Mixed Integer & Integer Linear Programming

Integer linear programming (ILP) involves mathematical programming problems where the variables are constrained to have integer values. Many engineering, industrial, and financial applications require integer constraints. For instance, in manufacturing, we cannot produce 9.5 cars in a day, as this is not feasible. Rounding off values may also not yield desirable results, so we need to obtain integer solutions.

Mixed Integer Linear Programming (MILP) is an extension of integer programming where some or all of the variables in the problem are constrained to take integer values. This broader class of problems allows for more flexibility in modelling real-world situations. In some cases, it may not be computationally efficient to solve large-scale problems with all integer constraints, hence the use of mixed integer linear programming. The use of cutting plane method, cover inequality, and Lagrangian relaxation helps us to achieve better results in mixed integer linear programming.”

Mixed Integer Linear Programming Pricing

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Integer Linear Programming Pricing

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How our product works

Objective function identification.
Constraints formulation
Model building & Cloud Integration.

Objective function

The objective function in Mixed Integer & Integer Linear Programming is the key focus of the stakeholder, where they want all or some decision variables to converge to an integer result.

Constraint formulation.

In Mixed & Integer linear programming, constraints play a crucial role as they represent the limitations on the available resources. Our algorithm must effectively utilize these resources to achieve optimality. We carefully extract the relevant information from the data to formulate the linear function of constraints, with respect to our objective function.

Model building & Cloud Integration.

We have developed a powerful model and seamlessly integrated it into the cloud, offering it to our customers as a Software as a Service (SaaS) platform.

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