Abstract:
Customers of a steel manufacturing company now order a large number of low volume
orders instead of a small number of high volume orders as they would have done just
a few decades ago. The change in customer expectations has complicated production
planning and scheduling within a steel manufacturing company.
The aim of this research is to improve production planning and scheduling capability
in steelmaking using one of the popular simulation techniques, called discrete event
simulation. In this research it is observed that there are three major areas that need
attention to improve production planning and scheduling capability. First, selection of
optimal schedules and plans based on throughput, production time, stock size, and other
production processing criteria. Next, incorporating cost into the criteria to select the
schedules and plans will make the planning more cost effective and realistic at the same
time. In addition, with the increased use of discrete event simulation modelling, there is
a need to improve the model development efficiency and make the process less reliant
on practitioners’ experience and capabilities, in order to improve the overall planning
and scheduling capability. This thesis presents frameworks to address the three major
areas for the capability improvement.
This research adapts a systematic approach to validation. Theoretical, realisation,
and empirical parts of the research were separately validated. Real life case studies
were used for validation of each proposed framework.
Discrete event simulation can improve the accuracy of production planning & scheduling
and cost estimation for complex production systems. GA-based multi-objective optimisation
can be successfully applied to optimisation of plans and schedules. Production
planning and scheduling optimisation for some production areas provides a challenging
problem to GAs. Cost estimation in the steel manufacturing company needs improvement
because of the current lack of accurate costs of product families that affects quality
of price management. The developed cost estimation technique is capable of providing
more realistic cost for product families. The cost estimation technique would be useful
for companies operating on volume-driven manufacturing processes rather than on
unit-driven. Conceptual modelling needs to be improved in order to achievein model
development efficiency and to make the process less reliant on practitioners’ experience
and capabilities. A formal information collection process can aid conceptual modelling
of production systems by further development of DES models for cost estimation.
