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| Wagner-Whitin-algoritmen× | Benders dekomposition× | Kolumngenerering (Dantzig-Wolfe)× | |
|---|---|---|---|
| Ämnesområde | Operationsanalys | Operationsanalys | Operationsanalys |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 1958 | 1962 | 1960 |
| Upphovsperson≠ | Harvey M. Wagner and Thomson M. Whitin | Jacques F. Benders | George B. Dantzig and Philip Wolfe |
| Typ | algorithm | algorithm | algorithm |
| Ursprungskälla≠ | Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Management Science, 5(1), 89-96. DOI ↗ | Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1), 238-252. DOI ↗ | Dantzig, G. B., & Wolfe, P. (1960). Decomposition principle for linear programs. Operations Research, 8(1), 101-111. DOI ↗ |
| Alias | Wagner-Whitin lot-sizing, dynamic lot-sizing algorithm | cutting plane method, constraint generation | Dantzig-Wolfe decomposition, column generation method |
| Närliggande | 3 | 3 | 3 |
| Sammanfattning≠ | 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. |
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