ddo 2.0.0

DDO a generic and efficient framework for MDD-based optimization.
Documentation
// Copyright 2020 Xavier Gillard
//
// Permission is hereby granted, free of charge, to any person obtaining a copy of
// this software and associated documentation files (the "Software"), to deal in
// the Software without restriction, including without limitation the rights to
// use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
// the Software, and to permit persons to whom the Software is furnished to do so,
// subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
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//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
// FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
// COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
// IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

//! This example uses ddo to solve the Aircraft Landing Problem

use std::time::{Duration, Instant};

use clap::Parser;
use ddo::*;

use crate::{io_utils::read_instance, model::{AlpRelax, AlpRanking, AlpDecision, RunwayState, Alp}, dominance::AlpDominance};

mod model;
mod dominance;
mod io_utils;

#[cfg(test)]
mod tests;

/// This structure uses `clap-derive` annotations and define the arguments that can
/// be passed on to the executable solver.
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// The path to the instance file
    fname: String,
    /// The number of concurrent threads
    #[clap(short, long, default_value = "8")]
    threads: usize,
    /// The maximum amount of time you would like this solver to run
    #[clap(short, long)]
    duration: Option<u64>,
    /// The maximum number of nodes per layer
    #[clap(short, long)]
    width: Option<usize>,
}

/// An utility function to return an max width heuristic that can either be a fixed width
/// policy (if w is fixed) or an adaptive policy returning the number of unassigned variables
/// in the overall problem.
fn max_width<P: Problem>(p: &P, w: Option<usize>) -> Box<dyn WidthHeuristic<P::State> + Send + Sync> {
    if let Some(w) = w {
        Box::new(FixedWidth(w))
    } else {
        Box::new(NbUnassignedWidth(p.nb_variables()))
    }
}
/// An utility function to return a cutoff heuristic that can either be a time budget policy
/// (if timeout is fixed) or no cutoff policy.
fn cutoff(timeout: Option<u64>) -> Box<dyn Cutoff + Send + Sync> {
    if let Some(t) = timeout {
        Box::new(TimeBudget::new(Duration::from_secs(t)))
    } else {
        Box::new(NoCutoff)
    }
}

/// This is your executable's entry point. It is the place where all the pieces are put together
/// to create a fast an effective solver for the knapsack problem.
fn main() {
    let args = Args::parse();
    let fname = &args.fname;
    let instance = read_instance(fname).unwrap();
    let problem = Alp::new(instance);
    let relaxation = AlpRelax::new(problem.clone());
    let ranking = AlpRanking;

    let width = max_width(&problem, args.width);
    let dominance = SimpleDominanceChecker::new(AlpDominance, problem.nb_variables());
    let cutoff = cutoff(args.duration);
    let mut fringe = NoDupFringe::new(MaxUB::new(&ranking));

    let mut solver = DefaultCachingSolver::custom(
        &problem, 
        &relaxation, 
        &ranking, 
        width.as_ref(), 
        &dominance,
        cutoff.as_ref(), 
        &mut fringe,
        args.threads,
    );

    let start = Instant::now();
    let Completion{ is_exact, best_value } = solver.maximize();
    
    let duration = start.elapsed();
    let upper_bound = - solver.best_upper_bound();
    let lower_bound = - solver.best_lower_bound();
    let gap = solver.gap();
    
    println!("Duration:   {:.3} seconds", duration.as_secs_f32());
    println!("Objective:  {}",            best_value.map(|v| -v).unwrap_or(-1));
    println!("Upper Bnd:  {}",            upper_bound);
    println!("Lower Bnd:  {}",            lower_bound);
    println!("Gap:        {:.3}",         gap);
    println!("Aborted:    {}",            !is_exact);
    
    let mut runways = vec![(RunwayState {prev_time:-1, prev_class: -1}, vec![]); problem.instance.nb_runways];
    let mut cur = problem.initial_state();
    if let Some(decisions) = solver.best_solution() {
        for decision in decisions {
            let AlpDecision { class, runway } = problem.from_decision(decision.value);
            let aircraft = problem.next[class][cur.rem[class]];
            let arrival = problem.get_arrival_time(&cur.info, aircraft, runway);
            
            runways[runway].0.prev_time = arrival;
            runways[runway].0.prev_class = problem.instance.classes[aircraft] as isize;
            runways[runway].1.push((arrival, aircraft));
            runways.sort_unstable();

            cur = problem.transition(&cur, decision);
        }
        
        for runway in runways {
            println!("{:?}", runway.1);
        }
    }
}