In today's fast-paced world, businesses face a myriad of complex challenges and decisions. From resource allocation and logistics to production planning and portfolio optimization, these challenges often involve numerous variables and constraints. That's where mathematical optimization comes in.
Mathematical optimization is the art and science of finding the best solution among a set of possibilities. It's the tool that helps you allocate resources optimally, reduce costs, maximize profits, and make data-driven decisions that lead to strategic advantages. Energy, financial, healthcare, supply chain and manufacturing industries can substantially benefit from optimization modelling.
Partnering with Operations Research Institute for Mathematical Optimization offers numerous advantages:
Our team stands out for its exceptional academic background in operations research, with a strong emphasis on mathematical optimization techniques. This academic foundation ensures a deep understanding of complex optimization methodologies.
Partnering with us means gaining access to a vast network of renowned professionals in the field of mathematical optimization, both locally and internationally.
With extensive experience applying mathematical optimization models in various industries. Our team members have a proven track record of delivering efficient and tailored optimization solutions.
We are committed to leveraging cutting-edge tools and methodologies in mathematical optimization.
We work with our partners to build personalized optimization models of different complexity. This includes linear programming, mixed integer and non-linear programs, and designing solution algorithms to optimize their decision-making. Our standardized robust framework entails:
1. Consulting with our partners to understand the problem
2. Designing the suitable problem formulation with optimization objectives and constraints
3. Coding the problem formulation in the pre-agreed programming language
4. Identifying the solution algorithm, commercial, or open-source package for the solver
5. Gathering data from the partner, performing the statistical analysis to identify model parameters, performing prediction, or forecasting
6. Solving the problem, and performing sensitivity analysis. Reporting the results for the decision variables to the partner with analytical recommendations
