A Predictive Mathematical Model for Manufacturing Sustainability Based on Life Cycle Assessment and Multi-Objective Decision Optimization

Manufacturing companies are increasingly under pressure to reduce the environmental effects of their operations, while at the same time striving to be economically viable and efficient in their operations, by embracing life cycle sustainability assessment [1] [2]. It is argued that an integrated approach that combines life cycle assessment (LCA) and multi-objective optimization algorithms is necessary for more predictive mathematical models to aid sustainable manufacturing decision-making. The method is based on non-dominated sorting genetic algorithm II (NSGA-II) and multi-criteria decision-making techniques to obtain the Pareto optimal solutions for several conflicting sustainability goals. Energy modeling and energy reduction of cutting parameters optimization of the cutting process has been known to be effective and powerful energy reduction approach [3]. Case studies are presented in additive manufacturing and conventional machining processes to show the significant performance improvement: If combined additive manufacturing is used instead of conventional CNC milling, the overall environmental impact is reduced by 53% [4] and optimized CNC plane milling parameters contribute to an improvement in efficiency by 21.0%, energy saving of 15.3% and reduction in surface roughness by 5.5% [3]. Optimal utilization of resources coupled with mathematical programming optimization models help to optimize the performance while minimizing environmental impacts with the incorporation of concepts of Industry 4.0 [5]. This research introduces a systematic approach to quantifying environmental impact in a manufacturing decision support system, a key step between LCA and the optimization of manufacturing decisions. The results show that the integrated LCA-MCDM approaches provide a comprehensive assessment of sustainability including multiple performance dimensions that can be used for informed decision making towards sustainable manufacturing systems.