Operations Research

 

Operations research, or operational research is a discipline that deals with the application of advanced analytical methods to help make better decisions.

 

It is often considered to be a sub-field of mathematics. 

 

The terms management science and decision science are sometimes used as synonyms.

 

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.

 

Because of its emphasis on human-technology interaction and because of its focus on practical applications, operations research has overlap with other disciplines, notably industrial engineering and operations management, and draws on psychology and organization science.

 

Operations research is often concerned with determining the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost) of some real-world objective. Originating in military efforts before World War II, its techniques have grown to concern problems in a variety of industries.

 

Operational research (OR) 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, queueing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, neural networks, expert systems, decision analysis, and the analytic hierarchy process. 

 

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, OR also has strong ties to computer science and analytics.

 

Operational 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 operational research, as identified by the journal Operations Research,[6] are:

·         Computing and information technologies

·         Financial engineering

·         Manufacturing, service sciences, and supply chain

          management

·         Marketing Engineering[7]

·         Policy modeling and public sector work

·         Revenue management

·         Simulation

·         Stochastic models

·         Transportation

Examples of OR in action

 

 

·         Scheduling:

o    of aircrews and the fleet for airlines,

o    of vehicles in supply chains,

o    of orders in a factory and

o    of operating theatres in a hospital.

 

·         Facility planning:

o    computer simulations of airports for the rapid and

o    safe processing of travelers,

o    Improving appointments systems for medical practice. 

 

·         Planning and forecasting:

o    Identifying possible future developments in telecommunications, deciding how much capacity is needed in a      

      holiday business.

 

·         Yield management:

o    Setting the prices of airline seats and hotel rooms to reflect changing demand and the risk of no shows.

 

·         Credit scoring:

o    Deciding which customers offer the best prospects for credit companies.

 

·         Marketing:

o    evaluating the value of sale promotions,

o    developing customer profiles and

o    Computing the life-time value of a customer.

 

·         Defense and peace keeping:

o    Finding ways to deploy troops rapidly.

 

 

Some OR methods and techniques

 

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  • Computer simulation:

    • Allowing you to try out approaches and test ideas for improvement.

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  • Optimization:

    • Narrowing your choices to the very best when there are so many feasible options that comparing them one by one is difficult.

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  • Probability and statistics:

    • Helping you measure risk, mine data to find valuable connections and insights in business analytics, test conclusions, and make reliable forecasts.

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  • Problem structuring:

    • Helpful when complex decisions are needed in situations with many stakeholders and competing interests.