Simulation and multi-objective optimisation for sustainability of a production flow

Abstract

Manufacturing companies are increasingly facing growing customer demands to produce more sustainable products across all dimensions of the Triple Bottom Line (TBL), including environmental, economic, and social aspects. To address these challenges, simulation-based optimisation has become a widely used approach in modern manufacturing systems. The aim of this thesis is to investigate how simulation software can support sustainable improvements in an industrial production flow. The study is based on a systematic literature review and an industrial case study of a robot cell in a heavy equipment manufacturing plant. A Discrete Event Simulation model was developed and analysed using the NSGA-II algorithm to identify improvements in productivity and energy consumption. In addition, an ergonomic assessment was conducted to include the social dimension of sustainability. The systematic literature review together with the case study found the possibilities with using simulation as a tool to sustainably analyse a manufacturing system. The results from the case study demonstrate that a large share of the energy consumption in the system is non-value-adding and related to idle operation. The optimisation identified multiple Pareto-optimal solutions, showing that energy consumption can be reduced while maintaining or increasing production output. The findings highlight idle power reduction as the most influential improvement. Overall, the study shows that simulation combined with multi-objective optimisation is a valuable tool for supporting sustainable manufacturing by enabling data-driven evaluation of improvements within the TBL.