Operational Research (OR), alternatively referred to as operational Research, represents a versatile and interdisciplinary domain within applied mathematics and decision science. It is dedicated to the enhancement of complex systems and processes with the primary objective of facilitating improved decision-making. For individuals aspiring to pursue a career in OR, enrolling in relevant courses is paramount. In this article, we will explore some of the best Operational Research courses available in the market.
Importance of operational Research
Operational Research (OR) holds profound significance in the contemporary business landscape. It serves as a critical tool for organizations like Coca-Cola and UPS, enabling them to optimize their complex supply chain logistics, distribution networks, and resource allocation processes. By leveraging OR methodologies, these industry giants are able to enhance operational efficiency, reduce costs, and improve decision-making.
The versatility of OR skills makes professionals in this field highly sought after, and the ability to apply quantitative analysis and modeling techniques to solve real-world problems is invaluable. Career growth and opportunities are further enhanced by the increasing importance of data-driven decision-making across sectors, making OR an exciting and rewarding career choice.
Table of Content
- 1 Best Operational Research Courses, Certification, Tutorials, Training, Classes Online
- 1.1 Operations Research (1): Models and Applications [Coursera]
- 1.2 Operations Research (2): Optimization Algorithms [Coursera]
- 1.3 Operations Research (3): Theory [Coursera]
- 1.4 HKPolyUx: operational Research: an Active Learning Approach [edX]
- 1.5 Master Transportation Problem Algorithm [Udemy]
- 1.6 Master Critical Path Method (CPM) & PERT [Udemy]
- 2 FAQ
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Best Operational Research Courses, Certification, Tutorials, Training, Classes Online
Operations Research (1): Models and Applications [Coursera]
Discover the core principles of operational Research, uncovering the practical application of mathematical models to resolve optimization issues across diverse industries. This beginner-level operational research course online immerses learners in the intricate world of optimization problems prevalent across various industries such as Business, Economics, Engineering, and Computer Science.
Course Instructor
The operational research class is instructed by Ling-Chieh Kung from National Taiwan University, a seasoned educator with expertise in operational Research and a track record of providing comprehensive instruction on mathematical modeling and optimization.
What you’ll learn
This course includes 6 modules which are divided into:
- Course Overview: The introductory module offers an insight into operational Research (OR) and its historical context, outlining its relevance across diverse fields.
- Linear Programming: This module delves into Linear Programming (LP), a crucial method for optimizing various business decisions such as production, inventory management, and personnel scheduling.
- Integer Programming: This module delves into Integer Programming (IP). It demonstrates how IP addresses optimization problems with integrality constraints, essential for resolving issues in facility location, machine scheduling, and vehicle routing across practical areas.
- Nonlinear Programming: Exploring nonlinearities prevalent in real-life scenarios like pricing, inventory, and portfolio optimization, this module introduces NLP.
- Case Study: Personnel Scheduling: Through nine videos and a quiz, this practical module presents a real-world business case solved using operational Research.
- Course Summary and Future Directions: The concluding module provides a comprehensive summary of the course content.
Pros & Cons
Pros
- Real-Life Problem Solving
- Accessible Software Application
- Future Perspectives
Cons
- Scope and Depth
Key Highlights & Learning Objectives
- The course stands out for its strong emphasis on practical application. It focuses on translating real business challenges into mathematical models that can be solved using computers.
- Unlike many introductory courses, this program covers various essential concepts in operational Research comprehensively.
- A distinctive feature is the integration of Microsoft Excel as a tool for solving optimization problems. This practical approach makes the course more accessible to learners.
- This course comprises 57 videos, accompanied by 1 reading resource, 6 quizzes and a certificate of achievement.
Who is it for?
This course is ideally suited for individuals seeking an entry point into the world of operational Research, especially those in Business, Economics, Engineering, or Computer Science fields. It caters to people aiming to enhance decision-making skills or those involved in logistics, supply chain management, or operational roles who will find value in learning how to formulate and solve business challenges through mathematical models.
Rating: 4.8/5
Students Enrolled: 58,429
Duration: 11 hours, 3 weeks at 3 hrs/week
Operations Research (2): Optimization Algorithms [Coursera]
This online operational research course series presents an immersive exploration into the realm of optimization algorithms, constituting the second part of this comprehensive educational sequence. Tailored for individuals seeking a deeper understanding of deterministic optimization techniques, this intermediate-level course delves into the practical application of algorithms designed to solve diverse optimization problems.
Course Instructor
This operational research class is instructed by Ling-Chieh Kung, a seasoned expert affiliated with National Taiwan University, renowned for their expertise in operational Research, contributing to eight courses and educating over 81,880 learners, emphasizing optimization algorithms and their practical implementation.
What you’ll learn
This course includes the following 6 modules:
- Course Overview: The course commences with a brief review of linear algebra essentials, encompassing Gaussian and Gauss-Jordan elimination, establishing foundational knowledge through seven videos, a reading assignment, and a quiz.
- The Simplex Method: Exploring the groundbreaking simplex method developed by Dr. George Dantzig, this module focuses on standard form, basic solutions of linear programs, and the efficient application of the simplex method, featuring 25 videos and a quiz.
- Branch and Bound Algorithm: This module introduces the Branch-and-Bound algorithm for solving integer programming, covering linear relaxation concepts in 16 videos and a quiz.
- Gradient Descent and Newton’s Method: Shifting to nonlinear programs, this segment reviews gradients and Hessians before presenting gradient descent and Newton’s method solutions, with 13 videos and a quiz comparing these methodologies.
- Heuristic Algorithm Design: Using a real-world case study involving NEC Taiwan, this module tackles facility location problems, optimizing hub locations and employee allocation across 12 videos and a quiz.
- Course Summary and Future Directions: Concluding the course, this module summarizes key learnings and provides insight into future advanced courses, featuring three videos and a quiz for reinforcement and guidance.
Pros & Cons
Pros
- Real-life Case Studies
- Thorough Algorithmic Exploration
- Python Integration with Gurobi Solver
Cons
- Dependency on Software Tools
- Scope and Depth
Key Highlights & Learning Objectives
- This course uniquely emphasizes deterministic optimization techniques, diving into the simplex method, Branch-and-Bound algorithm, and heuristic algorithm design.
- Setting itself apart, the course extensively incorporates the use of the Gurobi solver with Python, offering practical experience in solving linear, integer, and nonlinear programs.
- Incorporation of real-world examples, particularly the NEC Taiwan case study, enriches the learning experience by illustrating how optimization algorithms are employed.
- This course comprises a total of 74 videos, 4 readings or articles, 6 quizzes for assessment, and offers a certificate of completion.
Who is it for?
This operational research course with certificate caters to intermediate-level learners interested in operational Research, algorithmic problem-solving, and optimization techniques. It’s ideal for individuals with a foundational understanding of linear algebra and a desire to delve deeper into deterministic optimization methods.
Rating: 4.8/5
Students Enrolled: 42,349
Duration: 12 hours (3 weeks at 4 hrs/week)
Operations Research (3): Theory [Coursera]
The operational Research advanced course is a comprehensive exploration into the theoretical underpinnings of optimization problems. As the third installment in a series, this operational research training meticulously examines the mathematical properties associated with linear programs, integer programs, and nonlinear programs.
What you’ll learn
This course comprises of 8 modules:
- Course overview: The inaugural module lays the groundwork by elucidating the course’s importance and focusing on comprehending mathematical properties.
- Duality: This module centers on exploring the theory and practical applications of linear programming duality.
- Sensitivity Analysis and Dual Simplex Method: Building upon prior discussions on the simplex method and duality, this module introduces the dual simplex method.
- Network Flow: Focusing on decision-making in transportation, logistics, and project management, this module introduces network flow models.
- Convex Analysis: In this segment, learners are exposed to real-world applications through the case of NEC Taiwan, exploring the rearrangement of service hub locations and employee allocation to minimize costs.
- Lagrangian Duality and KKT Condition: This module centers on constrained nonlinear programs, introducing Lagrangian relaxation and the KKT condition as pivotal tools for resolution.
- Case Studies: This module unveils two prominent models derived from mathematical properties.
- Course Summary and Future Learning Directions :In the concluding module, it revisits key topics, offering conclusive insights and directions for further advanced studies.
Pros & Cons
Pros
- Matrix-Centric Approach
- Application-focused Case Studies
Cons
- Intensive Content
- Complexity
Key Highlights & Learning Objectives
- Emphasis on employing matrices to execute the simplex method, aiding in understanding subsequent lectures and problem-solving techniques.
- Comprehensive exploration of primal-dual pairs, weak and strong duality, and their practical applications, including shadow pricing for constraint analysis.
- Application of the dual simplex method in sensitivity analysis, particularly in evaluating new constraints and variables within linear programming models.
- Introduction to minimum cost network flow (MCNF) models, demonstrating their broad applicability in various decision-making scenarios and their connection to linear and integer programming.
- This course includes a total of 77 videos, 5 readings/articles, 8 quizzes for assessments, and provides a certificate of completion.
Who is it for?
This online operational Research course is ideally suited for individuals with a solid foundation in mathematics and business analytics seeking an in-depth understanding of optimization problems. Those familiar with deterministic optimization techniques and seeking to enhance their expertise in linear and nonlinear programming, duality, sensitivity analysis, and network flow models will find this course particularly beneficial for advancing their problem-solving skills within these domains.
Rating: 5/5
Students Enrolled: 37,291
Duration: 14 hours (3 weeks at 4 hr/week)
HKPolyUx: operational Research: an Active Learning Approach [edX]
The edX operational research course, is a comprehensive course designed to equip learners with the essential methodologies and practical techniques of operational Research (OR). This instructor-paced program focuses on addressing operational challenges across diverse sectors.
Course Instructor
This operational research training is led by a team of accomplished instructors from The Hong Kong Polytechnic University: Heung-wing Joseph LEE, an Associate Professor, Man-kin Adam LEUNG and Sze-leong Frankie TSOI, both seasoned Instructors, and Wai-him Solomon WONG, a proficient Project Associate, collectively providing expertise and guidance in operational Research methodologies and practical applications for operational problem-solving.
Pros & Cons
Pros
- Holistic Approach
- Quantitative Decision-making
- Problem-solving Orientation
- Focus Areas
Cons
- Mathematical Prerequisites
- Focus on Specific Techniques
Key Highlights & Learning Objectives
- Emphasizes the use of quantitative approaches in decision-making for operational challenges.
- Covers essential topics such as Linear Programming (LP) using the Simplex Method, Critical Path Method (CPM) for project management, and simulation techniques for stochastic OR problems.
- Employs practical examples, exercises, and active learning strategies to reinforce understanding and application of OR methodologies for solving real operational challenges.
- Provides a structured approach to understanding and applying OR methodologies, facilitating a step-by-step learning process for complex analytical tools.
- This course comprises approximately 30 videos, supplementary articles, resources and certificate of completion.
Who is it for?
This operational research class caters well to professionals in engineering, manufacturing, finance, and service sectors looking to enhance their decision-making skills for operational challenges. Ideal participants possess a solid understanding of high school-level mathematics, probability distributions, and basic calculus. Additionally, individuals aim to apply quantitative approaches to solve real-world operational problems, particularly in areas like project management, optimization, and decision analysis.
Rating: 4.75/5
Duration: 6 weeks, 4–6 hours per week
Master Transportation Problem Algorithm [Udemy]
Discover the essence of efficient logistics and optimization with this operational research course online. Delve into the intricacies of distributing goods across multiple origins and destinations while minimizing costs and adhering to various constraints. This course unveils the foundational algorithms, strategies, and real-world applications essential for tackling complex logistics scenarios.
Course Instructor
Madhusudan Sohani, an experienced instructor in operational Research, leads this OR class on the Transportation Problem, utilizing simplified teaching methods to elucidate complex algorithms, enabling learners to comprehend and apply strategies effectively in logistics optimization.
Pros & Cons
Pros
- Interactive Problem-solving
- Algorithmic Depth
Cons
- Technical Complexity
Key Highlights & Learning Objectives
- It could offer a deeper dive into the nuances of different solution methods, such as the North West Corner Rule, Least Cost Method, Vogel’s Approximation Method, and the Modified Distribution Method.
- It might feature interactive problem-solving sessions or exercises, allowing students to apply learned concepts in solving diverse transportation problems, thereby reinforcing comprehension.
- Different from traditional approaches, it employs innovative teaching methods, visual aids, or simplified explanations that cater to learners from diverse educational backgrounds.
- This course comprises 5 lectures totaling a duration of 10 hours and 35 minutes in video content, 3 downloadable resources, and grants a certificate of completion upon finishing the course.
Who is it for?
This course on the Transportation Problem in operational Research caters to individuals with varying backgrounds, including those studying operational Research, engineering, management, finance, and related fields. Whether a student delving into the subject or a professional aiming to enhance logistics proficiency, this online operational Research course provides valuable insights and problem-solving techniques applicable across diverse industries.
Rating: 4.9/5
Students Enrolled: 32,573
Duration: 10.5 Hours
Master Critical Path Method (CPM) & PERT [Udemy]
The course on Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT), serves as a fundamental resource in operational Research training. This course meticulously explores the intricacies of project monitoring and implementation.
Course Instructor
Prof. Madhusudan Sohani, an experienced educator in operational Research, offers this online operational Research course, leveraging his industry background in financial project appraisal and extensive teaching experience to simplify CPM and PERT methodologies.
Pros & Cons
Pros
- Project Compression Strategies
- Optimizing Non-Critical Activities
Cons
- Pacing and Complexity
- Varied Learning Preferences
Key Highlights & Learning Objectives
- The course delves deeply into Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT), providing a thorough understanding of project management tools.
- Tailored for students pursuing degrees in management, engineering, and diploma courses, the course caters to a wide range of learners.
- Exploring the concept of ‘float’ empowers students to leverage non-critical activities to accelerate critical tasks, ensuring smoother project timelines.
- This course comprises 5 lectures spanning 11.5 hours of video content, accompanied by 7 downloadable resources and provides a Certificate of Completion.
Who is it for?
This course is ideally suited for individuals seeking a comprehensive understanding of project management techniques like the Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT). The course’s practical examples make it an excellent choice for those looking to grasp these essential tools in project management within the realm of operational Research.
Rating: 4.7/5
Students Enrolled: 24,385
Duration:12 hours
FAQ
What is an example of an operation research study?
Operations research (OR) is a field that uses mathematical and analytical methods to make better decisions and solve complex problems. There are numerous examples of operations research studies across various industries.
Here’s one example:
Supply Chain Optimization: Consider a company that manufactures and distributes products. An operations research study in this context might involve optimizing the supply chain. This includes determining the most cost-effective way to source raw materials, allocate resources in production, manage inventory, and distribute finished products. The goal could be to minimize costs, maximize efficiency, or improve customer satisfaction.
The study might involve mathematical modeling, simulation, and optimization techniques to find the best routes for transportation, determine optimal inventory levels, and schedule production to meet demand while minimizing costs. This type of analysis can help companies make informed decisions that enhance their overall supply chain performance.
Other examples of operations research studies include:
Inventory Management: Determining the optimal levels of inventory to minimize carrying costs while ensuring product availability.
Finance and Investment: Portfolio optimization to maximize returns while managing risk in investment portfolios.
Healthcare Resource Allocation: Optimizing the allocation of resources, such as hospital staff, equipment, and beds, to improve patient outcomes and minimize costs.
Project Scheduling: Determining the most efficient schedule for completing a project by allocating resources and minimizing completion time.
Telecommunications Network Design: Designing efficient and cost-effective telecommunication networks to ensure reliable communication with minimum infrastructure costs.
Facility Location and Layout: Selecting optimal locations for facilities and designing layouts to minimize transportation costs and improve operational efficiency.
These examples illustrate how operations research can be applied in various domains to solve complex problems and make data-driven decisions.
Is Operation research a good career?
Yes, Operations Research (OR) can be a rewarding and fulfilling career for individuals who enjoy solving complex problems using mathematical and analytical techniques.
Here are some reasons why a career in operations research can be considered good:
Problem Solving: If you enjoy tackling real-world problems and finding innovative solutions, a career in operations research allows you to apply mathematical and analytical methods to address complex business and organizational challenges.
Impactful Decision-Making: Operations researchers often contribute to making significant decisions that can have a substantial impact on an organization’s efficiency, cost-effectiveness, and overall performance.
Diverse Applications: Operations research is applicable across a wide range of industries, including finance, healthcare, logistics, manufacturing, telecommunications, and more. This diversity provides opportunities to work on a variety of interesting and challenging projects.
Continuous Learning: The field of operations research is dynamic, with ongoing advancements in modeling techniques, algorithms, and technologies. This provides professionals with opportunities for continuous learning and skill development.
Collaboration: Operations researchers often work in interdisciplinary teams, collaborating with experts from various fields such as business, engineering, and computer science. This collaboration enhances the learning experience and allows for a broader perspective on problem-solving.
High Demand: As organizations increasingly recognize the value of data-driven decision-making, there is a growing demand for professionals with skills in operations research. This can lead to a stable and potentially lucrative career path.
Versatility: Operations research skills are versatile and can be applied in various roles, including data analysis, optimization, simulation, and decision support. This versatility allows professionals to explore different aspects of the field.
However, it’s essential to consider your personal interests, skills, and career goals when evaluating whether operations research is a good fit for you. If you enjoy quantitative analysis, problem-solving, and working with data to improve decision-making processes, a career in operations research could be a great choice.
What are the 5 uses of operation research?
Operations Research (OR) is a versatile field with numerous applications. Here are five common uses of operations research:
Optimization of Processes:
Example: In manufacturing, operations research can be used to optimize production schedules, minimize resource utilization costs, and maximize overall efficiency. This involves mathematical modeling and algorithms to find the best combination of inputs to achieve desired outputs.
Supply Chain Management:
Example: OR is extensively used in supply chain optimization to streamline the movement of goods and materials from the supplier to the end customer. This includes inventory management, transportation optimization, and demand forecasting to ensure products are available when and where they are needed while minimizing costs.
Finance and Investment:
Example: Portfolio optimization is a common application in finance. Operations research techniques help investors and financial analysts determine the optimal mix of investments to maximize returns while managing risk. This involves mathematical modeling and algorithms to allocate assets based on various criteria.
Healthcare Resource Allocation:
Example: In healthcare, operations research is used to optimize the allocation of resources such as hospital beds, staff, and medical equipment. This helps healthcare providers deliver efficient and effective services, minimizing costs while maintaining quality care.
Transportation and Logistics:
Example: OR is applied in route optimization for transportation networks, such as planning the most efficient delivery routes for a fleet of vehicles. This involves considering factors like distance, traffic conditions, and delivery time windows to minimize transportation costs and improve delivery efficiency.
These are just a few examples, and operations research has applications in various other fields, including marketing, telecommunications, energy, and public policy. The common thread is the use of quantitative and analytical methods to inform decision-making and improve processes. The goal is to find optimal solutions to complex problems, considering multiple variables and constraints.
