Incorporating car-sequencing rules in the planning of mixed-model assembly lines

Efficient configurations of assembly lines involve balancing the workload across stations and strongly depend on the sequence in which the products are processed. While these processes are interconnected, they are typically addressed separately due to the complexities and different time frames involved. Even if some literature methods combine balancing and sequencing problems, these are limited in scalability, often constrained to small-scale instances with a limited number of products. This restricts their applicability to real-world industrial scenarios, where the number of product configurations can be vast. Another branch of the literature assumes a random sequence, which is a pessimistic view of a real setting. To fill this gap, we propose a genetic algorithm for the integration of the balancing problem with a semi-random production sequence that respects selected car-sequencing rules. This paper addresses the combined balancing and car sequencing rules selection problem for large-scale assembly lines with uncertain production sequences. By incorporating rules related to car sequencing, we reduce the randomness of a simulated production sequence used to evaluate the efficiency of an assembly balancing solution without the burden of solving an optimisation problem. The impact of incorporating rules is evaluated through a discrete-event simulator, demonstrating significant improvements in line performance.