Stochastic preemptive goal programming to balance goal achievements under uncertainty

Abstract

Structured decision‐making in the presence of conflicting goals is difficult, and even more so when accounting for uncertainties in the goals or constraints. In this article, we propose a new approach to multi‐criteria decision‐making that extends the deterministic preemptive goal programming approach to account for such uncertainties. The uncertainties may be characterized in various ways including a Bayesian network or extensive Monte Carlo multi‐variate output. We contend the proposed stochastic preemptive goal programming approach is particularly applicable when better goal achievement increases uncertainty or induces risk to other goals. Resulting solutions tend to be a balance of the goals' achievements and robust to minor changes to the goals or constraints. We demonstrate the approach using three illustrative examples: a univariate example, a bivariate example, and a stock portfolio optimization example along with an application to determine military requests for absence. Our preliminary results suggest that a stochastic preemptive goal programming approach represents an effective means of analysing multi‐criteria decision‐making problems under uncertainty.

Stochastic preemptive goal programming of Air Force weapon systems mix

“…This paper aligns with the stochastic, preemptive goal programming approach advised by Ledwith et al 19 However, their method applies goal filters on simulation results sequentially to reduce the decision space. Instead of using simulation results, we develop linear goals/constraints that represent the uncertainty.…”

“…We apply a stochastic preemptive goal programming approach as Ledwith describes. 19 With the inability to assess all parameters with certainty (i.e. effectiveness and cost), Aouni et al 17 contend a technique that accounts for uncertainty is preferred.…”

A Decision Support Framework to Evaluate the Sustainability Performance of Urban Road Transportation

“…Measurement errors, variation in input-output relationships and preventing incorrect and biased results require that uncertainty be considered in DEA models (Dehnokhalaji et al, 2022). In addition, it is di cult for the decisionmaker or supply chain manager to determine the deterministic and accurate values for the goals related to the objective functions (Chang et al, 2008;Ledwith et al, 2021). Since multi-objective optimization models are taken into account in this study, the uncertainty conditions are assessed in input data, output data and determination of goal values.…”