Operation Research Models
Operational Research (OR) Models, also known as Management Science Models and Decision Science Models, are mathematical and analytical methods used to answer complex questions and make informed decisions in many fields, including business, engineering, healthcare, logistics, and finance.
By formulating real-world problems as mathematical equations or algorithms, OR models allow decision-makers to find the best solutions under given constraints, optimizing processes, resources, and outcomes.
It is the main objective of OR models to maximize profits, minimize costs, improve efficiency, and maximize overall performance. Decision-making situations involving multiple variables, uncertainties, and constraints need to be considered simultaneously using these models. There are several types of OR models, each suited for a different type of problem. Here are some of the most common types of OR models:
1. Linear Programming (LP) Model:
Linear Programming (LP) is one of the most widely used and prominent OR models. A linear equation represents the relationship between a decision variable and an objective/constraint when the objective function and constraints are all linear.
Profit, cost, utility, or any other relevant metric is typically represented by a linear function, and the objective of LP is to maximize or minimize it. Constraints limit the possible values of these variables, reflecting real-world limitations on resources and capacity, while decision variables represent the quantities to be determined.
A variety of fields utilize LP, including production planning, supply chain optimization, portfolio optimization, resource allocation, and transportation planning. In 1947, George Dantzig developed the Simplex Method, a popular algorithm for solving linear programming problems.