In this article we explore the current landscape of solvers, the key components of optimization solutions.
Behind the programmes and applications used by companies to manage the entry and exit of vehicles, the loading and unloading of containers or the shifts of their workers, there are mathematical models and optimisation algorithms.
These models represent in an abstract way the problem to be solved and find among all the possible valid solutions, the one that comes closest to the desired objective with the least computational effort and response time.
The same problem can be represented by different mathematical models, better or worse, and the quality of the solution will depend to a large extent on this. That is why it is important to have the right professionals and a good mathematical solver. Only in this way will companies be able to make decisions with guarantees of success, adapt to changing conditions and be competitive in the market.
Successfully applying software, mathematical or statistical techniques to automated decision-making requires talent and technology.
- TALENT – Professionals with knowledge and experience in mathematically capturing the characteristics of the business problem and the specific context in which it occurs.
- TECHNOLOGY – Powerful mathematical optimisation tools (solvers) capable of solving complex operational research problems.
What are solvers and which are the main ones on the market?
The demand for mathematical models and algorithms has increased in recent years and with it the number of optimisation programs or solvers. This technology allows companies to incorporate optimisation into their decision-making systems.
The solver boom began in 2009 when IBM bought the pioneering mathematical optimisation technology of the company Ilog and created CPLEX, its current optimiser. Soon after, technology leaders such as Microsoft joined the race, and now companies such as Google are getting in on the act with tools such as open-source Google OR-tools.
Mathematical solvers are programs that are usually based on the simplex method and Branch & Cut algorithms to solve problems, although each of them incorporates modifications in the solving algorithms. Therefore, for the same problem, each solver will have different execution times and results. This may be one of the characteristics to take into account when choosing a mathematical solver, in addition to the cost of acquiring the licence to use these tools.
Let’s take a look at the main existing solvers:
- CPLEX: this is IBM’s mathematical solver, as mentioned above in the article. Some of its main features are: it offers the possibility of connecting to different platforms and programming languages, and it supports problems with a large number of variables.
- Gurobi: is one of the most powerful solvers on the market for solving linear and non-linear programming problems. A very fast tool with very short execution times and accessible for various programming languages
- FICO® Xpress Solver: this is FICO’s solver and is focused on solving linear problems. It also incorporates different tools and libraries to extend its offer. For example, to evaluate the values of the system parameters, it includes the tuning interface, and to represent the solutions, the Mapping tool.
- LocalSolver: this solver is positioned as one of the fastest solvers for supply chain optimisation, vehicle routing problems, production scheduling, etc. It also offers the possibility of connecting with different platforms and programming languages.
The advantages of implementing Mathematical Optimisation
Thanks to the enormous amount of data available and today’s computing power, the full potential of mathematical models can be realised. This allows us to have solutions that add business value and give us a competitive advantage.
This race to further increase both the volume of quality data and computing power continues, as we can see through trends such as the IoT and quantum computers. This contributes to improved modelling results and represents a great opportunity for companies to invest in this type of technology.
Thanks to mathematical optimisation models, companies can:
- Optimise the use of its resources, both material and human.
- Improve operational margins, maximising profits and minimising costs.
- Increase efficiency and productivity.
- Improve the level of service.
At Numens, we help you understand how this technology can add value and improve your day-to-day operations. This way you can get the most out of the models and ensure the success of your implementation.
Any doubts about mathematical solvers?
