Class ApparentTardinessCostSetupAdjusted

java.lang.Object
org.cicirello.search.problems.scheduling.WeightedShortestProcessingPlusSetupTime
org.cicirello.search.problems.scheduling.ApparentTardinessCostSetupAdjusted
All Implemented Interfaces:
Splittable<ConstructiveHeuristic<Permutation>>, ConstructiveHeuristic<Permutation>
public final class ApparentTardinessCostSetupAdjusted extends WeightedShortestProcessingPlusSetupTime
This is an implementation of a variation of the Apparent Tardiness Cost (ATC) heuristic, with a simple adjustment for setup times for problems with sequence-dependent setups. Note that this is NOT the Apparent Tardiness Cost with Setups (ATCS) heuristic, although that heuristic can also be found in the library.

ATC is defined as: h(j) = (w[j]/p[j]) exp( -max(0,S(j)) / (k p̄) ), where w[j] is the weight of job j, p[j] is its processing time, and S(j) is a calculation of the slack of job j where slack S(j) is d[j] - T - p[j] - s[i][j]. The d[j] is the job's due date, T is the current time, and s[i][j] is setup time of the job if it follows job i on the machine (for problems with setup times). The k is a parameter that can be tuned based on problem instance characteristics, and p̄ is the average processing time of remaining unscheduled jobs.

Our simple adjustment for setup time is as follows: h(j) = (w[j]/(p[j]+s[i][j])) exp( -max(0,S(j)) / (k p̄) ).

The constant MIN_H defines the minimum value the heuristic will return, preventing h(j)=0 in support of stochastic sampling algorithms for which h(j)=0 is problematic. This implementation returns max( MIN_H, h(j)), where MIN_H is a small non-zero value.

  • Field Details

    • 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:

  • Constructor Details

    • ApparentTardinessCostSetupAdjusted

      public ApparentTardinessCostSetupAdjusted(SingleMachineSchedulingProblem problem, SingleMachineSchedulingProblemData data, double k)
      Constructs an ApparentTardinessCostSetupAdjusted heuristic.
      Parameters:
      problem - The cost function of a scheduling problem that is the target of the heuristic.
      data - The instance specific data.
      k - A parameter to the heuristic, which must be positive. Typical good values are in the interval [1.0, 4.0] but it is not limited to that interval.
      Throws:
      IllegalArgumentException - if problem.hasDueDates() returns false.
      IllegalArgumentException - if k ≤ 0.0.

    • ApparentTardinessCostSetupAdjusted

      public ApparentTardinessCostSetupAdjusted(SingleMachineSchedulingProblem problem, SingleMachineSchedulingProblemData data)
      Constructs an ApparentTardinessCostSetupAdjusted heuristic. Uses a default of k=2.
      Parameters:
      problem - The cost function of a scheduling problem that is the target of the heuristic.
      data - The instance specific data.
      Throws:
      IllegalArgumentException - if problem.hasDueDates() returns false.

  • Method Details

    • 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.
      Specified by:
      h in interface ConstructiveHeuristic<Permutation>
      Overrides:
      h in class WeightedShortestProcessingPlusSetupTime
      Parameters:
      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().
      Returns:
      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.

    • createIncrementalEvaluation

      public IncrementalEvaluation<Permutation> createIncrementalEvaluation()
      Description copied from interface: ConstructiveHeuristic
      Creates an IncrementalEvaluation object corresponding to an initially empty Partial for use in incrementally constructing a solution to the problem for which this heuristic is designed. The object returned incrementally computes any data associated with a Partial as needed by the ConstructiveHeuristic.h(Partial, int, IncrementalEvaluation) method. The ConstructiveHeuristic.h(Partial, int, IncrementalEvaluation) method will assume that it will be given an object of the specific runtime type returned by this method. It is unsafe to pass IncrementalEvaluation objects created by one heuristic to the ConstructiveHeuristic.h(Partial, int, IncrementalEvaluation) method of another.

      The default implementation simply returns null, which is appropriate for heuristics that won't benefit from incrementally computing heuristic information.

      Returns:
      An IncrementalEvaluation for an empty Partial to be used for incrementally computing any data required by the ConstructiveHeuristic.h(Partial, int, IncrementalEvaluation) method.

    • getProblem

      public final Problem<Permutation> getProblem()
      Description copied from interface: ConstructiveHeuristic
      Gets a reference to the instance of the optimization problem that is the subject of this heuristic.
      Specified by:
      getProblem in interface ConstructiveHeuristic<Permutation>
      Returns:
      the instance of the optimization problem that is the subject of this heuristic.

    • 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>
      Parameters:
      n - the desired length of the final complete solution.
      Returns:
      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>
      Returns:
      length of solutions to the problem instance for which this heuristic is configured