Operational research is concerned with various approaches to tackling specific problems. Many examples of combining Machine Learning (ML) and Operations Research (OR) can enhance accuracy and produce benefits.
A predictive model’s solution can be more dependable if optimized to solve the problem properly. Many Machine Learning (ML) strategies are employed to construct predictive models, while to identify optimal solutions, various operations research approaches are used. We get accurate and optimal solutions when both of these approaches are employed. This post will look at how combining Machine Learning (ML) and Operations Research (OR) can help tackle problems that require exact and optimal solutions. We’ll also go through the most common uses for this combination. The primary themes that will be explored in this article are as follows.
How does Operations Research function, and what is it?
Operation Research is an analytical approach or method that can help solve problems and make decisions. The fundamental technique for overcoming issues utilizing operation research is to break down a problem into small components and then solve those broken parts in defined steps using mathematical analysis. This method of problem-solving and decision-making can help with organizational management and benefits.
The stages below can be employed to complete the entire operation research technique:-
- The issue has been discovered
- Developing a model that understands and resembles the actual world
- The real world, as well as aspects in the problem’s setting
- Using the model, come up with solutions to the problem
- All of the model’s solutions should be optimized
- Put the most effective solution to the situation into action
Characteristics of Operational Research
The following are the characteristics of a basic operations research technique:-
Optimization – Operational Research’s primary goal is to find the optimal solution for conditions. By optimizing a solution, which comprises comparing and testing the solution through Operation Research, we can find the best option.
Simulation – Built models are used in simulation to try and test solutions before they are implemented in a procedure.
Probability and statistics – Mathematical algorithms and data help us find information and identify hazards, allowing us to make precise predictions and test viable remedies.
The functionality of Operations Research
Operations Research can be helpful in a variety of problem and decision-making domains. The following are a few of them:
- Task scheduling and time management are both crucial
- Steps in urbanization and agriculture planning
- Management of the supply chain
- Enterprise resource management (ERM)
- Management of inventory and warehouses
- Management of risks
- Network marketing is a type of marketing that entails
Machine Learning (ML) in Operations Research (OR)
We studied how to find the greatest and most ideal answer to a problem and how to make decisions using simple strategies in the previous part. When talking about Machine Learning, we can state that the algorithms learn from the data’s prior records and knowledge. Their main goal is to anticipate a suitable value that will satisfy the user and accurately accomplish the task.
Machine Learning (ML) models are employed in decision-making, and both OR and ML are focused on finding a better solution to a problem. Skilled Operation Research becomes more difficult as the list of options grows wider, and personally testing the solutions becomes monotonous and time-demanding. The experienced must also consider the hazard before applying the solution to the problem of making any decision with this testing work.
How ML and OR can be hybridized
The hybridization of ML and OR can be done in four different ways:-
Machine Learning (ML) then Operations Research (OR) – Here, the ML can assist in discovering points or solutions, and we can then use the OR to maximize the points of solutions.
Machine Learning (ML) in Operations Research OR: In this case, we may state that ML assists us in completing tasks within the OR umbrella. This is an example of an operation research procedure.
Operations Research (OR) in Machine Learning (ML) – In this case, we may state that operation research aids in the execution of machine learning tasks and thus can be called a Machine Learning technique.
New Hybridization of Machine Learning (ML) and Operations Research (OR) – This can be thought of as a total hybridization of ML and OR in which some new algorithms are introduced.
Conclusion
We’ve seen the fundamentals of Operations Research and how it can be integrated with Machine Learning in this post. The distinction to be made here is that Machine Learning models are concerned with a single task prediction. In contrast, operation research is concerned with a wide variety of unique approaches for specific problem classes. As we’ve seen in the instances, we can gain more accuracy and benefits by combining the ML and OR.
