A solution given by a predictive model can be more reliable if it gets optimized for being a proper solution to the problem. Different approaches of machine learning are used to build predictive models whereas different approaches of operations research are used to find optimal solutions. The combination of both of these approaches gives such solutions which are not only accurate but also optimal. In this article, we are going to discuss the combination of machine learning and operation research and how it helps in solving specific problems where accurate and optimal solutions are needed. We will also discuss a few notable use cases of this combination. The major points to be covered in this article are listed below.
Table of Contents
What is Operations Research?
Operation research 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 operation research can start with breaking down the problem into basic components and ends with solving those broken parts in defined steps using mathematical analysis.
The overall procedure of operation research can be completed into the following steps:-
Concepts of operation research became very useful for the world during World War II because of the military planner. After the world war, these concepts have become useful in the domain of society, management, and business problems.
Characteristics of Operations Research
There are the following characteristics of a basic operations research procedure:-
Uses of Operations Research
There are a variety of problem and decision-making domains where operations research can be helpful. Some of them are listed below as:
By the above, we can say that the operation research approach is far better than ordinary software and data analytic tools. An experienced person in operation research can benefit an organization to achieve more complete datasets and using all possible outcomes can predict the best solution and estimate the risk.
The above image is a representation of the operation research procedure with its main components. We can say that operation research is a science of optimization using which we can obtain a huge number of improvements in any field. Some of the papers and research are examples of 20-40% of the improvement in the problem-solving domain.
Machine Learning in Operations Research
In the above section, we have an overview of the operation where we have seen how we can find an optimal and best solution to a problem and how we can make decisions using simple steps. When we talk about machine learning we can say the algorithms under machine learning work on the basis of learning from the past histories of the data and information under the data and the main motive of the algorithms is to predict an accurate value that can satisfy the user and perform the task accurately for which model is assigned.
We can say that OR and ML both work on finding the better solution to a problem where models in machine learning can also be used in making decisions. For experienced operation research things become difficult when the set of the solution becomes higher and manually performing the testing of the solutions becomes hectic and time taking. Also with this testing task the experienced need to estimate the risk before applying the solution to the problem of making any decision. Using machine learning we can reduce the time taken by the operation research and the manual iteration between the testing. Hybridization of ML and OR can be considered as the next advancement of operation research where models from machine learning can help in various tasks that come under operation research.
Way to Hybridization of ML and OR
We can perform the hybridization of ML and OR in the following four ways:-
Comparing Operations Research and Machine Learning
Lets go through an example where we are in a city, lets call it Mumbai and we want to travel around Mumbai in an optimal way so that we can cover the most number of locations in a short time and at less cost. So to do this using machine learning we are required to optimize all the possible ways and their times and cost so that the model related to the machine learning can predict an optimal way by considering all the facts in the account. When the same problem comes in the way of operation research it can be thinking of the cost or time or the distance and we can find more than one solution for the problem and after applying them all once we can find an optimal way.
By these procedures of both, we can say that the number of nodes and steps taken by the machine learning algorithms is less than the number of nodes and steps taken by the operation research. We can even say that many of the building blocks of the machine learning models are taken from the operation research procedure. Some of the examples are as follows:
Example of Combination of OR and ML
Lets go through one more example of a road construction company which has got a tender from the government. The task of the company is to repair the road defects. This can be done by the combination of machine learning and operation research where the machine learning models can help in identifying the type of road defects like broken roads in a small area, medium area or large area After that, using the operation research, we can find the beneficial policies for replacement and repairing of the road. This can be a work procedure where the machine learning and operation research is used together for the development. Similarly, there are various domains where we are required to work on both of the technologies for approaching the solution to a problem in a better way.
Solving Problems of ML Using OR
The paradigm of machine learning can be considered as the combination of various domains like sentiment analysis, computer vision, and recommender systems where applying OR with them can help us in various aspects. Also, it can help in solving problems that occur with machine learning. Lets talk about the problems of machine learning and how we can solve it using operation research.
As we know that recommendation systems are becoming more important for a lot of business domains because of their success in providing fruitful recommendations to the user of the business and using these recommendations the owner of the business can make a lot of benefits also they are made using the machine learning procedure where they are used for giving recommendations.
Lets take an example of the restaurant where we have enabled services like online booking and machine learning algorithms are helping in estimating various aspects like eating time of the customer, habits of the customer and customer bookings and recommendation system are installed to provide recommendations to the users according to those attributes of the users. The problem with these instalments comes when the traffic of the customer is very high and the online booking system starts getting confused about the table allotment to the customer.
In such a situation operation research can help in increasing the traffic by managing them and system response time where the work of the operation research procedure can be optimizing the real-time booking, the number of people eating in the real-time, expected number of customers in a particular time. These optimizations can help in simulating the bookings with customer behaviour. This simulation can be done by combining the OR and ML together.
The computer vision algorithms of the machine learning paradigm work on the visual data and one of the main tasks of these algorithms are to classify or identify the images from a given set of images. Lets say we have a computer vision algorithm to track the food demand on a similar restaurant. where a deep learning model is installed with cameras and working for estimating the food wastage and it is working by recognizing the food type and estimating the food demand.
Since we know that pixels of the images will be the main factor in which the classification is dependent and due to distance and size sometimes we face the failure of the deep learning models. An operation research procedure can be enabled with the machine learning or deep learning algorithm, where it can be used for tracking the different matching algorithms between the frames of the image and we can optimize the maximum number of food sold and amount of food wasted.
In the field of sentiment analysis we know we have reached so far in the context of advancement and now many of the systems have become so reliable when we talk about the results that they are producing. One of the major problems with these systems or for making these systems we require a lot of data. And we know it is tough and costly to make such data available for the models. In this scenario, we can use operation research for optimizing data that can be accurate, effective, and cost-effective for the model.
Frequently it happens that the data we gather for modelling is biased by an emotion that can be estimated and tracked by the operation research. When we talk about the NLP system we know that the system cannot autonomously change its emotions and they are also allowed to control them less. Using the operation research we can make them controlled by just optimizing systems behaviour and results.
As we know that the machine learning models are based on the parameters which we need to fit in the models so that using the parameter and the data model be trained to perform the task which is assigned to the model and also we see that before feeding data into the model we require parameters that can help the model to work well with the data. Optimization of the parameters can be done by operation research because we have defined earlier that operation research is a science of optimization. The better fit parameters can be obtained by optimizing the sets of parameters using the operation research techniques.
Use Cases of Combination of ML and OR.
As of now, we have seen various ways and benefits of using the OR and ML together. In this section of the article, we will discuss some real-life use cases of this combination. Since both of them are very relatable to each other many of the big giant companies like google, amazon, etc. are using the combination to obtain a good result and provide customer satisfaction for example:
The above-given examples of real-life use cases of the combination of ML and OR are some major examples that are consistent with the improvement. There can be various examples of this combination and also the only motive is to use the combination to improve the work strength and accuracy and benefit of the organizations.
In this article, we have seen what are the basics of operation research and how it can be combined with machine learning. The point to be noted here is that the machine learning models are related and concerned with the one task prediction whereas the operation research is concerned with the large collection of unique methods for specific classes of problems. As we have seen in the examples we can achieve higher accuracy and benefits using the combination of the ML and OR.
Continue reading here:
How Machine Learning is Used with Operations Research? - Analytics India Magazine
- PhD Program | ML (Machine Learning) at Georgia Tech - January 20th, 2022
- Dask-ML dask-ml 2021.11.31 documentation - January 20th, 2022
- How Snapchat Is Using AI And Machine Learning To Thwart Drug Deals - Hot Hardware - January 20th, 2022
- Data Vault Holdings Expands Expertise In Artificial Intelligence, Machine Learning, and Big Data; Appoints Tony Evans of C3 AI To Advisory Board -... - January 20th, 2022
- Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction | Scientific Reports -... - January 20th, 2022
- The 6 Best Deep Learning Tutorials on YouTube to Watch Right Now - Solutions Review - January 20th, 2022
- Agnostiq Announces Partnership With Mila to Bridge the Quantum Computing and Machine Learning Communities - PRNewswire - January 20th, 2022
- Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes | Scientific... - January 20th, 2022
- 6 Ways Machine Learning Can Improve Customer Satisfaction - TechSpective - January 20th, 2022
- Analyzing Twisted Graphene with Machine Learning and Raman Spectroscopy - AZoNano - January 20th, 2022
- Cloudian Partners with WEKA to Deliver High-Performance, Exabyte-Scalable Storage for AI, Machine Learning and Other Advanced Analytics -... - January 20th, 2022
- Klika Tech Joins tinyML Foundation to Accelerate Development of Machine Learning at the Edge - PR Web - January 20th, 2022
- From Coffee Cart to Educational Computing Platform - UC San Diego Health - January 20th, 2022
- Five Machine Learning Applications in Healthcare - CIO Applications - January 20th, 2022
- Global Machine Learning in Automobile Market Size 2021-2029 Trend and Opportunities Discovery Sports Media - Discovery Sports Media - January 20th, 2022
- AI and Advance Machine Learning in BFSI Market Global Report 2021-2030 Featuring Leading Players - Cisco, SAP, Microsoft and IBM Among Others -... - December 22nd, 2021
- Machine Learning Democratized: Of The People, For The People, By The Machine - Forbes - December 22nd, 2021
- Top Python Machine Learning Libraries to Explore in 2022 - Analytics Insight - December 22nd, 2021
- Grants totaling $4.6 million support the use of machine learning to improve outcomes of people with HIV - Brown University - December 22nd, 2021
- Chat Commerce, machine learning and a stronger privacy focus eCommerce predictions for 2022 - BetaNews - December 22nd, 2021
- New platform uses machine-learning and mass spectrometer to rapidly process COVID-19 tests - UC Davis Health - December 22nd, 2021
- Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% 2021: Global Industry Insights by Global Players, Regional Segmentation,... - December 22nd, 2021
- Top Computer Vision Jobs to Apply in December 2021 - Analytics Insight - December 22nd, 2021
- LiveFreely Announces Apple Watch Version of 'BUDDY,' the Predictive AI-Driven Digital Health Assistant for Seniors and Their Loved Ones - Yahoo... - December 22nd, 2021
- These are the top priorities for tech executives in 2022, survey reveals - CNBC - December 22nd, 2021
- Machine Learning as a Service (MLaaS) Market 2021: Big Things are Happening in Development and Future Assessment by 2031 - Digital Journal - December 22nd, 2021
- Revisit Top AI, Machine Learning And Data Trends Of 2021 - ITPro Today - December 16th, 2021
- The automated machine learning market is predicted to reach $14,830.8 million by 2030, demonstrating a CAGR of 45.6% from 2020 to 2030 - Yahoo Finance - December 16th, 2021
- Human-centered AI can improve the patient experience - Healthcare IT News - December 16th, 2021
- Continual Launches With $4 Million in Seed to Bring AI to the Modern Data Stack - Business Wire - December 16th, 2021
- Artificial intelligence accurately predicts who will develop dementia in two years - EurekAlert - December 16th, 2021
- Real World Application of Machine Learning in Networking - IoT For All - December 16th, 2021
- Machine learning predicts risk of death in patients with suspected or known heart disease - EurekAlert - December 16th, 2021
- Reasons behind the Current Hype Around Machine Learning - CIO Applications - December 16th, 2021
- They test a machine learning system with 530,000 million parameters and this warns of the dangers of artifi... - Market Research Telecast - December 16th, 2021
- Quantum Mechanics and Machine Learning Used To Accurately Predict Chemical Reactions at High Temperatures - SciTechDaily - December 16th, 2021
- AWS re:Invent: How to Use Machine Learning and Other Technology to Make the Most of Your Data - Inc. - December 3rd, 2021
- A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation |... - December 3rd, 2021
- Mindtree has Earned the Al and Machine Learning on Microsoft Azure Advanced Specialization - PRNewswire - December 3rd, 2021
- AFTAs 2021: Most innovative third-party technology vendor (AI, machine learning and analytics)Moody's Analytics - www.waterstechnology.com - December 3rd, 2021
- Machine Learning Clarifies Stress-Based Degradation of Biosimilars - The Center for Biosimilars - November 25th, 2021
- Artificial Intelligence, Machine Learning, and Biometric Security Technology will be Drivers of Digital Transformation in 2022 And Beyond: IEEE... - November 25th, 2021
- ExoMiner Goes Planet Hunting! NASA's Machine Learning Network Validates 301 New Exoplanets at One Go | The Weather Channel - Articles from The Weather... - November 25th, 2021
- BIS: What Does Machine Learning Say About The Drivers Of Inflation? - Exchange News Direct - November 25th, 2021
- Machine learning can improve your public services. Are you ready to take the red pill? - The Register - November 25th, 2021
- Less energy, better quality PAM images with machine learning - The Source - Washington University in St. Louis - Washington University in St. Louis... - November 25th, 2021
- How AI Is Poised to Help Humanity - Entrepreneur - November 25th, 2021
- Women Innovators And Researchers Who Made A Difference In AI In 2021 - Analytics India Magazine - November 25th, 2021
- Machine learning optimization of an electronic health record audit for heart failure in primary care - DocWire News - November 25th, 2021
- Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain - DocWire... - November 25th, 2021
- Design of AI may change with the open-source Apache TVM and a little help from startup OctoML - ZDNet - November 25th, 2021
- Global Marketing Automation Market Report 2021: Market to Reach $6.3 Billion by 2026 - GlobeNewswire - November 25th, 2021
- IEEE: Most Important 2022 Tech Is AI/Machine Learning, Cloud and 5G - Virtualization Review - November 20th, 2021
- ML Kit | Google Developers - November 20th, 2021
- MCubed does web workshops: Join Mark Whitehorns one-day introduction to machine learning next month - The Register - November 20th, 2021
- DEWC, AIML partner on AI and machine learning to enhance RF signal detection - Defence Connect - November 20th, 2021
- Machine learning: Aleph Alpha works on transformative AI with Oracle and Nvidia - Market Research Telecast - November 20th, 2021
- Edinburgh machine learning specialist to add 100 jobs thanks to investment co-venture - The Scotsman - November 20th, 2021
- Brivo Unveils Anomaly Detection, a Revolutionary Technology that Harnesses Access Data and Machine Learning to Strengthen Built World Security - Yahoo... - November 20th, 2021
- Adelaide at the centre of next generation AI research - Newswise - November 20th, 2021
- Research Team Probes History with Cutting-Edge Tech - Bethel University News - November 20th, 2021
- DataX is funding new AI research projects at Princeton, across disciplines - Princeton University - November 20th, 2021
- Alphabet is putting its prototype robots to work cleaning up around Googles offices - The Verge - November 20th, 2021
- Red Hat bets on artificial intelligence and ... - BNamericas English - November 15th, 2021
- Top 10 Machine Learning Projects to Boost Your Resume - Analytics Insight - November 15th, 2021
- Exploring the Impact of Machine Learning and Artificial Intelligence in Drug Development from Discovery to Healthcare - PR.com - November 15th, 2021
- Google and AWS harness the power of machine learning to predict floods and fires - ZDNet - November 15th, 2021
- BigBear.ai And Palantir Announce Strategic Partnership, Combining AI-powered Products With Next Generation Operating Platform - Yahoo Finance - November 15th, 2021
- Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care... - November 15th, 2021
- Verizon CIO Shankar Arumugavelu on putting emerging technologies to work - CIO - November 15th, 2021
- Middle East and Africa Machine Learning Market Report by Connectivity Technology, by Application, by Type, by Region Global Forecast to 2026 ... - November 15th, 2021
- Global Machine Learning in Healthcare Market Potential growth, attractive valuation make it is a long-term investment 2027 Energy Siren - Energy... - November 15th, 2021
- Qualcomm is researching machine learning at the edge - Stacey on IoT - November 3rd, 2021
- TrainerRoad Announces Release of Adaptive Training Platform, Making Machine Learning-Powered Training Available to Cyclists - Outside Business Journal - November 3rd, 2021
- Turn your tech skills into machine learning expertise with this book and class bundle - TechRepublic - November 3rd, 2021
- Psychologists use machine learning algorithm to pinpoint top predictors of cheating in a relationship - PsyPost - November 3rd, 2021
- MIT: Forcing ML Models to Avoid Shortcuts (and Use More Data) for Better Predictions - insideHPC - November 3rd, 2021
- Top Machine Learning Tools Used By Experts In 2021 - Analytics Insight - November 3rd, 2021
- New exhibition to investigate the history of AI & machine learning in art. - FAD magazine - November 3rd, 2021
- Machine Learning May Help Predict Success of Prescription Opioid Regulations | Columbia Public Health - Columbia University - November 3rd, 2021