Model Building in Mathematical Programming |
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Page 41
... Section 1.2 represented only one of a number of products ( brands ) which a company manufactured . If the different products used some of the same ingre- dients and processing capacity then it would be possible to take account of their ...
... Section 1.2 represented only one of a number of products ( brands ) which a company manufactured . If the different products used some of the same ingre- dients and processing capacity then it would be possible to take account of their ...
Page 96
... Section 1.2 has a material balance constraint to make sure that the weight of the final product equals the total weight of the ingredients . The right - hand side value is zero . The shadow price predicts the effect of altering this ...
... Section 1.2 has a material balance constraint to make sure that the weight of the final product equals the total weight of the ingredients . The right - hand side value is zero . The shadow price predicts the effect of altering this ...
Page 118
... Section 1.2 ) and the format of its presentation to the package ( Section 2.3 ) . PROBLEM BLEND DUMP : DUMP 3 SOLUTION RIGHT HAND SIDE CAP OBJECTIVE PROF COLUMN INFORMATION NAME + + B VEG1 B VEG2 OIL1 VALUE 159.2593 40.7407 OBJECTIVE ...
... Section 1.2 ) and the format of its presentation to the package ( Section 2.3 ) . PROBLEM BLEND DUMP : DUMP 3 SOLUTION RIGHT HAND SIDE CAP OBJECTIVE PROF COLUMN INFORMATION NAME + + B VEG1 B VEG2 OIL1 VALUE 159.2593 40.7407 OBJECTIVE ...
Contents
Building Linear Programming Models | 3 |
Structured Linear Programming Models | 36 |
183 | 50 |
Copyright | |
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Common terms and phrases
0-1 variables allocation application arcs arise assignment problem branch and bound clearly coal condition considered convex hull depot described in Section discussed in Section example factory feasible region Figure formulation give rise given impose industry infeasible input input-output models integer programming models integer solution integer variables involving IP model knapsack problem LHAR limited linear programming model logical condition manpower mathematical programming model matrix Maximize method minimize multi-period naphtha network flow network flow problem node objective coefficients objective function objective value obtained OIL3 optimal solution output package programs planning possible practical problems procedure PROD PROD2 PROD3 PROD5 profit contribution programming problem quadratic assignment problem quantities ranges represented requirement restricted master model right-hand side coefficient Section 1.2 set covering problem set packing shadow prices simplex algorithm solve specialized algorithm submodels tion tons OIL2 transhipment transportation problem type of model unit upper bound valuations VEG1 x₁ y₁ zero