So sánh phương pháp

	Thuật toán Wagner-Whitin	Phân rã Benders	Column Generation (Dantzig-Wolfe)
Lĩnh vực	Vận trù học	Vận trù học	Vận trù học
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	1958	1962	1960
Người khởi xướng	Harvey M. Wagner and Thomson M. Whitin	Jacques F. Benders	George B. Dantzig and Philip Wolfe
Loại	algorithm	algorithm	algorithm
Công trình gốc	Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Management Science, 5(1), 89-96.	Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1), 238-252.	Dantzig, G. B., & Wolfe, P. (1960). Decomposition principle for linear programs. Operations Research, 8(1), 101-111.
Tên gọi khác	Wagner-Whitin lot-sizing, dynamic lot-sizing algorithm	cutting plane method, constraint generation	Dantzig-Wolfe decomposition, column generation method
Liên quan	3	3	3
Tóm tắt	The Wagner-Whitin Algorithm, introduced by Harvey M. Wagner and Thomson M. Whitin in 1958, is a dynamic programming solution to the capacitated lot-sizing problem. It determines optimal production quantities over multiple periods to minimize the total cost of production setup and inventory holding while meeting deterministic demand.	Benders Decomposition, introduced by Jacques F. Benders in 1962, is a powerful algorithmic framework for solving large-scale mixed-integer programming (MIP) problems. It decomposes the problem into a master problem (controlling complicating variables) and subproblems (handling remaining variables), using cutting planes generated from subproblem dual information to iteratively tighten the master problem.	Column Generation, developed by George B. Dantzig and Philip Wolfe in 1960, is a powerful optimization technique for solving large-scale linear programming problems with special structure. Also known as Dantzig-Wolfe Decomposition, it decomposes the problem into a master problem (restricted to a subset of variables/columns) and a pricing subproblem (identifying new variables), iteratively improving the solution by introducing only relevant columns.