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Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

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/*
 * Chips-n-Salsa: A library of parallel self-adaptive local search algorithms.
 * Copyright (C) 2002-2023 Vincent A. Cicirello
 *
 * This file is part of Chips-n-Salsa (https://chips-n-salsa.cicirello.org/).
 *
 * Chips-n-Salsa is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Chips-n-Salsa is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package org.cicirello.search.problems.scheduling;

import java.util.Scanner;

/**
 * Package-access parser for benchmark common duedate scheduling instances from the OR-Library.
 * Parses an instance data file that follows the format specified in the OR-Library of J.E. Beasley.
 * The description,
 * along with a set of benchmark instances, is mirrored in the following GitHub repository: https://github.com/cicirello/scheduling-benchmarks
 *
 * The first line of the file has the number of instances in the file. This is then followed by
 * the data for each instance in the following form. Number of jobs, n, for the instance on a line
 * by itself. This is then followed by n lines, one for each job, where the line consists of 3
 * integers: process time, earliness weight, and tardiness weight. These are separated by
 * whitespace. Don't assume any specific number of whitespace characters. This seems to vary. Lines
 * may also begin with whitespace.
 *
 * The h parameter is not specified in the file, and each instance in a file can be used to
 * specify multiple benchmark instances with varying degrees of duedate tightness. The instances in
 * the OR-Library assume values of h equal to 0.2, 0.4, 0.6, and 0.8 the OR-Library provides bounds
 * on optimal solutions for those values of h), but you can potentially define additional instances
 * using additional values of h. The only constraint on h is: 0.0 ≤ h ≤ 1.0. It is used to
 * define the common duedate for the instance as a percentage of the sum of process times.
 *
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
final class CommonDuedateInstanceReader {

  private final int[] process;
  private final int[] earlyWeights;
  private final int[] weights;
  private final int duedate;

  /**
   * Parses an instance of the common duedate scheduling instances from the OR-Library.
   *
   * @param file The file with the set of instances from the OR-library.
   * @param instanceNumber The number of the instance from the file to parse.
   * @param h Controls the tightness of the common duedate for the instance, as a percentage of the
   *     sum of process times, 0.0 ≤ h ≤ 1.0.
   */
  public CommonDuedateInstanceReader(Readable file, int instanceNumber, double h) {
    if (instanceNumber < 0)
      throw new IllegalArgumentException("instanceNumber must be nonnegative");
    if (h < 0 || h > 1) throw new IllegalArgumentException("h must be in [0.0, 1.0]");
    Scanner in = new Scanner(file);

    String line = in.nextLine();
    Scanner lineScanner = new Scanner(line);
    int numInstances = lineScanner.nextInt();
    lineScanner.close();
    if (instanceNumber >= numInstances) {
      in.close();
      throw new IllegalArgumentException("instanceNumber is too high.");
    }

    for (int i = 0; i < instanceNumber; i++) {
      skipInstance(in);
    }

    line = in.nextLine();
    lineScanner = new Scanner(line);
    int numJobs = lineScanner.nextInt();
    lineScanner.close();

    process = new int[numJobs];
    earlyWeights = new int[numJobs];
    weights = new int[numJobs];
    int totalP = 0;
    for (int i = 0; i < numJobs; i++) {
      lineScanner = new Scanner(in.nextLine());
      process[i] = lineScanner.nextInt();
      totalP += process[i];
      earlyWeights[i] = lineScanner.nextInt();
      weights[i] = lineScanner.nextInt();
      lineScanner.close();
    }
    duedate = (int) (totalP * h);

    in.close();
  }

  /**
   * Gets the array of process times.
   *
   * @return the process times
   */
  public int[] processTimes() {
    return process;
  }

  /**
   * Gets the array of early weights.
   *
   * @return the early weights
   */
  public int[] earlyWeights() {
    return earlyWeights;
  }

  /**
   * Gets the array of weights.
   *
   * @return the weights
   */
  public int[] weights() {
    return weights;
  }

  /**
   * Gets the common duedate.
   *
   * @return the common duedate
   */
  public int duedate() {
    return duedate;
  }

  private void skipInstance(Scanner in) {
    String line = in.nextLine();
    Scanner lineScanner = new Scanner(line);
    int numJobs = lineScanner.nextInt();
    lineScanner.close();
    for (int i = 0; i < numJobs; i++) {
      in.nextLine();
    }
  }
}