Question

Part 1. Discuss the differences and similarities between Operations Research and Data Science.

Part 2. What role does optimization play in Operations Research?

Answer 1

part .1

What is Operations Research?

Operations Research (O.R.), or operational research in the U.K, is a discipline that deals with the application of advanced analytical methods to help make better decisions. The terms management science and analytics are sometimes used as synonyms for operations research.

Employing techniques from other mathematical sciences, such as mathematical modeling, statistical analysis, and mathematical optimization, operations research arrives at optimal or near-optimal solutions to complex decision-making problems.

Operations research overlaps with other disciplines, notably industrial engineering and operations management. It is often concerned with determining a maximum (such as profit, performance, or yield) or minimum (such as loss, risk, or cost.)

Operations research encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queuing theory, Markov decision processes, economic methods, data analysis, statistics, neural networks, expert systems, and decision analysis. Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system.

Because of the computational and statistical nature of most of these fields, O.R. also has strong ties to computer science. Operations researchers faced with a new problem must determine which of these techniques are most appropriate given the nature of the system, the goals for improvement, and constraints on time and computing power.

The major sub-disciplines in modern operations research are:

Computing and information technologies
Environment, energy, and natural resources
Financial Engineering
Manufacturing, service science, and supply chain management
Marketing Science
Optimization
Policy modeling and public sector work
Revenue management
Simulation
Stochastic models
Transportation.

Data Science is the practice of:
Asking questions (formulating hypothesis), answers to which solve known problems or unearth unknown solutions that in turn drive business value,
Defining the data needed or working with an existing data set and employing tools (computer science based) to collect, store and explore such data generally in huge volume & variety (often more than 1 TB and 1000s of dimensions),
Identifying the type of analysis to be done to get to the answers and performing such analysis by implementing various algorithms/tools (statistics based), often in a distributed and parallel architecture,
Communicating the insights gathered from the analysis in the form of simple stories/visualizations/dashboards (the Data Product) that a non-data scientist can understand and build conversation out of it. (It should be kept in mind that a product can also be an piece of code that is internal to a company and is used by various departments. The presentation, maintenance, scalability, etc of the code are then the product features, which is often not practiced in many organizations)
Building a higher level abstraction that does steps 2-3-4 in an autonomous way, analyzing & taking actions on new data as they are fed to the system.

DIFFERECES:

Current buzzwords (“data science”, “business analytics”) are just like OR.
Mathematical / statistical sophistication is applied to complex (business) systems, using computational tools.
Value comes from combining domain expertise with numbers.
The best practitioners bridge multiple worlds.
Great communication is key.

Sound familiar? This is exactly what I’ve seen in my experiences at INFORMS events (including this conference, several years ago). And this is why INFORMS, the organization, has been so focused on getting its point of view out, with initiatives such as CAP certification, the rebranding of this conference, and the new big data conference.

But there are some cultural differences too.

Current buzzwords depart from traditional OR
more insight, less action — deliverables tend towards predictions and storytelling, versus formal optimization
more openness, less big iron — open source software leads to a low-cost, highly flexible approach
more scruffy, less neat — data science technologies often come from black-box statistical models, vs. domain-based theory
more velocity, smaller projects — a hundred $10K projects beats one $1M project
more science, less engineering — both practitioners and methods have different backgrounds
more hipsters, less suits — stronger connections to the tech industry than to the boardroom
more rockstars, less teams — one person can now (roughly) do everything, in simple cases, for better or worse

PART.2

Optimization and Operational Research

Operational Research is the discipline of applying advanced analytical methods to help make better decisions.

A branch of Applied Mathematics, Operational Research deals with real world problems. This requires a close collaboration among the clients (enterprises, entrepreneurs, companies, firms, organizations, etc.) who apply the advice of Operational Research, the specialists in Operational Research giving the advice, and everyone who will be influenced by the better decision. The clients may need various operational improvements – for example, increased production efficiency, lower costs, better quality, or improved planning. The mission of Operational Research is to cooperate with the clients and to identify and help to apply improved – if not optimal – solutions to practical problems arising in various branches of industry, transportation, business, etc.

For illustration, we present several examples of practical problems that Operational Research can resolve for the client:

Optimization of coated coils production

Salzgitter Flachstahl, a major German steel producer, manufactures coils of coated sheet metal. The coating line works continuously. Depending upon the coil geometry and the coating material, the coating line needs time-consuming adjustments. The goal is to prepare an optimal daily production plan which minimizes the coating line down-time (adjustments etc.). Compared with the production plans prepared in the previous way, application of the optimized plans (prepared after a theoretical analysis of the problem) achieved the down-time reduction of up to 30 %, with the average of 10 %, significantly more than what was deemed to be possible.

thank you!