Comparer des méthodes

	Programmation par objectifs de scénarios politiques	Programmation par objectifs robuste
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1961 (goal programming); policy scenario application 1980s–present	1961 (GP); 1990s (robust extension)
Auteur d'origine	Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature	Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)
Type	Optimization under multiple conflicting goals across policy scenarios	Mathematical programming under uncertainty
Source fondatrice	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471153405	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471155041
Alias	PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming	RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming
Apparentées	5	5
Résumé	Policy Scenario Goal Programming (PSGP) integrates goal programming optimization with policy scenario analysis to evaluate how well competing policy objectives can be achieved under distinct future conditions. Decision-makers define multiple goals and several plausible policy scenarios, then solve a goal programming model for each scenario to identify which policy strategies best satisfy priority targets across the full scenario space.	Robust Goal Programming (RGP) extends classical goal programming to handle uncertain or ambiguous model parameters. Instead of minimizing deviations from crisp targets, it seeks solutions that remain feasible and near-optimal across a range of plausible scenarios or uncertain data realizations. RGP is particularly valuable in planning problems where goals are aspirational and input data carries inherent variability or estimation error.