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This chapter provides a brief review on chance-constrained combinatorial optimization problems, where the decision vector x is purely discrete (or binary). Usually, this structure can be exploited to obtain stronger formulations and specialized algorithms. In this chapter, we mainly focus on two types of approaches to address these difficulties, namely, reformulation approaches and sample average approximation.

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Reformulation Techniques for Chance-Constrained Combinatorial Optimization Problems

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  • 发表日期：

    2025年

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  • Shunyu Yao,
  • Neng Fan,
  • Pavlo Krokhmal

摘要

This chapter provides a brief review on chance-constrained combinatorial optimization problems, where the decision vector x is purely discrete (or binary). Usually, this structure can be exploited to obtain stronger formulations and specialized algorithms. In this chapter, we mainly focus on two types of approaches to address these difficulties, namely, reformulation approaches and sample average approximation.