Monday, 15:45 - 16:10 h, Room: H 1029


Wlodzimierz Ogryczak
Fair multiobjective optimization: Models and techniques


In systems which serve many users there is a need to respect some fairness rules while looking for the overall efficiency, e.g., in network design one needs to allocate bandwidth to flows efficiently and fairly, in location analysis of public services the clients of a system are entitled to fair treatment according to community regulations. This leads to concepts of fairness expressed by the equitable multiple objective optimization. The latter is formalized with the model of multiple objective optimization of tail averages and the Lorenz order enhancing the Pareto dominance concept. Due to the duality theory, the order is also equivalent to the second order
stochastic dominance representing multiple objective optimization of the mean shortages (mean below-target deviations). Despite equivalent, two orders lead to different computational models though both based on auxiliary linear inequalities and criteria. Moreover, the basic computational models can be differently enhanced. We analyze advantages of various computational models when applied to linear programming and mixed integer programming problems of fair optimization.


Talk 2 of the invited session Mon.3.H 1029
"Multi-objective optimization" [...]
Cluster 15
"Multi-objective optimization" [...]


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