000			02008nam a22002537a 4500
003			arcduce
005			20230928213947.0
007			t|
008			221021s1976 gw_||||| |||| 00| 0 eng d
020			_a3540074910
040			_aarcduce _carcduce
082	0		_a519.72
100	1		_aKall, Peter _920413
245	1	0	_aStochastic linear programming / _cPeter Kall.
260			_aBerlín : _bSpringer-Verlag, _c©1976
300			_a95 p. : _bil.
490	0		_aEconometrics and operations research ; _v21
504			_abibliografía: p. 93.
520	3		_aToday many economists, engineers and mathematicians are familiar with linear programming and are able to apply it. This is owing to the following facts: during the last 25 years efficient methods have been developed; at the same time sufficient computer capacity became available; finally, in many different fields, linear programs have turned out to be appropriate models for solving practical problems. However, to apply the theory and the methods of linear programming, it is required that the data determining a linear program be fixed known numbers. This condition is not fulfilled in many practical situations, e. g. when the data are demands, technological coefficients, available capacities, cost rates and so on. It may happen that such data are random variables. In this case, it seems to be common practice to replace these random variables by their mean values and solve the resulting linear program. By 1960 various authors had already recog­ nized that this approach is unsound: between 1955 and 1960 there were such papers as "Linear Programming under Uncertainty", "Stochastic Linear Pro­ gramming with Applications to Agricultural Economics", "Chance Constrained Programming", "Inequalities for Stochastic Linear Programming Problems" and "An Approach to Linear Programming under Uncertainty".
650		4	_91432 _aPROGRAMACION LINEAL
650		4	_aOPTIMIZACION _9124
942			_2ddc _cLIBR _j519.72 K 33633
945			_aBEA _c2023-09-29
999			_c35255 _d35255