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1st, Integer Linear Programming, some or all of the decision variables are required to take integer values.
2. Mixed integer Linear Programming, some variables are required to take integer values while others are allowed to be continues.
3.Binary Linear Programming, restricts variables to take binary values (0 or 1). It's often used to model decision making problems with yes/no choices.
Network Flow Problems
Multi-Objective Linear Programming
Stochastic Linear Programming
Production planning: Linear programming can be used to determine the optimal production levels for a factory, given the costs of production, the demand for the products, and the availability of resources.
Four special cases and difficulties arise at times when using the graphical approach to solving LP problems
calculating the “optimal” production levels to maximize profits, given the restrictions of supplies and personnel.
Linear programming (LP) is a mathematical technique used to optimize a linear objective function subject to linear constraints. There are several special cases and variations of linear programming that have specific characteristics and applications. Here are some examples: Integer Linear Programming (ILP): Binary Linear Programming: Mixed Integer Linear Programming (MILP): Fractional Linear Programming: Network Flow Problems:
Special cases or variations of linear programming include:
Integer Linear Programming (ILP)
Binary Linear Programming
Mixed Integer Linear Programming (MILP)
Multi-Objective Linear Programming (MOLP)
Stochastic Linear Programming
Degeneracy
Unboundedness
Infeasibility
Redundant Constraints
Network Flow Problems
Assignment Problems
Integer learning programming has the decision variables are constrained to take only integers values. It can be used in situations like production quantities and project scheduling
Binary Linear Programming, restricts variables to take binary values (0 or 1). It's often used to model decision making problems with yes/no choices.