Multi-Objective Optimization Approach for Enhancing Production System Efficiency in a Food Processing Industry

Effective Industrial practices in the food processing Industry aim to lower production costs, maximize machine efficiency, reduced quality problems, and promote process sustainability. These goals present complex challenges that requires advanced strategies for enhancing process performance. This study explores a multi-objective optimization approach focused on achieving high overall equipment effectiveness (OEE), which translates to increased machine operating time and reduced downtime, while simultaneously decreasing energy use and overall waste. The methodology is formulated as a binary optimization problem, and solved using Sequential Least Square programming method (SLSQP) model, while a polynomial regression model is used to test findings. Analysis reveals significant variability in maximum OEE, energy use, and waste levels across different machines and products, with OEE values ranging from 0.194444 to 0.3066, energy from 1.144648 kWh to 0.025532 kWh, and waste levels from 0.91771 kg to 0.019682 kg. This inconsistency suggests inefficiencies stemming from suboptimal machine-to-product arrangements. The validation results indicate a perfect alignment with the optimal values for OEE, energy, and waste levels derived from the optimization outcome. The research emphasizes the importance of aligning machines with products and scheduling effectively to enhance production efficiency. Implementing structured multi-objective optimization solution in the food Industry presents significant opportunities for enhancing production system efficiency. By addressing multiple objectives simultaneously, food producers can achieve a balance between energy use, waste levels, sustainability, quality, and responsiveness to market demands. Continued research and development in this area will be crucial for driving innovation and improving the competitiveness of the food corporation.