Generation (or a posteriori) methods in Multi-Objective Mathematical Programming (MOMP)
is the most computationally demanding category among the MOMP approaches. Due to the …

Min–max and min–max regret criteria are commonly used to define robust solutions. After
motivating the use of these criteria, we present general results. Then, we survey complexity …

In decision analysis, difficulties of obtaining complete information about model parameters
make it advisable to seek robust solutions that perform reasonably well across the full range …

The knapsack problem (KP) and its multidimensional version (MKP) are basic problems in
combinatorial optimization. In this paper, we consider their multiobjective extension (MOKP …

Algorithms for approximating the nondominated set of multiobjective optimization problems
are reviewed. The approaches are categorized into general methods that are applicable …

In this paper we present a review of approximative solution methods, that is, heuristics and
metaheuristics designed for the solution of multiobjective combinatorial optimization …

In this paper, we present an approach, based on dynamic programming, for solving the 0–1
multi-objective knapsack problem. The main idea of the approach relies on the use of …

Many optimization problems encountered in the real-world have more than two objectives.
To address such optimization problems, a number of evolutionary many-objective …

The idea of k-adaptability in two-stage robust optimization is to calculate a fixed number k of
second-stage policies here-and-now. After the actual scenario is revealed, the best of these …

In this article, local optimality in multiobjective combinatorial optimization is used as a
baseline for the design and analysis of two iterative improvement algorithms. Both …