjusted depending on whether or not we can solve the subproblem in a given time limit.
The given initial_solution should contain a feasible solution to the initial CpModelProto given to this class. Any solution to the returned CPModelProto should also be valid solution to the same initial model.
This function should be thread-safe.
Implements operations_research::sat::NeighborhoodGenerator.
Definition at line 2043 of file cp_model_lns.cc.