public class WeightedShortestProcessingPlusSetupTimePrecompute 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
WeightedShortestProcessingTimeclass, 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_His 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.
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
WeightedShortestProcessingPlusSetupTimeclass, which requires only O(1) space.
Fields Modifier and Type Field Description
MIN_HThe minimum heuristic value.
All Methods Instance Methods Concrete Methods Modifier and Type Method Description
completeLength()Gets the required length of complete solutions to the problem instance for which this constructive heuristic is configured.
createPartial(int n)Creates an empty Partial solution, which will be incrementally transformed into a complete solution of a specified length.
getProblem()Gets a reference to the instance of the optimization problem that is the subject of this heuristic.
h(Partial<Permutation> p, int element, IncrementalEvaluation<Permutation> incEval)Heuristically evaluates the possible addition of an element to the end of a Partial.
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public static final double MIN_HThe 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
public WeightedShortestProcessingPlusSetupTimePrecompute(SingleMachineSchedulingProblem problem)Constructs an WeightedShortestProcessingPlusSetupTimePrecompute heuristic.
problem- The instance of a scheduling problem that is the target of the heuristic.
public double h(Partial<Permutation> p, int element, IncrementalEvaluation<Permutation> incEval)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
- 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.
public final Problem<Permutation> getProblem()Gets a reference to the instance of the optimization problem that is the subject of this heuristic.
public final Partial<Permutation> createPartial(int n)Creates an empty Partial solution, which will be incrementally transformed into a complete solution of a specified length.
public final int completeLength()Gets the required length of complete solutions to the problem instance for which this constructive heuristic is configured.