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#[cfg(test)]
#[path = "../../../../tests/unit/solver/mutation/ruin/cluster_removal_test.rs"]
mod cluster_removal_test;
extern crate rand;
use super::*;
use crate::algorithms::dbscan::{create_clusters, Cluster, NeighborhoodFn};
use crate::algorithms::geometry::Point;
use crate::construction::heuristics::InsertionContext;
use crate::models::problem::Job;
use crate::models::Problem;
use crate::solver::RefinementContext;
use crate::utils::{compare_floats, Random};
use hashbrown::HashSet;
use rand::prelude::*;
use std::ops::Range;
use std::sync::{Arc, RwLock};
pub struct ClusterRemoval {
params: Vec<(usize, f64)>,
limit: JobRemovalLimit,
}
impl ClusterRemoval {
pub fn new(problem: Arc<Problem>, cluster_size: Range<usize>, limit: JobRemovalLimit) -> Self {
let min = cluster_size.start.max(3);
let max = cluster_size.end.min(problem.jobs.size()).max(min + 1);
let params = (min..max).map(|min_pts| (min_pts, estimate_epsilon(&problem, min_pts))).collect::<Vec<_>>();
Self { params, limit }
}
}
impl Ruin for ClusterRemoval {
fn run(&self, _: &mut RefinementContext, mut insertion_ctx: InsertionContext) -> InsertionContext {
let problem = insertion_ctx.problem.clone();
let random = insertion_ctx.random.clone();
let mut clusters = create_job_clusters(&problem, &random, self.params.as_slice());
clusters.shuffle(&mut rand::thread_rng());
let mut route_jobs = get_route_jobs(&insertion_ctx.solution);
let removed_jobs: RwLock<HashSet<Job>> = RwLock::new(HashSet::default());
let locked = insertion_ctx.solution.locked.clone();
let affected = get_removal_chunk_size(&insertion_ctx, &self.limit);
clusters.iter_mut().take_while(|_| removed_jobs.read().unwrap().len() < affected).for_each(|cluster| {
let left = affected - removed_jobs.read().unwrap().len();
if cluster.len() > left {
cluster.shuffle(&mut rand::thread_rng());
}
cluster.iter().filter(|job| !locked.contains(job)).take(left).for_each(|job| {
if let Some(rc) = route_jobs.get_mut(job) {
if rc.route_mut().tour.remove(&job) {
removed_jobs.write().unwrap().insert((*job).clone());
}
}
});
});
removed_jobs.write().unwrap().iter().for_each(|job| insertion_ctx.solution.required.push(job.clone()));
insertion_ctx
}
}
fn create_job_clusters<'a>(
problem: &'a Problem,
random: &Arc<dyn Random + Send + Sync>,
params: &[(usize, f64)],
) -> Vec<Cluster<'a, Job>> {
let profile = problem.fleet.profiles[random.uniform_int(0, problem.fleet.profiles.len() as i32 - 1) as usize];
let &(min_items, eps) = params.get(random.uniform_int(0, params.len() as i32 - 1) as usize).unwrap();
let eps = random.uniform_real(eps * 0.9, eps * 1.1);
let neighbor_fn: NeighborhoodFn<'a, Job> = Box::new(move |job, eps| {
Box::new(once(job).chain(
problem.jobs.neighbors(profile, job, 0.).take_while(move |(_, cost)| *cost < eps).map(|(job, _)| job),
))
});
create_clusters(problem.jobs.all_as_slice(), eps, min_items, &neighbor_fn)
}
fn estimate_epsilon(problem: &Problem, min_points: usize) -> f64 {
let mut costs = get_average_costs(problem, min_points);
costs.sort_by(|&a, &b| compare_floats(a, b));
let curve = costs.into_iter().enumerate().map(|(idx, cost)| Point::new(idx as f64, cost)).collect::<Vec<_>>();
get_max_curvature(curve.as_slice())
}
fn get_average_costs(problem: &Problem, min_points: usize) -> Vec<f64> {
let mut costs = problem.fleet.profiles.iter().fold(vec![0.; problem.jobs.size()], |mut acc, &profile| {
problem.jobs.all().enumerate().for_each(|(idx, job)| {
acc[idx] += problem
.jobs
.neighbors(profile, &job, 0.)
.nth(min_points - 1)
.map(|(_, cost)| *cost)
.unwrap_or(0.);
});
acc
});
costs.iter_mut().for_each(|cost| *cost /= problem.fleet.profiles.len() as f64);
costs
}
fn get_max_curvature(values: &[Point]) -> f64 {
if values.is_empty() {
return 0.;
}
let first = values.first().unwrap();
let last = values.last().unwrap();
values
.iter()
.fold((0., std::f64::MIN), |acc, p| {
let distance = p.distance_to_line(&first, &last);
if distance > acc.1 {
(p.y, distance)
} else {
acc
}
})
.0
}