Struct vrp_core::prelude::EvolutionConfigBuilder

source · 
pub struct EvolutionConfigBuilder<C, O, S, K>
where
    C: HeuristicContext<Objective = O, Solution = S> + Stateful<Key = K> + 'static,
    O: HeuristicObjective<Solution = S> + 'static,
    S: HeuristicSolution + 'static,
    K: Hash + Eq + Clone + Send + Sync + 'static,
{ /* private fields */ }
Expand description

Provides configurable way to build evolution configuration using fluent interface style.

Implementations§

source§

impl<C, O, S, K> EvolutionConfigBuilder<C, O, S, K>
where C: HeuristicContext<Objective = O, Solution = S> + Stateful<Key = K> + 'static, O: HeuristicObjective<Solution = S> + 'static, S: HeuristicSolution + 'static, K: Hash + Eq + Clone + Send + Sync + 'static,

source

pub fn with_max_generations( self, limit: Option<usize>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets max generations to be run by evolution. Default is 3000.

Examples found in repository?
examples/custom_objective.rs (line 143)
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158

fn main() -> GenericResult<()> {
    let transport = Arc::new(define_routing_data()?);

    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    let config = VrpConfigBuilder::new(problem.clone()).prebuild()?.with_max_generations(Some(10)).build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert_eq!(solution.unassigned.len(), 2, "expected two assigned jobs due to capacity constraint");
    assert_eq!(solution.routes.len(), 1, "only one tour should be there");
    assert_eq!(
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>(),
        vec![vec![0, 2, 4]],
        "tour doesn't serve only top-prio jobs"
    );
    assert_eq!(solution.cost, 545., "unexpected cost - closest to depot jobs should be assigned");

    Ok(())
}
More examples
Hide additional examples
examples/custom_constraint.rs (line 126)
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146

fn main() -> GenericResult<()> {
    let transport = Arc::new(define_routing_data()?);

    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    let config = VrpConfigBuilder::new(problem.clone()).prebuild()?.with_max_generations(Some(10)).build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert_eq!(
        solution.unassigned.len(),
        2,
        "expected two assigned jobs due to hardware requirement and capacity constraints"
    );
    assert_eq!(solution.routes.len(), 1, "only one tour should be there: second vehicle cannot serve hardware jobs");
    assert_eq!(solution.cost, 1050., "unexpected cost - closest to depot jobs should be assigned");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
examples/cvrp.rs (line 90)
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107

fn main() -> GenericResult<()> {
    // get routing data, see `./common/routing.rs` for details
    let transport = Arc::new(define_routing_data()?);

    // specify CVRP variant as problem definition and the goal of optimization
    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    // build a solver config with the predefined settings to run 5 secs or 10 generations at most
    let config = VrpConfigBuilder::new(problem.clone())
        .prebuild()?
        .with_max_time(Some(5))
        .with_max_generations(Some(10))
        .build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert!(solution.unassigned.is_empty(), "has unassigned jobs, but all jobs must be assigned");
    assert_eq!(solution.routes.len(), 2, "two tours are expected");
    assert_eq!(solution.cost, 2135., "unexpected cost (total distance traveled)");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
examples/pdptw.rs (line 101)
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118

fn main() -> GenericResult<()> {
    // get routing data, see `./common/routing.rs` for details
    let transport = Arc::new(define_routing_data()?);

    // specify PDPTW variant as problem definition and the goal of optimization
    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    // build a solver config with the predefined settings to run 5 secs or 10 generations at most
    let config = VrpConfigBuilder::new(problem.clone())
        .prebuild()?
        .with_max_time(Some(5))
        .with_max_generations(Some(10))
        .build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert!(solution.unassigned.is_empty(), "has unassigned jobs, but all jobs must be assigned");
    assert_eq!(solution.routes.len(), 1, "one tour should be there");
    assert_eq!(solution.cost, 1105., "unexpected cost (total distance traveled)");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
source

pub fn with_max_time( self, limit: Option<usize>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets max running time limit for evolution. Default is 300 seconds.

Examples found in repository?
examples/cvrp.rs (line 89)
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107

fn main() -> GenericResult<()> {
    // get routing data, see `./common/routing.rs` for details
    let transport = Arc::new(define_routing_data()?);

    // specify CVRP variant as problem definition and the goal of optimization
    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    // build a solver config with the predefined settings to run 5 secs or 10 generations at most
    let config = VrpConfigBuilder::new(problem.clone())
        .prebuild()?
        .with_max_time(Some(5))
        .with_max_generations(Some(10))
        .build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert!(solution.unassigned.is_empty(), "has unassigned jobs, but all jobs must be assigned");
    assert_eq!(solution.routes.len(), 2, "two tours are expected");
    assert_eq!(solution.cost, 2135., "unexpected cost (total distance traveled)");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
More examples
Hide additional examples
examples/pdptw.rs (line 100)
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118

fn main() -> GenericResult<()> {
    // get routing data, see `./common/routing.rs` for details
    let transport = Arc::new(define_routing_data()?);

    // specify PDPTW variant as problem definition and the goal of optimization
    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    // build a solver config with the predefined settings to run 5 secs or 10 generations at most
    let config = VrpConfigBuilder::new(problem.clone())
        .prebuild()?
        .with_max_time(Some(5))
        .with_max_generations(Some(10))
        .build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert!(solution.unassigned.is_empty(), "has unassigned jobs, but all jobs must be assigned");
    assert_eq!(solution.routes.len(), 1, "one tour should be there");
    assert_eq!(solution.cost, 1105., "unexpected cost (total distance traveled)");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
source

pub fn with_min_cv( self, min_cv: Option<(String, usize, f64, bool)>, key: K, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets variation coefficient termination criteria. Default is None.

source

pub fn with_target_proximity( self, target_proximity: Option<(Vec<f64>, f64)>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets target fitness and distance threshold as termination criteria.

source

pub fn with_initial( self, max_size: usize, quota: f64, operators: Vec<(Box<dyn InitialOperator<Objective = O, Context = C, Solution = S> + Send + Sync>, usize)>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets initial parameters used to construct initial population.

source

pub fn with_processing( self, processing: ProcessingConfig<C, O, S>, ) -> EvolutionConfigBuilder<C, O, S, K>

Specifies processing configuration.

source

pub fn with_init_solutions( self, solutions: Vec<S>, max_init_size: Option<usize>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets initial solutions in population. Default is no solutions in population.

source

pub fn with_objective( self, objective: Arc<dyn HeuristicObjective<Solution = S>>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets objective.

source

pub fn with_context(self, context: C) -> EvolutionConfigBuilder<C, O, S, K>

Sets heuristic context.

source

pub fn with_termination( self, termination: Box<dyn Termination<Context = C, Objective = O>>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets termination.

source

pub fn with_heuristic( self, heuristic: Box<dyn HyperHeuristic<Context = C, Objective = O, Solution = S>>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets a different heuristic replacing initial.

source

pub fn with_strategy( self, strategy: Box<dyn EvolutionStrategy<Context = C, Solution = S, Objective = O>>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets a different heuristic replacing initial.

source

pub fn with_search_operators( self, search_operators: Vec<(Arc<dyn HeuristicSearchOperator<Solution = S, Context = C, Objective = O> + Send + Sync>, String, f64)>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets search operators for dynamic heuristic.

source

pub fn with_diversify_operators( self, diversify_operators: Vec<Arc<dyn HeuristicDiversifyOperator<Context = C, Solution = S, Objective = O> + Send + Sync>>, ) -> EvolutionConfigBuilder<C, O, S, K>

Sets diversify operators for dynamic heuristic.

source

pub fn build(self) -> Result<EvolutionConfig<C, O, S>, GenericError>

Builds the evolution config.

Examples found in repository?
examples/custom_objective.rs (line 143)
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158

fn main() -> GenericResult<()> {
    let transport = Arc::new(define_routing_data()?);

    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    let config = VrpConfigBuilder::new(problem.clone()).prebuild()?.with_max_generations(Some(10)).build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert_eq!(solution.unassigned.len(), 2, "expected two assigned jobs due to capacity constraint");
    assert_eq!(solution.routes.len(), 1, "only one tour should be there");
    assert_eq!(
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>(),
        vec![vec![0, 2, 4]],
        "tour doesn't serve only top-prio jobs"
    );
    assert_eq!(solution.cost, 545., "unexpected cost - closest to depot jobs should be assigned");

    Ok(())
}
More examples
Hide additional examples
examples/custom_constraint.rs (line 126)
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146

fn main() -> GenericResult<()> {
    let transport = Arc::new(define_routing_data()?);

    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    let config = VrpConfigBuilder::new(problem.clone()).prebuild()?.with_max_generations(Some(10)).build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert_eq!(
        solution.unassigned.len(),
        2,
        "expected two assigned jobs due to hardware requirement and capacity constraints"
    );
    assert_eq!(solution.routes.len(), 1, "only one tour should be there: second vehicle cannot serve hardware jobs");
    assert_eq!(solution.cost, 1050., "unexpected cost - closest to depot jobs should be assigned");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
examples/cvrp.rs (line 91)
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107

fn main() -> GenericResult<()> {
    // get routing data, see `./common/routing.rs` for details
    let transport = Arc::new(define_routing_data()?);

    // specify CVRP variant as problem definition and the goal of optimization
    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    // build a solver config with the predefined settings to run 5 secs or 10 generations at most
    let config = VrpConfigBuilder::new(problem.clone())
        .prebuild()?
        .with_max_time(Some(5))
        .with_max_generations(Some(10))
        .build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert!(solution.unassigned.is_empty(), "has unassigned jobs, but all jobs must be assigned");
    assert_eq!(solution.routes.len(), 2, "two tours are expected");
    assert_eq!(solution.cost, 2135., "unexpected cost (total distance traveled)");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}
examples/pdptw.rs (line 102)
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118

fn main() -> GenericResult<()> {
    // get routing data, see `./common/routing.rs` for details
    let transport = Arc::new(define_routing_data()?);

    // specify PDPTW variant as problem definition and the goal of optimization
    let goal = define_goal(transport.clone())?;
    let problem = Arc::new(define_problem(goal, transport)?);

    // build a solver config with the predefined settings to run 5 secs or 10 generations at most
    let config = VrpConfigBuilder::new(problem.clone())
        .prebuild()?
        .with_max_time(Some(5))
        .with_max_generations(Some(10))
        .build()?;

    // run the VRP solver and get the best known solution
    let solution = Solver::new(problem, config).solve()?;

    assert!(solution.unassigned.is_empty(), "has unassigned jobs, but all jobs must be assigned");
    assert_eq!(solution.routes.len(), 1, "one tour should be there");
    assert_eq!(solution.cost, 1105., "unexpected cost (total distance traveled)");

    // simple way to explore the solution, more advanced are available too
    println!(
        "\nIn solution, locations are visited in the following order:\n{:?}\n",
        solution.get_locations().map(Iterator::collect::<Vec<_>>).collect::<Vec<_>>()
    );

    Ok(())
}

Trait Implementations§

source§

impl<C, O, S, K> Default for EvolutionConfigBuilder<C, O, S, K>
where C: HeuristicContext<Objective = O, Solution = S> + Stateful<Key = K> + 'static, O: HeuristicObjective<Solution = S> + 'static, S: HeuristicSolution + 'static, K: Hash + Eq + Clone + Send + Sync + 'static,

source§

fn default() -> EvolutionConfigBuilder<C, O, S, K>

Returns the “default value” for a type. Read more

Auto Trait Implementations§

§

impl<C, O, S, K> Freeze for EvolutionConfigBuilder<C, O, S, K>
where C: Freeze, K: Freeze,

§

impl<C, O, S, K> !RefUnwindSafe for EvolutionConfigBuilder<C, O, S, K>

§

impl<C, O, S, K> !Send for EvolutionConfigBuilder<C, O, S, K>

§

impl<C, O, S, K> !Sync for EvolutionConfigBuilder<C, O, S, K>

§

impl<C, O, S, K> Unpin for EvolutionConfigBuilder<C, O, S, K>
where C: Unpin, K: Unpin, S: Unpin,

§

impl<C, O, S, K> !UnwindSafe for EvolutionConfigBuilder<C, O, S, K>

Blanket Implementations§

source§

impl<T> Any for T
where T: 'static + ?Sized,

source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
source§

impl<T> Borrow<T> for T
where T: ?Sized,

source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
source§

impl<T> From<T> for T

source§

fn from(t: T) -> T

Returns the argument unchanged.

source§

impl<T, U> Into<U> for T
where U: From<T>,

source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

source§

impl<T> IntoEither for T

source§

fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
source§

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
source§

impl<T> Pointable for T

source§

const ALIGN: usize = _

The alignment of pointer.
§

type Init = T

The type for initializers.
source§

unsafe fn init(init: <T as Pointable>::Init) -> usize

Initializes a with the given initializer. Read more
source§

unsafe fn deref<'a>(ptr: usize) -> &'a T

Dereferences the given pointer. Read more
source§

unsafe fn deref_mut<'a>(ptr: usize) -> &'a mut T

Mutably dereferences the given pointer. Read more
source§

unsafe fn drop(ptr: usize)

Drops the object pointed to by the given pointer. Read more
source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

§

type Error = Infallible

The type returned in the event of a conversion error.
source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
source§

impl<V, T> VZip<V> for T
where V: MultiLane<T>,

source§

fn vzip(self) -> V