Introduction
If you are an active resident of analytics land, you know that Artificial Intelligence (AI) and Machine Learning (ML) tools are the new bosses in town. Every tool, technology and technology solution around you is trying to incorporate them in their solution in some form (Singh, 2021).
On the other hand, Operation Research (OR) is used as an analytical approach or method which can help in solving problems and making decisions. This decision and problem-solving approach can help in management and benefits of an organization. The basic approach for solving problems using OR can start with breaking down the problem into basic components and ends with solving those broken parts in defined steps using mathematical analysis (Verma, 2021).
Application of Operations Research
There are a variety of problem and decision-making domains where OR can be helpful, such as:
- Scheduling of tasks and management of their time.
- Planning of urbanization and agricultural steps.
- Supply chain management
- Enterprise resource planning
- Risk management
- Network marketing.
OR and ML both work on finding the better solution to a problem where models in ML can be used in making decisions. For experienced OR, things become difficult when the set requirements of the solution become higher and manually performing the testing of the solutions is too time-consuming. Also with this testing task the experienced need to estimate the risk before applying the solution to the problem of making any decision.
Using ML can reduce the time taken by the OR and the manual iteration between the testing. Hybridization of ML and OR can be considered as the next advancement of OR where models from machine learning (ML) can help in various tasks that come under OR. However, this combination can also result in a more complexed process or solutions in some cases (Verma, 2021).
Hybridization of ML and OR
We can perform the hybridization of ML and OR in the following four ways:
- ML then OR – Here the ML can help in finding the points or the solution and then after using the OR we can optimize the points of solutions.
- ML in OR – Here we can say that ML is helping us to perform tasks that come under the OR. This can be considered as the operation research procedure.
- OR in ML – Here we can say that the operation research is helping in performing tasks of the machine learning procedures and this can be considered as the machine learning procedure.
- New Hybridization of ML and OR – In this, we can consider it as the total hybridization of ML and OR where we receive some new algorithms (Verma, 2021).
Conclusion
In conclusion, OR and the various ways it can be combined with ML, could be a double-edged sword. It has the potential to produce highly optimized results, but at this point, it is resource and skills-intensive and difficult to apply. Nevertheless, the same could probably be said about ML a few years ago. Therefore combining these two disciplines with techniques could achieve better results in near future (Anadiotis, 2022)
References
Anadiotis, G. (2022, May 25). Could machine learning and operations research lift each other up? Retrieved October 9, 2022, from https://venturebeat.com/ai/could-machine-learning-and-operations-research-lift-each-other-up/
Singh, K. (2021, October 1). Exploring synergies between Operations Research (OR) and machine learning (ML). Exploring Synergies between Operations Research (OR) and Machine Learning (ML). Retrieved October 9, 2022, from https://sapinsider.org/blogs/why-operations-research-or-and-machine-learning-ml-do-not-need-to-compete-with-each-other/
Verma, Y. (2021, November 18). How machine learning is used with operations research? How Machine Learning is Used with Operations Research? Retrieved October 9, 2022, from https://analyticsindiamag.com/how-machine-learning-is-used-with-operations-research/
