Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …

In a world of electronic calendars, the prospect of intelligent, personalized time management
assistance seems a plausible and desirable application of AI. PTIME (Personalized Time …

In the present paper, we propose a new approach for scheduling ground-handling vehicles,
tackling the problem with a global perspective. Preparing an aircraft for its next flight requires …

A key challenge to software product line engineering is to explore a huge space of various
products and to find optimal or near-optimal solutions that satisfy all predefined constraints …

Multi-Objective Combinatorial Optimization (MOCO) is fundamental to the development and
optimization of software systems. We propose five novel parallel algorithms for solving …

Abstract Large neighborhood search (LNS)[25] is a framework that combines the
expressiveness of constraint programming with the efficiency of local search to solve …

Generally, the composition is the process of combining services to fulfill complex tasks
based on their functional and non-functional values such as quality of services (QoS) and …

This paper presents a new general-purpose algorithm for exact solving of combinatorial
many-objective optimization problems. We call this new algorithm the guided improvement …

In many settings just finding a good clustering is insufficient and an explanation of the
clustering is required. If the features used to perform the clustering are interpretable then …

We describe a simple CSP formalism for handling multi-attribute preference problems with
hard constraints, one that combines hard constraints and preferences so the two are easily …