| 27.2. |
Introduction and motivation. Efficient and proper efficient solutions. Scalarization and relation to (proper) efficient solution set.
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| 6.3. |
Convex multiobjective optimization: relation between (proper) efficient solutions and optimal solutions of scalarizations (Geoffrion theorem).
Karush-Kuhn-Tucker optimality conditions for multiobjective problems: necessary conditions and sufficient conditions.
|
| 13.3. |
Multiobjective linear programming: test of efficiency of a feasible point (Charnes & Cooper), characterization of efficient solutions via scalarization (Isermann), topology of the efficient solution set.
|
| 20.3. |
Multiobjective linear programming and relation to parametric programming.
Methods of type III.: Geoffrion method, STEM, algorithms of dialogs (Guddat, Wendler).
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| 27.3. |
Methods of type I.: Methods of global objective function and Benson's method.
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| 3.4. |
Methods of type II.: goal attainment, epsilon-constraints, lexicografical method, goal programming.
|
| 11.4. |
Methods of type IV.: Inner and outer approximation, Normal-Boundary Intersection method. General utility function approach.
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| 17.4. |
Combinatorial multiobjective optimization: Shortest path and minimum spanning tree problems with multiple criteria.
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| 24.4. |
Cone efficiency and dominance. DEA (Data Envelopment Analysis).
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| 15.5. |
Interval multiobjective linear programming: possibly efficiency and necessary efficiency, computational complexity, characterization, geometry of the efficient solution set.
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| 22.5. |
AHP (Analytic Hierarchy Process): comparison matrix and its algebraic properties, consistency, hierarchy, examples.
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