- author = "Christos Dimopoulos",
- title = "Genetic programming for manufacturing optimisation",
- school = "University of Sheffield",
- year = "2000",
- address = "UK",
- month = aug,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://etheses.whiterose.ac.uk/24962/",
-
URL = "
http://etheses.whiterose.ac.uk/24962/1/327668.pdf",
-
URL = "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327668",
- timestamp = "Fri, 23 Sep 2016 17:28:49 +0200",
-
biburl = "https://dblp.org/rec/phd/ethos/Dimopoulos00.bib",
- size = "233 pages",
-
abstract = "A considerable number of optimisation techniques have
been proposed for the solution of problems associated
with the manufacturing process. Evolutionary
computation methods, a group of non-deterministic
search algorithms that employ the concept of Darwinian
strife for survival to guide the search for optimal
solutions, have been extensively used for this purpose.
Genetic programming is an evolutionary algorithm that
evolves variable-length solution representations in the
form of computer programs. While genetic programming
has produced successful applications in a variety of
optimisation fields, genetic programming methodologies
for the solution of manufacturing optimisation problems
have rarely been reported. The applicability of genetic
programming in the field of manufacturing optimisation
is investigated in this thesis. Three well-known
problems were used for this purpose: the one-machine
total tardiness problem, the cell-formation problem and
the multiobjective process planning selection problem.
The main contribution of this thesis is the
introduction of novel genetic programming frameworks
for the solution of these problems.
In the case of the one-machine total tardiness problem genetic programming employed combinations of dispatching rules for the indirect representation of job schedules. The hybridisation of genetic programming with alternative search algorithms was proposed for the solution of more difficult problem instances. In addition, genetic programming was used for the evolution of new dispatching rules that challenged the efficiency of man-made dispatching rules for the solution of the problem.
An integrated genetic programming - hierarchical clustering approach was proposed for the solution of simple and advanced formulations of the cell-formation problem. The proposed framework produced competitive results to alternative methodologies that have been proposed for the solution of the same problem. The evolution of similarity coefficients that can be used in combination with clustering techniques for the solution of cell-formation problems was also investigated.
Finally, genetic programming was combined with a number of evolutionary multiobjective techniques for the solution of the multiobjective process planning selection problem. Results on test problems illustrated the ability of the proposed methodology to provide a wealth of potential solutions to the decision-maker.",
- notes = "Supervisor: Neil Mort",
