Class SmallestSetupPrecompute

  • All Implemented Interfaces:

    public final class SmallestSetupPrecompute
    extends Object

    This heuristic is the smallest setup first. We define it as: h(j) = 1 / (1 + s[i][j]), where s[i][j] is the setup time of job j if it follows job i on the machine.

    In this version, the heuristic is precomputed for all pairs of jobs (e.g., for evaluating job j for each possible preceding job). This may speed up stochastic sampling search when many iterations are executed (won't need to recompute the same heuristic values repeatedly). However, for large problems, the O(n2) space, where n is the number of jobs may be prohibitive. For a version that doesn't precompute the heuristic, see the SmallestSetup class, which requires only O(1) space.

    • Field Detail

      • MIN_H

        public static final double MIN_H
        The minimum heuristic value. If the heuristic value as calculated is lower than MIN_H, then MIN_H is used as the heuristic value. The reason is related to the primary purpose of the constructive heuristics in the library: heuristic guidance for stochastic sampling algorithms, which assume positive heuristic values (e.g., an h of 0 would be problematic).
        See Also:
        Constant Field Values
    • Constructor Detail

      • SmallestSetupPrecompute

        public SmallestSetupPrecompute​(SingleMachineSchedulingProblem problem)
        Constructs an SmallestSetupPrecompute heuristic.
        problem - The instance of a scheduling problem that is the target of the heuristic.
    • Method Detail

      • h

        public double h​(Partial<Permutation> p,
                        int element,
                        IncrementalEvaluation<Permutation> incEval)
        Description copied from interface: ConstructiveHeuristic
        Heuristically evaluates the possible addition of an element to the end of a Partial. Higher evaluations imply that the element is a better choice for the next element to add. For example, if you evaluate two elements, x and y, with h, and h returns a higher value for y than for x, then this means that y is believed to be the better choice according to the heuristic. Implementations of this interface must ensure that h always returns a positive result. This is because stochastic sampling algorithms such as HBSS and VBSS assume that the constructive heuristic returns only positive values.
        p - The current state of the Partial
        element - The element under consideration for adding to the Partial
        incEval - An IncrementalEvaluation of p. This method assumes that incEval is of the same runtime type as the object returned by ConstructiveHeuristic.createIncrementalEvaluation().
        The heuristic evaluation of the hypothetical addition of element to the end of p. The higher the evaluation, the more important the heuristic believes that element should be added next. The intention is to compare the value returned with the heuristic evaluations of other elements. Individual results in isolation are not necessarily meaningful.
      • createPartial

        public final Partial<Permutation> createPartial​(int n)
        Description copied from interface: ConstructiveHeuristic
        Creates an empty Partial solution, which will be incrementally transformed into a complete solution of a specified length.
        Specified by:
        createPartial in interface ConstructiveHeuristic<Permutation>
        n - the desired length of the final complete solution.
        an empty Partial solution
      • completeLength

        public final int completeLength()
        Description copied from interface: ConstructiveHeuristic
        Gets the required length of complete solutions to the problem instance for which this constructive heuristic is configured.
        Specified by:
        completeLength in interface ConstructiveHeuristic<Permutation>
        length of solutions to the problem instance for which this heuristic is configured