Dr Jacomine Grobler, a Senior Lecturer of Supply Chain in the Department of Industrial and Systems Engineering at the University of Pretoria, has developed multi-objective algorithms for complex, real-world production scheduling problems, which has earned her the 2015 JD Roberts Award for Emerging Researchers.

The award was instituted by the construction company, Murray & Roberts in 1980, in remembrance of one of the group’s founding fathers, Dr JD Roberts. It is bestowed annually in recognition of scientific research into technology that promotes environmentally sustainable solutions to challenges faced by the human living environment. All the finalists’ research presentations are assessed according to the relevance and potential improvement to people’s quality of life; the innovation, uniqueness and distinction of the work; the potential uptake and practicality of the research in the relevant sector; and the potential impact of the research.

Dr Grobler’s research has been extremely well received by the engineering world and has been touted as the first of its kind. She says that she developed these optimisation algorithms specifically to address production scheduling challenges in the manufacturing sector, taking into consideration the related matters of how operators schedule jobs and which machines they will use. Order scheduling and resource allocation have a major impact on the competitiveness of companies because they are in a better position to meet their scheduled delivery dates if jobs are scheduled efficiently.

One of the algorithms Dr Grobler developed focusses on simultaneously minimising queue time and total production time, while maximising delivery performance. Dr Grobler’s research points out the financial implications of long waiting times on companies – they increase costs related to storage space and insurance, and affect the companies’ ability to meet due dates, which in turn has a significant impact on customer satisfaction. Her algorithms have been shown to outperform existing advanced algorithms by up to 76%, which translates to a substantial decrease in manufacturing costs.