discrete-simAnalyze complex systems through discrete event simulation.

Installation ​

npm install discrete-sim

yarn add discrete-sim

pnpm add discrete-sim

bun add discrete-sim

Requirements: Node.js 16+, TypeScript 5.0+ (recommended)

Quick Example ​

import { Simulation, Resource, timeout } from 'discrete-sim';

// Define a simple customer process
function* customer(id: number, server: Resource) {
  console.log(`Customer ${id} arrives at ${sim.now}`);

  // Request the server
  yield server.request();
  console.log(`Customer ${id} starts service at ${sim.now}`);

  // Service time
  yield* timeout(5);

  // Release the server
  server.release();
  console.log(`Customer ${id} leaves at ${sim.now}`);
}

// Create simulation
const sim = new Simulation();
const server = new Resource(sim, 1, { name: 'Server' });

// Start 3 customer processes
for (let i = 0; i < 3; i++) {
  sim.process(() => customer(i, server));
}

// Run simulation
sim.run();

Features ​

• Process-Based Modeling - Use generator functions to describe processes naturally with intuitive yield syntax
• Resource Management - Built-in support for shared resources (Resources, Buffers, Stores) with FIFO queuing and automatic statistics tracking
• Buffer Resources - Model homogeneous quantities like fuel, money, or raw materials with put/get operations
• Store Resources - Manage collections of distinct objects with FIFO or filter-based retrieval
• Advanced Statistics - Time-weighted averages, percentiles (P50, P95, P99), histograms, standard deviation, and comprehensive metrics
• Warm-up Periods - Exclude initial transient behavior from statistical analysis for accurate steady-state results
• Event Tracing - Detailed event execution logging for debugging and analysis
• Rich Random Distributions - Exponential, normal, uniform, triangular, Poisson, and more with seedable RNG
• Reproducible Results - Seedable random number generator for consistent experiments and validation
• TypeScript Native - Full type safety and excellent IDE support for robust simulation development
• Zero Dependencies - Lightweight and fast with no external dependencies
• Performance Optimized - O(log n) priority queuing, cached statistics, and efficient algorithms

Why discrete-sim? ​

Discrete event simulation is a powerful technique for modeling complex systems where state changes occur at discrete points in time. Whether you're modeling:

• Queueing Systems - Banks, call centers, service desks
• Manufacturing - Production lines, supply chains
• Healthcare - Hospital operations, patient flow
• Logistics - Warehouses, transportation networks
• Computer Networks - Data packet routing, server farms

discrete-sim provides the tools you need to build accurate, efficient simulations.