Special issue on exact and approximation methods for mixed-integer multi-objective optimization

Multi-objective optimization has become a very active area of research.It is an interdisciplinary field bringing together researchers from mathematics, computer science, engineering and economics.Multi-objective optimization problems arise frequently in applications when conflicting interests have to be considered.Multi-objective optimization problems are often solved according to the principle of efficiency or Pareto optimality: a solution is efficient if no other feasible solution exists that is better or equal in all objectives and strictly better in at least one objective.Each efficient solution corresponds to a possible compromise among the several objectives and is potentially relevant to a decision maker.Depending on the context, the goal is to compute either the set of all efficient solutions, its image in the objective space, or a representation of that image according to some measure of interest.The multidimensional nature of these problems raises relevant mathematical and algorithmic challenges.The aim of this Special Issue is to collect the latest advances on exact and approximation methods with quality guarantees for multi-objective (mixed) integer optimization problems.The contributions cover both objective-space and decisionspace approaches based on principles of branch-and-bound and column generation.New approximation results are also presented, as well as instance generators for benchmarking purposes.Bazgan et al. ( 2023) investigate which types of partially exact approximation sets of polynomial cardinality are guaranteed to exist for general multi-objective problems.