We review the most important mathematical models and algorithms developed for the exact
solution of the one-dimensional bin packing and cutting stock problems, and experimentally …

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the
goal of automating the design of heuristic methods to solve hard computational search …

In the past decade, considerable advances have been made in the field of computational
intelligence and operations research. However, the majority of these optimisation …

Hyper-heuristics comprise a set of approaches that aim to automate the development of
computational search methodologies. This chapter overviews previous categorisations of …

We present a general framework for solving a real-world multimodal home-healthcare
scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of …

In recent years, deep reinforcement learning has shown great potential in solving computer
games with sequential decision-making scenarios. Hyper-heuristic is a generic search …

Multi-objective optimization allows satisfying multiple decision criteria concurrently, and
generally yields multiple solutions. It has the potential to be applied to structural damage …

Educational timetabling problems, namely, university examination timetabling, university
course timetabling and school timetabling, are combinatorial optimization problems …

In this paper we address the multi-depot open vehicle routing problem (MDOVRP), a
complex and difficult problem arising in several real-life applications. In the MDOVRP …

Many practical productions are full of significant uncertainties. For example, the working
environment may change, machines may breakdown, workers may become unstable, etc. In …