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Mixed-Integer Linear Optimization

In: Optimization in Engineering

Author

Listed:
  • Ramteen Sioshansi

    (The Ohio State University)

  • Antonio J. Conejo

    (The Ohio State University)

Abstract

In this chapter, we study mixed-integer linear optimization problems, which are also known as mixed-integer linear programming problems (MILPPs). MILPPs are problems with an objective function and constraints that all linear in the decision variables. What sets MILPPs apart from linear optimization problems is that at least some of the variables in MILPPs are constrained to take on integer values. This chapter provides examples to show the practical significance of MILPPs. We also demonstrate the use of a special type of integer variable known as a binary variable, to model a number of types of nonlinearities and discontinuities while maintaining a linear model structure. Two solution techniques for MILPPs are also introduced.

Suggested Citation

  • Ramteen Sioshansi & Antonio J. Conejo, 2017. "Mixed-Integer Linear Optimization," Springer Optimization and Its Applications, in: Optimization in Engineering, chapter 0, pages 123-196, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-56769-3_3
    DOI: 10.1007/978-3-319-56769-3_3
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