Process / pipelineSimulation / optimization
Mixed-Integer Programming — Exact optimization over continuous and integer decisions
Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.
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Sources
- Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
- Wolsey, L. A. (1998). Integer Programming. Wiley-Interscience, New York. ISBN: 9780471283669
Related methods
Referenced by
Bayesian Integer ProgrammingBayesian Mixed-Integer ProgrammingDeterministic Dynamic ProgrammingDeterministic Integer ProgrammingDeterministic Linear ProgrammingDeterministic Mixed-Integer ProgrammingMulti-objective mixed-integer programmingMulti-Objective OptimizationRobust Integer ProgrammingRobust Mixed-Integer ProgrammingStochastic Integer ProgrammingStochastic Mixed-Integer Programming