Bandingkan metode

	Policy Scenario Goal Programming	Pemrograman Sasaran Stokastik
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1961 (goal programming); policy scenario application 1980s–present	1968
Pencetus	Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature	Contini, B. (building on Charnes & Cooper's chance-constrained programming)
Tipe	Optimization under multiple conflicting goals across policy scenarios	Stochastic multi-goal optimization
Sumber perintis	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 9780471153405	Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586.
Alias	PSGP, Policy GP, Scenario-based Goal Programming, Multi-scenario Goal Programming	SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming
Terkait	5	6
Ringkasan	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.	Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable.