In the course “Operations Research & Optimization Projects With Python,” we help you master the necessary skills, starting from linear programming, passing through discrete optimization in Python and up to stochastic processes, using real-life examples, and engaging with interactive tasks.
This program is focused on “Operations Research & Optimization Projects With Python,” and the material ranges from basic concepts of Operations Research to the ideas of machine learning, then genetic algorithms, and multi-objective decision-making. Besides, you will become familiar with Python libraries such as Gurobi, SciPy, PuLP, and Or-Tools, which help you employ optimization techniques when working on your projects. Moreover, real problems and their applications to logistics, supply chain management, and price-based systems like revenue management with dynamic programming will be discussed, and problems related to scheduling.
The knowledge of operation research and the usage of optimization algorithms will be thoroughly introduced in this course by the time you complete the course. To make the concepts concrete for you, examples are drawn from manufacturing, healthcare, and logistics industries, and you’ll observe how these techniques are applied toward boosting organizational performance.
Operations Research & Optimization Projects With Python Table of Contents:
- Introduction (03:36)
- About the Course (05:04)
- Installations (02:00)
- What is Optimization? (03:03)
- What is Operations Research? (05:19)
- Cplex, Gurobi, Xpress and More (12:00)
- What's Solver? (03:55)
- What's Mathematical Model? (04:23)
- Common Symbols and Notations (02:40)
- Production Planning Scenario (01:49)
- Building Model (02:08)
- Linear Programming (02:50)
- Outputs (01:42)
- Intro (01:39)
- Theory (01:12)
- Project (01:36)
- Mathematical Model (02:38)
- Code Time (05:53)
- Output (04:02)
- Intro (01:39)
- Theory (01:46)
- Scenario Overview (01:44)
- Mathematical Model (02:46)
- Code Time (06:35)
- Gurobi Results (03:24)
- Network Flows (03:27)
- Project (01:04)
- Minimum Cost Problem (04:14)
- Maximum Flow & Ford-Fulkerson (08:10)
- Graphical Representation With Networkx (05:39)
- Bellman-Ford (03:27)
- Edmonds-Karp (04:01)
- What is Factory Planning? (01:20)
- Project (01:13)
- Mathematical Model (03:02)
- Code Overview (02:01)
- Results (02:21)
- Logic of Scheduling (03:47)
- Scenario (01:17)
- Model (02:08)
- Gurobi Code (07:00)
- Review (01:53)
- Theoretical Approach (02:07)
- First Look at Scenario (01:09)
- Model (01:40)
- Coding (07:01)
- Review (02:48)
- Theory (02:48)
- Scenario & Model (01:37)
- Code Time (04:11)
- Result Review (02:40)
- Theory (02:13)
- Scenario (01:51)
- Mathematical Model (02:36)
- Code (05:22)
- Output Review (05:00)
- Theory (02:04)
- Logistics Company (01:34)
- Model (02:15)
- Coding (07:03)
- Output (03:44)
- What is Genetic Algorithm (02:02)
- Terms (04:44)
- Scenario (02:27)
- Mathematical Model (02:58)
- Coding with DEAP (07:32)
- DEAP's Results (03:27)
- Brain Gymnastics (00:59)
- Wine Tasting Menu (02:51)
- Project Introduction (01:12)
- Theory (02:52)
- Scenario & Model (02:28)
- Code (05:10)
- First Look at Goal Programming (02:16)
- Scenario (01:41)
- Mathematical Model (02:24)
- Code (04:36)
- Outputs (01:37)
- An Operations Research Approach to Third-Generation Coffee Shop (02:26)
- Introduction (01:51)
- Theory (02:02)
- Project (01:02)
- Mathematical Model (01:57)
- Code Time (05:51)
- Outputs (02:10)
- Theory & Project (04:11)
- Model (02:59)
- Code (06:44)
- Output (02:34)
- Intro (01:23)
- Theory (01:46)
- Scenario (01:30)
- Model (01:29)
- Code (06:28)
- Outputs (01:46)
- Theory & Scenario (03:07)
- Intro (01:34)
- Theory (02:38)
- Scenario (01:01)
- Model (01:52)
- Code (03:37)
- Results (01:00)
- Introduction (03:36)
- Theory and Scenario (03:20)
- Theory & Scenario (03:12)
- Model (02:16)
- HS Code (03:38)
- Output (02:05)
- Project Introduction (01:37)
- Theory (02:59)
- Rastrigin Function (01:45)
- Model of AFSA (01:51)
- Code of AFSA (03:37)
- Outcomes of AFSA (01:47)
- Tabu Search for TSP (11:29)
- Introduction (06:45)
- What is SPEA2? (02:50)
- Project (01:30)
- Model of SPEA2's (02:24)
- Let's Code (06:24)
- Review of Results (04:04)
- Ant Colony Optimization for TSP (14:46)
- Project Introduction (01:40)
- Theory (02:39)
- Coding (07:23)
- Outputs and Graph (03:32)
- Project (20:21)
- Operations Research & Machine Learning (09:15)
- Optimization Related Books (00:47)
- Closing (01:51)
Who is this course for?
- The individuals who consider improving their decision-making capability across the areas of operation, including production, supply chain, and services.
- Working analysts and data scientists willing to improve their algorithmic problem-solving skills and organizational efficiency.
- The target audience is students and professionals engaged in operations research who seek to enhance their Python skills with a focus on algorithmic solutions.
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