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    Operations Research

    Operations Research (OR) is ORMAE’s core strength. We take pride in the OR focused organizational knowledge that we have created over years. We leverage this knowledge to solve real world complex problems that our clients face on day-to-day basis.
    OR is a mathematical tool. The following paragraphs explain how ORMAE leverages OR for solving challenges faced by clients in areas of logistics, supply chain management, finance, healthcare, transportation etc.

    Here is a simple example of how OR is leveraged to find best possible solution. Imagine one of our clients produces two products A and B. The client needs to decide quantity of ‘A' and quantity of 'B’ that are supposed to be manufactured to maximize the profit. ‘A' needs a lot of raw material but very less workforce effort. For product 'B’, it is other way round. Also, given the capacity limitations let us say the client can produce only 100 units of A at max or 50 units of B at max. Thus, the client has the following options to produce

    • 1. A=100, B = 0
    • 2. B=50 and A=0
    • 3.Options in between the above two (say (A=60, B=30) or say (A=55, B=40)……)

    If they choose option 1, they may leave workforce un-used and thus may get a hit on profit. If they choose option 2, they may leave raw material un-used and thus may get a hit on profits. Probably one of the options in between the above two would be most profitable option (optimal solution). How does the client identify that option? Here is where ORMAE comes in! We create a mathematical model, to find that option and use algorithms to find that quickly. This is operations research (OR). In real world, the clients have thousands of products and not just two. Thus, the solution options available to them run in tens of thousands and sometimes into millions.
    Utility of OR depends on accurate data, availability of computing power and algorithms that can leverage the data and computing power efficiently. We at ORMAE have extensive experience in leveraging algorithms for the optimization challenges that companies face. Also, given the huge amount of data and computing power that is needed for these problems, we have developed auxiliary capabilities to provide ideas around creation of effective data infrastructure and computing capabilities to clients.

    Core Components of Optimization

    Optimization integrates several key components to address complex problems effectively:

    Image of Problem Formulation
    Problem Formulation

    Defining the problem, identifying decision variables, and establishing objectives and constraints.

    Image of Mathematical
    Mathematical Modeling

    Representing the problem mathematically using techniques like linear programming, integer programming, or simulation.

    Image of Data
    Data Collection and Analysis

    Gathering and analyzing relevant data to derive actionable insights.

    Image of Optimization method
    Optimization Methods

    Applying algorithms to determine optimal solutions for decision variables.

    These components work together to offer a structured and analytical approach to solving problems and improving decision-making.

    Step by step Model
    development methodology → REQUIREMENT → OR MODEL → OR SOLUTION

    Key Techniques and Methodologies in Optimization

    Deterministic models use known parameters, while stochastic models incorporate uncertainty through probability distributions.

    Includes Linear Programming (LP), Integer Programming (IP), and Non-linear Programming (NLP) for finding optimal solutions.

    Involves regression analysis, hypothesis testing, and time series analysis for data interpretation and forecasting.

    Used to model uncertainties and dynamic systems, such as queueing models and Markov chains.

    Provide good-enough solutions for complex problems using methods like Genetic Algorithms, Simulated Annealing, and Tabu Search.

    These methodologies enable practitioners to develop efficient, cost- effective, and actionable solutions.

    Optimization in Practice: Real-World Applications

    Optimization is applied across various sectors to improve processes and outcomes:

    Includes Economic Order Quantity (EOQ) models for inventory optimization and Vehicle Routing Problems (VRP) and vehicle loading problems for delivery routes.

    Enhances resource allocation and patient flow

    Applies portfolio optimization and option pricing

    Assists in production planning, scheduling, and facility layout design and supply network design.

    Optimizes airline scheduling, public transportation planning, and other logistics.

    We solve the real-world problems and deploy and integrate them with clients’ existing systems so that clients can leverage the created models, products and solutions sustainably.

    Case Studies

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