Waiting Line Models

Waiting Line Models

A Full Guide to Waiting Line Models and Queuing Theory. In this article, I will give a detailed overview of waiting line models. I will discuss when and how to use waiting line models from a business standpoint.

In this article, I will give a detailed overview of waiting line models. I will discuss when and how to use waiting line models from a business standpoint. In the second part, I will go in-depth into multiple specific queuing theory models, that can be used for specific waiting lines, as well as other applications of queueing theory.

Introduction to waiting line models

Waiting line models are mathematical models used to study waiting lines. Another name for the domain is queuing theory.

Waiting lines can be set up in many ways. In a theme park ride, you generally have one line. In the supermarket, you have multiple cashiers with each their own waiting line. And at a fast-food restaurant, you may encounter situations with multiple servers and a single waiting line.

The goal of waiting line models is to describe expected result KPIs of a waiting line system, without having to implement them for empirical observation. Result KPIs for waiting lines can be for instance reduction of staffing costs or improvement of guest satisfaction.

When to use waiting line models?

Waiting line models can be used as long as your situation meets the idea of a waiting line. This means that there has to be a specific process for arriving clients (or whatever object you are modeling), and a specific process for the servers (usually with the departure of clients out of the system after having been served).

Waiting line models need arrival, waiting and service

This idea may seem very specific to waiting lines, but there are actually many possible applications of waiting line models. For example, waiting line models are very important for:

  • Computer processors and task handling. Many tasks arrive at the same time to your computer’s processor, and it has to handle them one by one without the computer failing.
  • Telecommunication models. For example when many messages are being sent in a short time frame and have to be dealt with correctly while limiting the “waiting time” / “delay” of the message.
  • Traffic engineering. For example when many cars arrive at the same location at the same time and they have to wait.
  • Complicated multi-layer systems like call centers with multiple services are an example of multiple waiting lines connected together.

operations-research industrial-engineering statistics data-science mathematics

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Managing Data as a Data Engineer:  Understanding Data Changes

Understand how data changes in a fast growing company makes working with data challenging. In the last article, we looked at how users view data and the challenges they face while using data.

Statistics for Data Science

Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.

Top 20 Latest Research Problems in Big Data and Data Science

Even though Big data is into main stream of operations as of 2020, there are still potential issues or challenges the researchers.

The List of Top 10 Data Science Lists; Data Science MOOCs with Substance

The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science; Content-Based Recommendation System using Word Embeddings; How Natural Language Processing Is Changing Data Analytics. Also this week: The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science