Abstract

Purpose: This study examines how mathematical optimisation methods can rigorously support engineering solutions for industrial waste reduction within a circular economy framework. While numerous conceptual frameworks exist, there is a critical gap in the literature regarding generalised, quantitative models that balance environmental, economic, and operational criteria across diverse industrial contexts. The purpose of this research is to address that gap by developing a robust optimisation model that identifies optimal material circularity pathways and evaluates their potential to minimise waste while maintaining economic viability.

Methodology: This study present a multi-objective mathematical optimisation model built on advanced decision-making techniques  including genetic algorithms and non-linear programming  to maximise material recovery and economic returns from industrial waste streams. The model integrates key circular economy principles, applies simulation based on real industrial waste datasets, and conducts sensitivity analysis on objective trade-offs.

Findings: Quantitative results demonstrate that optimisation significantly enhances material recovery rates and improves waste reduction while balancing profit and sustainability objectives. The optimised pathways outperform conventional strategies by allocating resources efficiently across recycling, reuse, and energy recovery functions. Sensitivity results reveal critical thresholds where environmental gains come at cost trade-offs, emphasising the need for adaptive decision criteria.

Value: This paper contributes a novel, mathematically grounded decision-support framework applicable across industrial sectors for circular economy engineering. It advances empirical knowledge by quantifying optimisation benefits and revealing structural dynamics between economic and environmental objectives  insights that deepen theoretical understanding and provide practical guidance for policymakers and industry engineers.

Keywords: Circular economy; industrial waste reduction; mathematical optimisation; genetic algorithm; multi-objective modelling; resource recovery; sustainable engineering.