Operational research is the application of advanced analytical methods to help make better decisions. It involves using mathematical modeling, statistical analysis, and computer simulation techniques to study complex situations. Operational research aims to provide executives, managers and analysts with the quantitative information needed for decision-making.
What does operational research involve?
Operational research typically involves several steps:
- Defining the problem – Identifying the real issues that need to be addressed.
- Building a mathematical model – Creating a simplified representation of the system using mathematical relationships.
- Gathering data – Collecting relevant data about the problem to input into the model.
- Testing the model – Verifying that the model provides a good representation of the real system.
- Running experiments – Using the model to test different decisions or scenarios.
- Interpreting results – Analyzing the output from the model to gain insights.
- Implementing solutions – Applying the findings from the model to implement changes.
The process is highly iterative, with constant refinement of the model and analysis of new data. The goal is to produce quantitative evidence to support strategic and operational decisions.
What techniques are used in operational research?
Some of the main techniques used in operational research include:
- Linear programming – Optimizing an outcome based on certain linear relationships between variables and constraints.
- Integer programming – A variant of linear programming where variables are restricted to integers.
- Network models – Analyzing graph networks of nodes and connectors, like transport or telecom systems.
- Queuing theory – Modeling waiting lines and congestion in services to improve flow.
- Simulation – Imitating the behavior of a real-world system over time.
- Decision analysis – Evaluating alternatives under uncertainty using decision trees and expected value criteria.
- Time series analysis – Forecasting future values based on historical data patterns.
- Monte Carlo sampling – Using random sampling and probability statistics to model systems with uncertainty.
The choice of technique depends on the particular problem and the kind of data available. Real-world problems often require a combination of OR techniques.
What are the application areas of operational research?
Operational research emerged during World War II for military applications but quickly spread to many sectors of industry and government. Today, OR is used across a wide range of complex planning and management problems, including:
- Transportation – Route planning, transit timetables, delivery logistics
- Energy – Power plant optimization, electricity grid planning, oil/gas field development
- Retail – Supply chain optimization, pricing strategies, sales forecasting
- Healthcare – Hospital capacity planning, appointment scheduling, emergency response
- Manufacturing – Production planning, inventory control, assembly line design
- Finance – Portfolio optimization, risk modeling, algorithmic trading strategies
- Telecommunications – Cell tower location, network traffic management, wavelength routing
- Public services – Disease control, disaster relief logistics, waste management
The wide range of applications highlights the versatility of operational research for gaining insight into complex resource allocation problems across industries.
What are the benefits of using operational research?
Some of the key benefits that can be obtained from applying operational research include:
- Testing decision options and scenarios without disrupting real systems
- Optimizing operations to achieve cost, revenue or service level targets
- Making forecasts and estimates using predictive models
- Allocating limited resources more efficiently
- Quantifying the factors that drive system performance
- Identifying bottlenecks and improvement opportunities
- Providing data-driven guidance for decision making
- Comparing policy or design alternatives
- Anticipating the impact of external influences
By taking an analytical approach, operational research allows executives to make decisions based on quantified trade-offs between alternatives rather than gut feelings. This enhances productivity, reduces costs, and manages risk.
What software tools are used for operational research?
Operational research relies heavily on software platforms to build and solve analytical models. Some of the main tools used include:
- Microsoft Excel – For small models and prototyping algorithms using built-in Solver module.
- Mathematica – General symbolic math software with extensive OR modeling capabilities.
- MATLAB – Leading modeling platform with toolboxes for optimization, simulation and statistics.
- R – Open-source statistical language popular for modeling and machine learning.
- Python – General programming language, popular for scripting OR workflows and integrating tools.
- Gurobi – Industry-leading commercial solver for linear, integer and quadratic programming.
- CPLEX – Widely used optimization solver from IBM, often accessed via MATLAB.
- AnyLogic – Simulation modeling framework for imitating complex systems and processes.
- FlexSim – Discrete event simulation software for manufacturing, logistics and other workflows.
- Palisade DecisionTools – Excel add-ins for optimization, simulation and decision analysis.
The continuing growth in computing power and simulation technology is allowing operational research to tackle ever more complex, real-world problems.
What are the limitations of operational research?
Despite its power and flexibility, there are some inherent limitations to the operational research approach:
- Models are simplifications – They cannot capture every detail and nuance of real systems.
- Garbage in, garbage out – Results depend heavily on the quality of input data used.
- Parameter uncertainty – Key model parameters may not be known precisely.
- Stochastic effects – Randomness can affect model validity for dynamic systems.
- Narrow focus – Models may optimize a specific metric but ignore wider effects.
- Human factors – Cultural and behavioral factors affect how solutions are implemented.
- Solution implementation – Technical insights must still be executed properly to gain benefits.
- Cost and complexity – Significant expertise, time and software required for large scale models.
The best outcomes arise when operational research teams collaborate closely with field experts and decision makers during the entire process.
What education is required for a career in operational research?
Operational research requires strong quantitative and analytical skills. Typical educational backgrounds include:
- Undergraduate degree in mathematics, statistics, computer science, engineering, economics or a physical science.
- Master’s degree in operational research, management science or analytics.
- PhD for advanced research or academic roles.
- Executive education courses in operations management.
- Industry-specific training, e.g. finance, logistics.
- Programming languages like R, Python and MATLAB.
- Certification programs such as from INFORMS or The OR Society.
Continuous learning of new modeling techniques and software tools is also crucial to keep pace with innovation in the field.
What career options exist with operational research skills?
Typical career paths for operational research analysts and consultants include:
- Operations research analyst – Applying OR models to business problems in areas like supply chain, revenue management, mining, energy.
- Business analyst – Supporting diverse analytics and planning functions across departments.
- Data scientist – Designing and implementing machine learning algorithms for prediction.
- Strategy consultant – Providing analytics and insights to address strategic business issues.
- Logistics analyst – Optimizing transportation, warehouse and distribution operations.
- Financial analyst – Creating quantitative models for risk, algorithmic trading, derivatives pricing.
- Revenue manager – Forecasting demand and optimizing pricing for airlines, hotels etc.
- Simulation modeler – Designing discrete event simulations of systems like hospitals, ports, factories.
Strong commercial awareness and communication skills are vital to present insights effectively to senior management.
What are examples of operational research impact?
Here are some real-world examples that demonstrate the power and impact of operational research in practice:
Optimizing kidney exchange programs
Matching models increased transplant surgeries by 20-30% by identifying efficient kidney swaps between incompatible donor-patient pairs.
Reducing airline delays
Scheduling algorithms minimized aircraft ground time and congestion effects, saving millions in costs.
Designing adaptive traffic signals
Real-time optimization of traffic lights improved traffic flow, achieving 10-40% travel time savings.
Boosting warehouse productivity
Simulations tested layout designs and picking routes, leading to substantial throughput increases.
Finding optimal oilfield drilling locations
Algorithms identified high-yield drill sites based on 3D seismic data, improving recoverable reserves.
Allocating fair political districts
Bipartisan neutral redistricting models stopped gerrymandering and enforced representation quotas.
Improving hospital bed utilization
Queueing models ensured optimal bed capacity for fluctuating patient demand, avoiding lost admissions.
Enhancing retail supply chains
Inventory optimization and distribution center location models maximized service levels while minimizing logistics costs.
These examples highlight the versatility of operational research and its ability to deliver major performance improvements across diverse industries.
Conclusion
Operational research provides a powerful quantitative approach for gaining insight into complex resource allocation decisions. It can help organizations improve efficiency, reduce costs, manage risk, and optimize performance across many operational domains. Real-world applications have demonstrated the value of operational research techniques time and again. The ever-increasing availability of data and computing power means the potential of OR will continue to grow. To leverage these benefits requires building mathematical models that reflect real-world complexity, and collaborating closely with decision makers to implement solutions.