Note

This project is under active development.

This is a new book in Operation Research that emphasizes the use of Python.

Operations Research (OR) or Management Science concerns advanced analytical methods for better decision making. It is a practical discipline that deals with quantitative thinking in managerial scenarios. It uses tools from mathematical sciences, such as modeling, statistics, and optimization, to arrive at optimal or near-optimal solutions to complex decision-making problems. Operations research is often concerned with determining the extreme values of real-world objectives, such as maximizing profit or minimizing cost.

Operations research is used to deal with real-world problems. For example:

1. Scheduling: hospital patients, classes, buses, planes, sporting events.

2. Marketing: store layout, advertising, social media, online ad placement, recommendations on a website.

3. Product development: product features, pricing, sales forecasts.

4. Inventory: how many to build, how many touchpads the store should have in stock.

• Organizations: business management, cross-cultural issues, social networks.

• Queueing: waiting for lines at amusement parks, banks, movie theaters, the line at the store to buy new electronic gadgets, traffic.

• Environment: managing sustainable resources, reducing materials needed to manufacture a product.

• Optimizing: internet search engines, product design.

• Decision making: security, investment, what college to attend.

Contents

• Introduction
  • Overview
  • Creating recipes
• Linear Programming
  • Part A
  • Part B
• The Simplex Method
  • Part A
  • Part B
• Integer Programming
  • Part hjdshfdjhf
  • Part jk asdjkacsdja
• Network Flow Models
  • Part A
  • Part B
• Project Management
  • Part A
  • Part B
• Multicriteria Decision Making
  • Part A
  • Part B
• Nonlinear Programming
  • Part A
  • Part B
• Probability and Statistics
  • Part A
  • Part B
• Decision Analysis
  • Part A
  • Part B
• Queuing Analysis
  • Part A
  • Part B
• Simulation
  • Part A
  • Part B
• Forecasting
  • Part A
  • Part B
• Inventory Management
• Python Review
  • Introduction
  • Basics of Python
  • Basic data types
    • Numbers
    • Booleans
    • Strings
    • Lists
    • Slicing
    • Loops
    • List comprehensions:
    • Dictionaries
    • Sets
    • Tuples
    • Functions
    • Classes
  • Numpy
    • Arrays
    • Array indexing
    • Datatypes
    • Array math
    • Broadcasting
  • Pandas
    • Series objects
    • Index labels
    • Initializing from a dict
    • Automatic alignment
    • DataFrame objects
    • Multi-indexing
    • Dropping a level
    • Transposing
    • Stacking and unstacking levels
    • Accessing rows
    • Adding and removing columns
    • Assigning new columns
    • Evaluating an expression
    • use inplace=True to modify the original DataFrame
    • Querying a DataFrame
    • Sorting a DataFrame
    • Operations on DataFrame
    • Handling missing data
    • Aggregating with groupby
    • Pivot tables
    • functions
    • saving and loading
    • save and load
    • combining DataFrames
      • Concatenation
      • Categories
• Linear Algebra Review
  • Part A
  • Part B