Development of frameworks for steel manufacturing planning capability improvement using discrete event simulation

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.