Abstract. A multiobjective optimization problem (MOP) with inequality and equality constraints is considered where the objective and inequality constraint functions are locally Lipschitz and equality ...

Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations.

We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...

Distributed Constraint Optimisation and Search Algorithms form a vital framework for addressing complex decision‐making and scheduling problems in multi-agent systems. These algorithms ...

We focus on a particular generic framework for solving constraint optimization problems, the so-called implicit hitting set (IHS) approach. The approach is based on a theory of duality between ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial ...

Examples of such an optimization problem include finding capacity in information theory, finding least favorable distributions in statistics and finding best and worst case arrival times in queuing ...

Following this line of thought, the researchers formulated the "soft constraints" variant of the routing problem in bicycle rebalancing.