Operations Research in Decision-Making

The use of mathematical and statistical techniques to analyze and solve complex problems, especially in business and industry.

The process of choosing the best option from a set of possible alternatives. It involves assessing the pros and cons of each option and selecting the one that best meets the desired outcome.

Using quantifiable data and methods to analyze decisions, rather than relying solely on intuition or experience.

The potential consequences or outcomes of a decision, both positive and negative.

The use of computers and specialized software to help decision-makers process large amounts of data and identify optimal solutions.

The process of analyzing and evaluating all possible actions or strategies to find the best one.

A situation where a decision needs to be made in the absence of complete or accurate information.

Using both quantitative methods and qualitative factors to make informed decisions. This recognizes the complexities of real-world problems.

A field of study that uses quantitative methods to analyze and optimize complex decision-making problems, especially in organizational contexts.

World War II, due to the need for strategically allocating limited resources for military operations.

A group of experts from diverse fields, like mathematics, statistics, and engineering, working together to find the best solutions for complex problems.

Finding the best possible solution considering all factors and constraints, considering the overall good of the organization.

The industrial boom after World War II, advancements in OR techniques like linear programming, and the advent of high-speed computers.

A structured approach to analyzing and solving complex problems using quantitative techniques.

The goal is to find the best solution that balances competing goals or interests, optimizing for the organization as a whole.

The OR approach is valuable when there are multiple options and you need to select the best one, balancing multiple goals, considering resources, and finding the global optimum.

A decision that benefits one part of an organization but may not be the best for the whole organization.

This approach focuses on finding the best solution for the entire organization, even if it might not be the best for every individual part.

OR helps resolve conflicts between different departments by seeking the best outcome for the whole organization.

OR uses scientific models to predict and compare the outcomes of different decisions, strategies or controls.

The purpose of OR is to help management make better decisions based on scientific analysis.

OR is a systematic way to analyze problems in systems using quantitative methods, techniques, and tools.

Considering all conflicting goals and perspectives of different departments within an organization, while making decisions that benefit the entire organization.

Using objective measures of desirability (like profit or efficiency) to evaluate different options and choose the one that maximizes the desired outcome.

A simplified representation of a real-world system, capturing its essential aspects and relationships between key variables.

Operations research often requires computers to handle complex calculations and analyze large amounts of data.

Recognizing that human factors, beyond just numbers, play a significant role in decision-making processes.

OR aims to enhance the effectiveness of management decisions by providing data-driven insights and optimal solutions.

When a model in OR only captures key elements and ignores minor details, it's called an approximation or abstraction.

These models help you explore different 'what if' scenarios by relating dependent and independent variables, allowing you to predict outcomes based on various choices.

These models aim to find the optimal solution to a problem, considering limited resources and constraints. They provide recommended actions.

These models represent a system at a specific point in time, without considering changes over time. For example, determining inventory levels based on current demand.

These models account for changes over time and consider the impact of those changes on decisions. They analyze sequences of decisions over a period.

These models assume that all parameters, constants, and relationships are known with certainty when making a decision. The outcome is uniquely determined for a specific set of inputs.

These models acknowledge that variables are uncertain and can vary. They incorporate probability and statistical methods to analyze possible outcomes.

These models aim to evaluate the impact of complex decisions on the entire system or organization, considering multiple factors and interactions.

These models are used to simulate real-world situations and assess the impact of different strategies. They involve creating a simplified representation of the system and testing different scenarios.

Models that incorporate at least one random variable for parameters or decision variables. These models are used when outcomes are uncertain and can be better understood through probability distributions.

Models that use a set of rules to solve problems, even if they might not be optimal.

Models that have a well-defined mathematical structure and can be solved using known analytical or mathematical techniques.

Models are economical representations of complex systems. They help decision-makers understand the system's structure and operation.

In probabilistic models, the consequences of changes in independent variables cannot be predicted with certainty.

Predicting a pattern of values for variables in probabilistic models is possible by studying their probability distributions.

Insurance against events such as fire, accidents, and sickness are examples of real-world applications of probabilistic models.