A fixed recourse integer programming approach towards a scheduling problem with random data: A case study

Abstract

Regardless of the success that linear programming and integer linear programming has had in applications in engineering, business and economics, one has to challenge the assumed reality that these optimization models represent. In this paper the certainty assumptions of an integer linear program application is challenged in an attempt to improve the solution robustness in an uncertain environment. The authors resort to a two-stage, fixed recourse program to introduce random variables with a uniform distribution instead of deterministic expected values in a workforce sizing and scheduling problem. Although the solution to the problem comprises a significantly larger fulltime staff complement than that determined via the problem without the introduction of random variables, the expected workforce requirements preempt and consider the costly expense of casual workers.