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Operations Optimization

Development of tools for process automation, and heuristics design to solve complex problems in limited timeframes.

Supply chain applications

Production programming

Storage logics and warehouse management

Logistic network design

Vehicle routing

Decision making automation

Team task assignment

Project planning

Transport cargo assignment

Maintenance planning



From model generation, solutions are searched by the use and development of algorithms or heuristics, that constitute logical procedures, deterministic or stochastic, that result in proposals for answers of the posed situation.

Instead of trying different scenarios with a simulation, optimization allows, iterative and autonomously, to find the right combination of variables that comply with the established restrictions, maximizing or minimizing the objective function.

It constitutes a key factor in the automatization of the decision making process.

Optimization algorithms are divided in two large categories given the nature of the problem to solve.

Optimización Continua

Los modelos de Optimización Continua son aquellos en los cuales los valores de las variables puede tomar cualquier valor real. Para estos casos se suele aplicar el algoritmo Simplex, diseñado justamente para problemas de programación lineal.

Optimización Discreta

En contraste, la Optimización Discreta abarca aquellos modelos donde se presenta al menos una variable de control que puede tomar unicamente valores discretos (generalmente enteros). La mayoría de los problemas a resolver en Supply Chain caen dentro de esta categoría. Usualmente, la cantidad de combinaciones de valores es tan elevada que probar todas las opciones no es viable en tiempos razonables. En estos procesos de resolución, se busca balancear la probabilidad de encontrar un óptimo global con el tiempo necesario para su ejecución.


Continuous Optimization

Continuous Optimization models are those in which the variables used in the objective function are required to be continuous. The Simplex algorithm is usually applied in these cases, which was designed just for linear programming problems.

Discrete Optimization

In contrast, Discrete Optimization covers those models where at least one control variable can only take discrete values (generally integers). Most of the problems to be solved in Supply Chain fall into this category. Usually, the amount of combinations of values is so high that trying all the options (known as brute force) is not feasible in reasonable times. Therefore, in these resolution processes, a balance is searched between the probability of finding a global optimum and the time required for it’s execution.

In turn, there are also other ways to categorize problems in order to determine the right algorithm to apply.

Optimization with or without restrictions

Although the global optimum of the system is always sought, there may be restrictions on the values that the variables can take.

Deterministic Optimization

In Deterministic Optimization, it is assumed that all the data for a problem are known and accurate. However, such data is not always available, or simply the nature of the modeled processes present stochastic behaviors, and therefore, said variability must be considered in the model.

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