Multi-Objective Optimization for Integrated Production Planning and Control

Abstract

Integrated production planning and control is an essential aspect in ensuring operational efficiency in the manufacturing sector. However, companies often face challenges in optimizing various goals that frequently conflict, such as cost reduction, quality improvement, and production capacity. Therefore, multi-objective optimization methods are particularly relevant for resolving conflicts between objectives in production planning and control. The research aims to develop a multi-objective optimization method that integrates production planning and control, enabling the company to achieve an optimal balance between various conflicting objectives and improve overall operational efficiency. The approach employed in this study is a multi-objective optimization method that combines genetic algorithms and mathematical programming. This model integrates production, quality management, and cost factors in a single framework to achieve optimal results. Simulations were conducted on various production scenarios to assess the performance of these models. The simulation results indicate that the application of multi-objective optimization methods can lead to more efficient production planning, resulting in a 20% reduction in costs and a 15% improvement in product quality. The model also shows increased flexibility in responding to changes in market demand.