Optimizing Car Resequencing on Mixed-Model Assembly Lines: Algorithm Development and Deployment

TL;DR

This paper presents a multi-objective algorithm for optimizing car model sequencing on mixed-model assembly lines, improving production efficiency, reducing changeovers, and enhancing delivery reliability based on real-world deployment data.

Contribution

The paper introduces a novel multi-objective resequencing algorithm specifically designed for MMAL, validated through real-world deployment and demonstrating significant operational improvements.

Findings

30% increase in average batch size

23% reduction in color changeovers

10% decrease in delivery delay risk

Abstract

The mixed-model assembly line (MMAL) is a production system used in the automobile industry to manufacture different car models on the same conveyor, offering a high degree of product customization and flexibility. However, the MMAL also poses challenges, such as finding optimal sequences of models satisfying multiple constraints and objectives related to production performance, quality, and delivery -- including minimizing the number of color changeovers in the Paint Shop, balancing the workload and setup times on the assembly line, and meeting customer demand and delivery deadlines. We propose a multi-objective algorithm to solve the MMAL resequencing problem under consideration of all these aspects simultaneously. We also present empirical results obtained from recorded event data of the production process over $4$ weeks following the deployment of our algorithm in the Saarlouis plant of Ford-Werke GmbH. We achieved an improvement of the average batch size of about $30\%$ over the old control software translating to a $23\%$ reduction of color changeovers. Moreover, we reduced the spread of cars planned for a specific date by $10\%$, reducing the risk of delays in delivery. We discuss effectiveness and robustness of our algorithm in improving production performance and quality as well as trade-offs and limitations.