Class WeightedShortestProcessingPlusSetupTime

All Implemented Interfaces:
Direct Known Subclasses:
ApparentTardinessCostSetupAdjusted, WeightedCostOverTimeSetupAdjusted, WeightedCriticalRatioSetupAdjusted, WeightedShortestProcessingPlusSetupTimeLateOnly

public class WeightedShortestProcessingPlusSetupTime extends Object
This class implements a variation the weighted shortest process time heuristic, but adjusted to incorporate setups times for problems with sequence-dependent setups. The original version of the heuristic can be found in the WeightedShortestProcessingTime class, and is defined as: h(j) = w[j] / p[j], where w[j] is the weight of job j, and p[j] is its processing time.

We modify this to incorporate setup times by instead defining the heuristic as: h(j) = w[j] / (p[j] + s[i][j]), where s[i][j] is the setup time required by job j if it immediately follows job i on the machine, where job i is the preceding job.

Furthermore, this implementation returns: max( MIN_H, h(j)), where MIN_H is a small non-zero value. This is to deal with the possibility of a job with weight w[j] = 0, or especially high processing and setup times relative to weight. For deterministic construction of a schedule, this adjustment is unnecessary. However, for stochastic sampling algorithms it is important for the heuristic to return non-zero values.

The heuristic values are computed each time the h(<org.cicirello.permutations.Permutation>, int,<org.cicirello.permutations.Permutation>) method is called. Therefore, for many iterations of stochastic sampling, the same heuristic values may be computed repeatedly. If your problem instance is small enough to be able to afford the extra memory, you might consider instead using the WeightedShortestProcessingPlusSetupTimePrecompute class, which implements the same heuristic, but it precomputes a table of heuristic values upon constructing the heuristic object.

  • 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

    • WeightedShortestProcessingPlusSetupTime

      public WeightedShortestProcessingPlusSetupTime(SingleMachineSchedulingProblem problem)
      Constructs an WeightedShortestProcessingPlusSetupTime heuristic.
      problem - The instance of a scheduling problem that is the target of the heuristic.
  • 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.
      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.
    • 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>
      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>
      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