Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Multi-objective optimisation using agent-based modelling
ENGLISH ABSTRACT: It is very seldom that a decision-making problem concerns only a single value or objective. The process of simultaneously optimising two or more con icting objectives is known as multi-objective optimisation (MOO). A number of metaheuristics have been successfully adapted for...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | en_ZA |
| Published: |
Stellenbosch : Stellenbosch University
2012
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | ENGLISH ABSTRACT: It is very seldom that a decision-making problem concerns only a single
value or objective. The process of simultaneously optimising two
or more con
icting objectives is known as multi-objective optimisation
(MOO). A number of metaheuristics have been successfully adapted
for MOO. The aim of this study was to investigate the feasibility of
applying an agent-based modelling approach to MOO.
The (s; S) inventory problem was chosen as the application eld for
this approach and Anylogic used as model platform. Agents in the
model were responsible for inventory and sales management, and had
to negotiate with each other in order to nd optimal reorder strategies.
The introduction of concepts such as agent satisfaction indexes,
aggression factors, and recollection ability guided the negotiation process
between the agents.
The results revealed that the agents had the ability to nd good
strategies. The Pareto front generated from their proposed strategies
was a good approximation to the known front. The approach was also
successfully applied to a recognised MOO test problem proving that
it has the potential to solve a variety of MOO problems.
Future research could focus on further developing this approach for
more practical applications such as complex supply chain systems,
nancial models, risk analysis and economics. |
|---|