เปรียบเทียบวิธี

	อัลกอริทึม Wagner-Whitin	Benders Decomposition	การสร้างคอลัมน์ (Dantzig-Wolfe)
สาขาวิชา	การวิจัยดำเนินงาน	การวิจัยดำเนินงาน	การวิจัยดำเนินงาน
ตระกูล	Machine learning	Machine learning	Machine learning
ปีกำเนิด	1958	1962	1960
ผู้ริเริ่ม	Harvey M. Wagner and Thomson M. Whitin	Jacques F. Benders	George B. Dantzig and Philip Wolfe
ประเภท	algorithm	algorithm	algorithm
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Wagner-Whitin lot-sizing, dynamic lot-sizing algorithm	cutting plane method, constraint generation	Dantzig-Wolfe decomposition, column generation method
ที่เกี่ยวข้อง	3	3	3
สรุป	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.