Mastering Discrete Event Simulation: Concepts & Project Workflow

Discrete Event Simulation: Core Concepts & Project Workflow

Key Components of a Simulation Model

• Locations

  Fixed places where entities are processed, stored, decisions are made, or other activities occur.

• Entities

  Whatever the model processes or tracks within the system.

• Physical Road Network

  Networks where entities and resources move and interact.

• Resources

  Any person, equipment (e.g., vehicles), or tools used to transport entities, develop operations, or perform tasks.

• Process

  Defines the path of entities using the system's logic and operational experience at each location.

• Arrivals

  New entities entering the system at specific times or rates.

• Shifts

  Defined periods for breaks or specific assignments for locations or resources, affecting availability.

• Attributes

  Characteristics or properties linked to an entity or specific location, influencing its behavior.

• Variables

  Placeholders for values used to represent numeric data within the model.

• Arrays

  A matrix of cells containing real or integer numbers, where each cell functions as a variable.

• Subroutine

  A command or block of logic that can be called to perform a specific task, and optionally return a value.

• Arrival Cycles

  An individual arrival pattern that occurs over a defined period of time.

• Table Function

  A relationship between independent and dependent values, often used for lookup tables.

• User Distributions

  An empirical data table used to represent a dataset that does not conform to any standard statistical distribution.

• External Files

  Used during model execution to read input data or write simulation output data.

• General Information

  The section or window that allows you to specify basic model information and parameters.

• Cost Tracking

  Functionality to monitor and analyze the costs associated with each element or activity within the model.

• Background Graphics

  Adds visual elements to improve and/or make the model animation more realistic and engaging.

Understanding Discrete Event Simulation (DES)

• What is Discrete Event Simulation?

  A modeling approach using mathematical logic and probabilistic relationships to represent the behavior of a system as a sequence of individual events over time.

• Advantages of DES

  • Helps ascertain the impact of process changes and test "what-if" scenarios without real-world disruption.
  • Improves understanding of current processes and system dynamics.
  • Provides a valuable tool for training and decision-making, allowing users to experiment with strategies.
  • Offers a cost-effective way to test scenarios compared to physical experimentation.
  • Simplifies the visualization of process improvements and bottlenecks.

• Disadvantages of DES

  • It is not an optimization tool; it evaluates scenarios rather than finding optimal solutions directly.
  • Can be expensive in terms of software, hardware, and expert personnel.
  • Requires considerable time for model development, data collection, and analysis.
  • Demands expertise in interpreting statistical data and simulation results accurately.

• Defining Simulation Scope

  Encompasses all objects, entities, and interactions that are relevant to achieving the study objectives.

• Determining Level of Detail

  Primarily determined by the study objectives; the model should be sufficiently detailed to accurately replicate system behavior without unnecessary complexity.

• Key Simulation Objectives

  • Visualization: To observe and understand what is happening within the system.
  • Load Analysis: To quantify what is happening in the system, such as throughput, utilization, and queue lengths.
  • Communication: To effectively show what is happening in the system to stakeholders and decision-makers.

• Phases of a Simulation Project

  1. System Definition: Clearly defining the problem, boundaries, and objectives.
  2. Conceptual Model Development: Creating a high-level representation of the system and its logic.
  3. Data Collection and Analysis: Gathering and preparing the necessary input data for the model.
  4. Preliminary Model Development: Building an initial version of the simulation model.
  5. Model Verification and Validation: Ensuring the model works correctly and accurately represents the real system.
  6. Final Model Simulation: Running experiments and collecting output data from the validated model.
  7. Documentation: Recording all aspects of the project, including assumptions, data, and results.