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 …

Project controls have traditionally been focused on after-the-fact detection of variances. This
thesis proposes a control system, the Last Planner system, that causes the realization of …

In this paper, we propose a particle swarm optimization (PSO) based hyper-heuristic
algorithm for solving the resource constrained project scheduling problem (RCPSP). To the …

Grid computing utilizes the distributed heterogeneous resources in order to support
complicated computing problems. Grid can be classified into two types: computing grid and …

Hyper-heuristics can be identified as methodologies that search the space generated by a
finite set of low level heuristics for solving search problems. An iterative hyper-heuristic …

The bin-packing problem is a well known NP-Hard optimisation problem, and, over the
years, many heuristics have been developed to generate good quality solutions. This paper …

The fields of machine meta-learning and hyper-heuristic optimisation have developed
mostly independently of each other, although evolutionary algorithms (particularly genetic …

Most of the current search techniques represent approaches that are largely adapted for
specific search problems. There are many real-world scenarios where the development of …

In this chapter, we review a few important concepts from Grid computing related to
scheduling problems and their resolution using heuristic and meta-heuristic approaches …

Grid computing is a form of distributed computing that co-ordinates and provides the facility
of resource sharing over various geographical locations. Resource scheduling in Grid …