Hassene Aissi

TL;DR: This work surveys complexity results for the min-max and min- max regret versions of some combinatorial optimization problems: shortest path, spanning tree, assignment, min cut, min s-t cut, knapsack, and investigates the approximability of these problems.

TL;DR: This paper investigates the complexity of the min-max and min- max regret assignment problems both in the discrete scenario and interval data cases and shows that these problems are strongly NP-hard for an unbounded number of scenarios.

TL;DR: This paper investigates, for the first time in the literature, the approximation of min–max (regret) versions of classical problems like shortest path, minimum spanning tree, and knapsack, using dynamic programming and classical trimming techniques to establish fully polynomial-time approximation schemes.

TL;DR: This work presents a preference relation based on the weighted sum aggregation, where weights are not precisely defined, and provides an efficient and generic way of generating this preferred set using any standard multi-objective optimization algorithm.

TL;DR: This paper investigates, for the first time in the literature, the approximation of min-max (regret) versions of classical problems like shortest path, minimum spanning tree, and knapsack, using dynamic programming and classical trimming techniques and establishes fully polynomial-time approximation schemes for these problems.