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<p>We consider decision-making problems in which there are both “hard” constraints that must be satisfied and “soft” constraints that the decision-maker would like to satisfy. When no feasible solution exists that satisfies all of the soft constraints, the decision-maker must choose which of these to enforce and which to violate while still ensuring that all of the hard constraints remain satisfied. Enumerating all minimally infeasible and maximally feasible subsets of the soft constraints can assist in making an informed decision, but generating these sets can be a time-intensive process. We propose two enhancements to a known algorithm for generating these sets and evaluate their computational impact on a collection of real-world problems from a healthcare setting.</p>

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Evaluating new methods for improving performance of algorithms for enumerating maximally feasible and minimally infeasible sets of constraints

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Daiwen Zhang,
  • Amy E.M. Cohn,
  • Marina A. Epelman

摘要

We consider decision-making problems in which there are both “hard” constraints that must be satisfied and “soft” constraints that the decision-maker would like to satisfy. When no feasible solution exists that satisfies all of the soft constraints, the decision-maker must choose which of these to enforce and which to violate while still ensuring that all of the hard constraints remain satisfied. Enumerating all minimally infeasible and maximally feasible subsets of the soft constraints can assist in making an informed decision, but generating these sets can be a time-intensive process. We propose two enhancements to a known algorithm for generating these sets and evaluate their computational impact on a collection of real-world problems from a healthcare setting.