The science behind decision-making in a complex world - operations research

Every day we make decisions with limited resources: time, money, energy, personnel, or production capacity, among others. Some decisions are simple, but others affect large and complex systems in which different elements are tightly interconnected, making decision-making significantly more difficult. In such cases, relying solely on intuition is usually not enough.

Operations research emerged precisely to address this type of situation. Its goal is to help make better decisions based on data, using scientific models and methods, with optimization as a central pillar.

Historical evolution

The origins of operations research can be traced back to World War II. However, although the discipline was not formally established until then, its development can be divided into three stages: before the war, during the war, and after the war.

Mathematical foundations of operations research

The need to optimize resources is not a modern concern. Long before operations research existed as a discipline, similar problems were already being considered:

• Finding the shortest and safest routes for trade.
• Designing strong structures using the minimum amount of material.
• Maximizing profits or minimizing costs in economic activities.

From a mathematical perspective, scholars such as Euler, Lagrange, and Gauss developed fundamental tools for solving optimization problems. However, these advances were mainly focused on theoretical problems and had not yet evolved into a systematic methodology applied to the management of real organizations and systems.

The birth of operations research

Operations research emerged as a discipline during World War II. In the United Kingdom, teams composed of scientists from different fields began working together to solve very concrete problems:

• How to allocate military resources more effectively.
• How to use radar more efficiently.
• How to organize maritime convoys to reduce losses.

For the first time, optimization was applied in a structured way to large-scale real-world decisions. The results were so successful that, after the war, this approach was quickly transferred to the civilian sector.

From military applications to everyday life: expansion into the civilian world

From the 1950s onward, operations research began to be used in companies, public administrations, and industrial settings. Its applications expanded to areas such as:

• Production planning.
• Inventory management.
• Workforce scheduling.
• Design of transportation and distribution networks.

During this period, many of the classical tools of the discipline were developed, always driven by a fundamental question: what is the best way to do things given a set of conditions?

For a more detailed overview of the history of operations research, see (McCloskey, 1987).

What is operations research?

With this context, it becomes easier to understand what we mean by operations research. Below are some of the most widely recognized formal definitions:

Operational Research Society (United Kingdom)

First definition: Operations research is the application of scientific methods to complex problems arising in the direction and management of large systems of men, machines, materials and money.

Modern definition: Operational research is a scientific approach to the solution of problems in the management of complex systems that enables decision makers to make better decisions.

Churchman, Ackoff, and Arnoff (Churchman et al., 1957)

Operations Research is the application of scientific methods, techniques, and tools to problems involving the operations of a system so as to provide those in control of the system with optimum solutions to the problems.

Hillier and Lieberman (Hillier & Lieberman, 1967)

Operations research is concerned with scientifically deciding how to best design and operate man–machine systems, usually under conditions requiring the allocation of scarce resources.

INFORMS

Operations research and analytics is the discipline of using advanced analytical methods to help make better decisions.

In simple terms, operations research consists of understanding a real-world problem, representing it through an abstract model, and using that model to make better decisions, i.e., is the science of better decision-making.

The need to organize knowledge: scientific societies

As operations research grew and was applied in more domains, a clear need emerged: to organize the people working in this field, share knowledge, and give visibility to the discipline. This led to the creation of scientific societies.

These organizations make it possible to:

• Connect researchers, practitioners, and students.
• Share theoretical advances and practical applications.
• Organize conferences and scientific meetings.
• Promote education and outreach.

Thanks to these societies, operations research has become a well-established discipline at an international level.

Major operations research societies

• ORS (Operational Research Society) – United Kingdom, 1948.
• INFORMS – United States, 1952.
• IFORS (International Federation of Operational Research Societies) – 1959.
• EURO (Association of European Operational Research Societies) – 1975.
• ALIO (Latin-Iberoamerican Association of Operations Research) – 1980.

Operations research in Spain: SEIO

In Spain, the discipline is represented by SEIO (Spanish Society of Statistics and Operations Research), which brings together researchers and professionals from both statistics and operations research.

Its main objectives are:

• To promote the scientific and applied development of both disciplines.
• To organize conferences and training activities.
• To foster collaboration between academia, industry, and the public sector.
• To disseminate the role of operations research in society.

Operations research today

Today, operations research plays a role in many decisions that affect our daily lives, even if we are not always aware of it. The discipline has evolved alongside computing, data availability, and the growing complexity of modern systems. It is used, for example, in:

• Commercial logistics:
  • Facility location.
  • Warehouse management.
  • Production planning.
  • Workforce scheduling.
  • Distribution and transportation networks.
• Humanitarian logistics:
  • Prevention: risk analysis, scenario simulation, optimization of early warning systems.
  • Mitigation: management of strategic inventories and resource allocation.
  • Preparedness: evacuation route planning, personnel organization, and drills.
  • Response: distribution of humanitarian aid, allocation of critical resources, and planning under uncertainty.
  • Recovery: reconstruction planning, management of multiple projects, and long-term inventory control.
• Military logistics:
  • Optimal allocation of resources.
  • Planning of convoy routes and transportation.
  • Management of critical inventories.
  • Scheduling of maintenance and equipment repair.
  • Optimization of operations under uncertainty.

Operations research is no longer focused solely on finding mathematically “optimal” solutions. Its current goal is to provide useful, robust, and implementable decisions, even when information is incomplete or problems are too large to be solved exactly.

In an increasingly interconnected world with limited resources, operations research remains an essential tool for understanding complexity, evaluating alternatives, and making better decisions.