General advanced job shop scheduling approach

Abstract

The development of this thesis aims to design a new approach for solving production planning and scheduling in the process industries in such a way to be adaptable to any manufacturing plant, the description of which would have to be previously provided together with a series of ordered jobs. The planning and scheduling solving is concerned with the allocation over time of scarce resources between competing activities to meet a given set of requirements with an efficient organization. But, things get complicated as larger the scale of the problem is, i.e. as more resources, activities and requirements are involved. That is why the orientation of the work is focused on an innovative method in the style of Artificial Intelligence, by means of an automated process seeking to converge to a predefined objective. Although this research object has been studied since the middle of the last century, major breakthroughs were not achieved until the emergence of high-performance computing technologies; since by nature these are combinatorial problems which, the larger the scale, the more exploration they require to find some optimal. In addition, most of the last years related articles has been focused on solution approaches based on mathematical programming techniques, and it is important to note that there are other solution methods for dealing with this kind of problems. These methods can be used either as alternative methods, or as methods that can be combined with mathematical programming models, like the one proposed in this document