Jyoti Luhaniwal

Linear Programming (LP) is a key tool in operations research and optimization that helps with efficient decision-making. It is frequently used to make sure that resources are used as efficiently as possible in fields including public sector planning, engineering projects, production planning, and company management. Despite having a mathematical foundation, LP's true power is in its real-world implementation. By presenting complex decision-making scenarios in an understandable and structured mathematical format, it aids in their simplification. Clear definitions of objectives and constraints enable decision-makers to weigh their alternatives and select the best course of action. Linear programming is still a useful technique for resolving real-world issues in a variety of industries due to its simplicity, effectiveness, and broad application. Furthermore, in organizational planning, linear programming promotes data-driven analysis and logical reasoning. It offers an open framewo...

We often think that policies are made only through debates, political ideas, and public speeches. We see leaders talking about budgets, reforms, and national plans. But what we usually do not see is the quiet work happening in the background — the mathematical models that help shape decisions even before they are officially announced. In today’s world, optimization is not just a topic in mathematics. It is a practical tool for decision-making. It helps governments, hospitals, industries, and cities choose better options. It takes complex problems and organizes them into clear steps. It turns uncertainty into measurable comparisons and trade-offs. At the center of every policy decision lies a simple reality: resources are limited, but needs are endless. The Core of Optimization: Structure in Scarcity Every policy environment operates under constraints. A government cannot spend beyond its budget. A hospital cannot exceed its bed capacity. An energy grid cannot distribute more po...

In the field of Operations Research and Optimization, the Transportation Problem (TP)  is an important model because it is simple, well-structured, and very useful in real-life situations. It helps us decide the cheapest way to send goods from different supply locations to different demand locations while making sure all requirements are satisfied. Even though it is one of the oldest linear programming models, the Transportation Problem is still widely used today in supply chain management, network planning, and decision-making. The Transportation Problem addresses a fundamental logistical question:  How should resources be allocated from several origins to several destinations in order to minimize total transportation cost while satisfying supply and demand requirements?  Each origin (source) has a fixed supply capacity, and each destination has a specified demand requirement. The decision variable represents the quantity transported between each source–destination p...