Bandingkan metode

	Pemrograman Tujuan Multi-Objektif	Optimasi Multi-Objektif
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1961	1896 (concept); 1989–2002 (evolutionary algorithms era)
Pencetus	Charnes, A. and Cooper, W. W.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Tipe	Mathematical programming / multi-criteria optimization	Optimization framework
Sumber perintis	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 978-0471148258	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	MOGP, Multi-goal programming, Vector goal programming, Multi-criteria goal programming	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Terkait	4	3
Ringkasan	Multi-Objective Goal Programming (MOGP) is a mathematical programming technique that simultaneously pursues several aspirational targets by minimizing weighted deviations from each goal. Rooted in Charnes and Cooper's original goal programming framework (1961), MOGP extends it to handle multiple competing objectives, making it indispensable in operations research, supply chain design, resource allocation, and policy analysis where decision-makers must satisfy — or come close to — multiple conflicting requirements at once.	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.