Class WeightedLateness

java.lang.Object
org.cicirello.search.problems.scheduling.WeightedLateness
All Implemented Interfaces:
IntegerCostOptimizationProblem<Permutation>, Problem<Permutation>, SingleMachineSchedulingProblem
public final class WeightedLateness extends Object implements SingleMachineSchedulingProblem
Implements the scheduling cost function known as weighted lateness. The lateness L[j] of job j is: L[j] = C[j] - d[j], where C[j] is the time it is completed by the machine, and d[j] is its due date. The weighted lateness cost function is the weighted sum over the jobs of: w[j]L[j], where w[j] is job j's weight. Note that the lateness of a job can be negative if the job is completed early, so this cost function can evaluate to a negative value.

  • Constructor Details

    • WeightedLateness

      public WeightedLateness(SingleMachineSchedulingProblemData instanceData)
      Constructs a single machine scheduling problem for minimizing weighted lateness.
      Parameters:
      instanceData - An encapsulation of the job characteristics, such as processing times, etc.
      Throws:
      IllegalArgumentException - if instanceData.hasDueDates() returns false.

  • Method Details

    • cost

      public int cost(Permutation candidate)
      Description copied from interface: IntegerCostOptimizationProblem
      Computes the cost of a candidate solution to the problem instance. The lower the cost, the more optimal the candidate solution.
      Specified by:
      cost in interface IntegerCostOptimizationProblem<Permutation>
      Parameters:
      candidate - The candidate solution to evaluate.
      Returns:
      The cost of the candidate solution. Lower cost means better solution.

    • value

      public int value(Permutation candidate)
      Description copied from interface: IntegerCostOptimizationProblem
      Computes the value of the candidate solution within the usual constraints and interpretation of the problem.
      Specified by:
      value in interface IntegerCostOptimizationProblem<Permutation>
      Parameters:
      candidate - The candidate solution to evaluate.
      Returns:
      The actual optimization value of the candidate solution.