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https://doi.org/10.1016/j.ejor.2007.12.043

has been cited by the following article:

• TITLE: Shedding Light on Non Binding Constraints in Linear Programming: An Industrial Application

  AUTHORS: Alireza Tehrani Nejad Moghaddam, Thibault Monier

  KEYWORDS: Linear Programming, Solution Interpretation, Non Binding Constraint, Decision Support

  JOURNAL NAME: American Journal of Operations Research, Vol.8 No.1, January 30, 2018

  ABSTRACT: In Linear Programming (LP) applications, unexpected non binding constraints are among the “why” questions that can cause a great deal of debate. That is, those constraints that are expected to have been active based on price signals, market drivers or manager’s experiences. In such situations, users have to solve many auxiliary LP problems in order to grasp the underlying technical reasons. This practice, however, is cumbersome and time-consuming in large scale industrial models. This paper suggests a simple solution-assisted methodology, based on known concepts in LP, to detect a sub set of active constraints that have the most preventing impact on any non binding constraint at the optimal solution. The approach is based on the marginal rate of substitutions that are available in the final simplex tableau. A numerical example followed by a real-type case study is provided for illustration.