Methoden vergleichen

	Policy Scenario Goal Programming	Robust Goal Programming
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1961 (goal programming); policy scenario application 1980s–present	1961 (GP); 1990s (robust extension)
Urheber	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)
Typ	Optimization under multiple conflicting goals across policy scenarios	Mathematical programming under uncertainty
Wegweisende Quelle	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
Aliasnamen	PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming	RGP, Goal Programming under Uncertainty, Robust GP, Uncertainty-Aware Goal Programming
Verwandt	5	5
Zusammenfassung	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.