Mathematical optimization or mathematic programming is a scientific discipline which allows us, within any economic, productive or engineering context, answering questions such as

How much room for improvement do we have?
How can we reach that improvement?

To answer these questions, any economic, productive or engineering problem might be transformed into a mathematical model. Mathematical programming helps us finding the optimal solution among millions of possible alternatives


ESCISSION has developed algorithms that have extended the capabilities of the products there are at present for solving mathematical programming problems in highly uncertain environments.

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Optimization is a branch of mathematics that enables any physical or symbolic system to be planned, operated and modified once its internal rules and final objectives have been defined. Stochastic optimization goes one step further by taking the variability of the input variables and rules into account, in order to answer the following questions:

What is the most appropriate configuration of a system for reaching any given goal when faced with uncertainty?

What are the most appropriate operating rules in a system in which results are maximized, and a set of restrictions on use and risks of failure are fulfilled?

What is the most robust way of expanding a given system under conditions of uncertainty in future operating scenarios?

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Application fields

Most of the technical and financial decisions industry faces at present can be framed within a stochastic optimization problem: how to maximize or minimize results with some restrictions, taking into account the uncertainty associated with the input variables and operating rules.

Structural and Hydraulic

Structural design, dam design, highway design, water resources and reservoir management, etc.


Renewable energy generation, energy transportation, power hiring, etc.

Economy & finances

Stock trades, logistics, investment strategies and portfolios optimization, etc.


Hydro power management problem

One example of using stochastic optimization is the optimal management of reservoir systems in order to maximize the value of the hydroelectric energy produced, bearing in mind certain operational restrictions.

Managers have limited knowledge of how much water is discharged from rivers that feed into the reservoirs, the exact consumptive demands that must be met, or the price of energy, which fluctuates constantly. This context of uncertainty poses the following question: how should turbines be scheduled in order to maximize the expected benefit, whilst maintaining water levels in the reservoir within maximum and minimum limits, with a given probability of failure?

Uncertainty in the input and intermediate parameters (weather, hydrological processes, other consumption, energy prices, etc.) transforms what is a relatively simple mathematical programming problem into a stochastic optimization one, which requires more complex techniques.

Hydro power management problem

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