A Tactical Planning Approach using Genetic Algorithms and Process Chain Simulation for Closed-Loop Production Systems for high-value components

The implementation of closed-loop production systems in manufacturing is a promising approach to mitigate environmental impacts and supply chain disruptions, while increasing the value and lifespan of products and materials. These systems integrate forward and reverse material flow activities such as repair, remanufacturing, and recycling within a single facility. However, a major challenge in planning and operating closed-loop production systems is the high uncertainty surrounding material availability and condition. Different process chains may be required depending on the extent and type of wear present in the used products. During the tactical planning phase, factory planners play a crucial role in evaluating and economically assessing various system and process chain configurations. Multiple technology chain and manufacturing equipment options provide different solutions for managing forward and reverse material flows. To address this, this paper introduces a metaheuristic framework based on discrete event simulation and genetic algorithms to evaluate these system configurations. The framework serves as decision-support for factory planners, whether they are planning a greenfield production system or undertaking a brownfield project aimed at transforming linear into circular production systems with both forward and reverse flows. The presented framework is built on data models that incorporate different technology and process chain options for manufacturing and repair, considering the condition of the cores. A manufacturing equipment database contains predefined data models describing machine skills and load profiles, which are utilized to generate various simulation models using different production system configurations. These models are then used to execute predefined scenarios. The main objective is to identify configurations that achieve a beneficial fitness value balancing technical, economic, and environmental key performance indicators for closed-loop production systems.