Constructor Detail

Method Detail

• pfqn_bs

   final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N)

  Bard-Schweitzer approximate mean value analysis algorithm

• pfqn_bs

  @JvmOverloads() final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z, Double tol, Integer maxiter, Matrix QN0)

  Bard-Schweitzer approximate mean value analysis algorithm

• pfqn_bs

  @JvmOverloads() final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z, Double tol, Integer maxiter)

  Bard-Schweitzer approximate mean value analysis algorithm

• pfqn_bs

  @JvmOverloads() final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z, Double tol)

  Bard-Schweitzer approximate mean value analysis algorithm

• pfqn_bs

  @JvmOverloads() final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z)

  Bard-Schweitzer approximate mean value analysis algorithm

• pfqn_bs

   final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z, Double tol, Integer maxiter, Matrix QN0, Matrix weight)

  Bard-Schweitzer approximate mean value analysis algorithm with weighted priorities

  Parameters:

  L -

  • the service demand matrix

  N -

  • the population vector

  Z -

  • the think times vector

  tol -

  • max tolerance admitted between successive iterations

  maxiter -

  • maximum number of iterations

  QN0 -

  • original queue lengths

  weight -

  • weight matrix for priorities (MxR)

  Returns:

  • the performance metrics for this network.

• pfqn_bs

   final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z, Double tol, Integer maxiter, Matrix QN0, Array<SchedStrategy> type)

  Bard-Schweitzer approximate mean value analysis algorithm

  Parameters:

  L -

  • the service demand matrix

  N -

  • the population vector

  Z -

  • the think times vector

  tol -

  • max tolerance admitted between successive iterations

  maxiter -

  • maximum number of iterations

  QN0 -

  • original queue lengths

  type -

  • scheduling disciplines at each station

  Returns:

  • the performance metrics for this network.

• pfqn_bs

   final static Ret.pfqnAMVA pfqn_bs(Matrix L, Matrix N, Matrix Z, Double tol, Integer maxiter, Matrix QN0, Array<SchedStrategy> type, Matrix weight)

  Bard-Schweitzer approximate mean value analysis algorithm with optional weighted priorities

  Parameters:

  L -

  • the service demand matrix

  N -

  • the population vector

  Z -

  • the think times vector

  tol -

  • max tolerance admitted between successive iterations

  maxiter -

  • maximum number of iterations

  QN0 -

  • original queue lengths

  type -

  • scheduling disciplines at each station

  weight -

  • optional weight matrix for priorities (MxR)

  Returns:

  • the performance metrics for this network.