Package jline.examples.java.advanced

Class AgentModel

java.lang.Object
jline.examples.java.advanced.AgentModel
public class AgentModel extends Object
Model factory for AG, the agent-based solver, examples. These models demonstrate the capabilities of the agent-based solver for analyzing queueing networks through agent decomposition.

  • Constructor Details

    • AgentModel

      public AgentModel()

  • Method Details

    • tandemOpen

      public static Network tandemOpen()
      Creates an open tandem queue model (M/M/1 -> M/M/1). Network structure: Source -> Queue1 -> Queue2 -> Sink Parameters: - Arrival rate: 0.5 - Service rate at Queue1: 1.0 - Service rate at Queue2: 1.5
      Returns:
      the network model

    • tandemPhaseType

      public static Network tandemPhaseType()
      Creates an open tandem queue whose service laws are PHASE-TYPE. Network structure: Source -> Queue1 -> Queue2 -> Sink Both stations carry the SAME mean service time and differ only in variability (Queue1 an Erlang-2 of SCV 0.5, Queue2 a HyperExp of SCV 4), which is exactly what a mean-rate reading of the service process cannot see. The agent-based methods represent them exactly: each agent is a QBD over (queue length, phase). Parameters: - Arrival rate: 0.5 - Mean service time at both queues: 1.0
      Returns:
      the network model

    • closedNetwork

      public static Network closedNetwork()
      Creates a closed network with two PS queues. Network structure: Queue1 <-> Queue2 (closed loop) Parameters: - Number of jobs: 10 - Service rate at Queue1: 2.0 - Service rate at Queue2: 1.0
      Returns:
      the network model

    • multiclassClosed

      public static Network multiclassClosed()
      Creates a multiclass closed network. Network structure: Queue1 <-> Queue2 (closed loop, 2 classes) Parameters: - Class 1: 5 jobs - Class 2: 3 jobs - Different service rates per class at each queue
      Returns:
      the network model

    • jacksonNetwork

      public static Network jacksonNetwork()
      Creates a Jackson network with probabilistic routing. Network structure: Source -> Queue1/2/3 (with feedback) -> Sink Parameters: - Arrival rate: 1.0 - Service rates: [2.0, 3.0, 2.5] - Routing with feedback between queues
      Returns:
      the network model

    • gNetwork

      public static Network gNetwork()
      Creates a G-network (Gelenbe network) with negative customers. G-networks extend standard queueing networks with "negative customers" (signals) that remove jobs from queues upon arrival. This models scenarios like job cancellations or service interrupts. Network structure: - Source generates positive customers (jobs) and negative signals - Positive customers: Source -> Queue1 -> Queue2 -> Sink - Negative signals: Source -> Queue1 -> Queue2 (removes job) -> Sink Parameters: - Positive arrival rate: 1.0 - Negative signal rate: 0.3 - Service rates: [2.0, 3.0] Reference: Gelenbe, E. (1991). "Product-form queueing networks with negative and positive customers", Journal of Applied Probability
      Returns:
      the network model with signal class configured