Model Building in Mathematical ProgrammingThis extensively revised and updated edition discusses the general principles of model building in mathematical programming and shows how they can be applied by using twenty simplified, but practical problems from widely different contexts. Suggested formulations and solutions are given in the latter part of the book, together with some computational experience to give the reader some feel for the computational difficulty of solving that particular type of model. |
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Page 48
... 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 ingredients 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 ingredients and processing capacity then it would be possible to take account of their ...
Page 104
... 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 128
H. P. Williams. Section 1.2 . This output should be fairly easy to understand when consi- dered in conjunction with the original statement of the problem ( Example 2 of Section 1.2 ) and the format of its presentation to the package ( ...
H. P. Williams. Section 1.2 . This output should be fairly easy to understand when consi- dered in conjunction with the original statement of the problem ( Example 2 of Section 1.2 ) and the format of its presentation to the package ( ...
Contents
Introduction 335 | 3 |
Solving Mathematical Programming Models | 10 |
Building Linear Programming Models | 20 |
Copyright | |
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Common terms and phrases
0-1 variables application arise assignment problem blending problem branch and bound clearly condition considered convex hull depot described in Section drilling example extra constraints factory feasible region Figure following constraints formulation given grinding capacity impose increase indicate industry infeasible input input-output models integer solution integer variables involving IP model knapsack problem LIMIT linear programming model logical conditions manpower master model mathematical programming model matrix minimize minimum cost MPSX naphtha necessary network flow nodes non-convex non-linear non-zero objective coefficient objective function objective value obtained optimal solution output package programs piecewise planning possible practical problems procedure PROD product mix profit contribution quantities ranges redundant represented result right-hand side coefficient rows Section 1.2 set covering problem set packing shadow prices SIGMA simplex algorithm sometimes structure submodels subproblem SVEG tion tons OIL tons VEG total profit totally unimodular transportation problem type of model valuations x₁ y₁ zero